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When data is submitted to TCIA it undergoes an extensive curation process to assure completeness, proper formatting to facilitate discovery and data reuse and removal of all protected health information.  Once data is released on the public TCIA repository it is Published to the world.  This publication is associated with the creation of a Digital Object Identifier that allows direct access to the data. 

In addition to data publication via TCIA we strongly urge researchers who submit data to TCIA to also submit a Data Descriptor publication to a journal such as Nature Scientific Data.  In this type of publication the authors will describe the data acquisition process, the experiment that drove this data collection and value of the data for future research (see each journal for specific content requirements).  A Data Descriptor is a scientific paper that includes the DOI to the data previously published on TCIA and helps to call the attention of the scientific community to the data you have submitted.  The details provided in a Data Descriptor publication greatly enhance the value of your contribution. 

A Data Descriptor is different from a scholarly paper in which you describe your experiment and present the results of your analysis.  Many journals do not provide sufficient space for details of data acquisition.  So today you can provide those details and the data you collected by making full use of TCIA and journals that support data publication.  In summary we urge you to:

  1. Submit your data to TCIA for publication.
  2. Submit a Data Descriptor article including the TCIA provided DOI to describe your data and how it was acquired.
  3. Submit a paper describing your experiment and results.

Please remember in all of your publications based on TCIA data to include appropriate references to TCIA so we can identify your publications, reference them, and make them easily available to other researchers from the TCIA web site. These citations are critical for providing continued justification of funding from the agencies that support TCIA, and are what allow us to provide this data to you free of charge. Guidelines for how to cite TCIA can be found on our Citation Guidelines wiki page.  In addition we would like to list these publications here on our web site. If you have utilized TCIA in your research please contact us at so that we can include your publications in the list below. The publication list below includes references to the original data collection as well as publications that specifically used data from TCIA.

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TCIA-Related Publication History

Table of Contents

TCIA General

  1. Williamson JF, Das SK, Goodsitt MS, Deasy JO. Introducing the Medical Physics Dataset Article. Med. Phys. (2017) 44(2)349-350. doi: 10.1002/mp.12003
  2. Nida, N; Khan, M. Efficient Colorization of Medical Imaging based on Colour Transfer Method. U.G. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 53(4); 253-261. (2016).
  3. Kalpathy-Cramer J, Zhao B, et al. A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging (2016). 29(4):476-487. DOI: 10.1007/s10278-016-9859-z
  4. Parks CL, Monson KL. Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications. Journal of Digital Imaging. 2016:1-11. DOI: 10.1007/s10278-016-9932-7

  5. Huang BE, Mulyasasmita W, Rajagopal G. The Path from Big Data to Precision Medicine. Expert Review of Precision Medicine and Drug Development (2016). 1(2):129-143. (link)

  6. Chatellier G, Varlet V, Blachier-Poisson C. "Big data" and "open data": What kind of access should researchers enjoy? Therapie. 2016 Feb;71(1):97-105, 107-14.(link)
  7. Benedict SH, Hoffman K, et al. Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data. Int J Radiat Oncol Biol Phys. 2016. 95(3):873-879 (link)
  8. Toga AW, Dinov ID. Sharing big biomedical data. Journal of Big Data. 2015;2(1):1-12.
  9. Moore SM, Maffitt DR, Smith KE, Kirby JS, Clark KW, Freymann JB, Vendt BA, Tarbox LR, Prior FW. De-identification of Medical Images with Retention of Scientific Research Value. RadioGraphics. 2015;35(3):727-35. doi: doi:10.1148/rg.2015140244.
  10. Mayo CS, Deasy JO, et al. How Can We Effect Culture Change Toward Data-Driven Medicine? Int J Radiat Oncol Biol Phys. 2016. 95(3):916-21. (link)
  11. Kirby, J., L. Tarbox, et al. (2015). "TU-AB-BRA-03: The Cancer Imaging Archive: Supporting Radiomic and Imaging Genomic Research with Open-Access Data Sets." Medical physics 42(6): 3587-3587.  DOI: 10.1118/1.4925508
  12. GIllies RJ, Kinahan PE, et al. RadiomicsImages Are More than Pictures, They Are Data. Radiology, 2016. 278(2):563-77. (link)
  13. Fedorov A, Clunie D, et al. DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research PeerJ, 2016. (link)
  14. Commean PK, Rathmell JM, Clark KW, Maffitt DR, Prior FW. A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial. Journal of Digital Imaging. 2015:1-9. (paper)
  15. Bourne PE. DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 2015;275(1):3-4. link. PubMed PMID: 25799330.
  16. Armato SG, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP. Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Journal of Medical Imaging. 2015;2(2):020103-.
  17. Herskovits EH. Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 2014;124:1-30.
  18. Gutman DA, Dunn Jr WD, Cobb J, Stoner RM, Kalpathy-Cramer J, Erickson B. Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer. Frontiers in Neuroinformatics. 2014;8.(paper)
  19. Erickson BJ, Fajnwaks P, Langer SG, and Perry J. Multisite Image Data Collection and Management Using the RSNA Image Sharing Network., Translational oncology, 2014. 7(1):36-39. (paper)
  20. Prior FW, Clark K, Commean P, Freymann J, Jaffe C, Kirby J, Moore S, Smith K, Tarbox L, Vendt B. TCIA: an information resource to enable open science. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE; 2013. (paper)
  21. Gutman DA, Cobb J, Somanna D, et al. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data., Journal of the American Medical Informatics Association, 2013. 20(6): p. 1091-1098. doi: 10.1136/amiajnl-2012-001469 (paper)
  22. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
  23. Villani L and Prati RC. Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário. Congresso Brasileiro de Informática em Saúde (CBIS’2012). Citado na. 2012.(paper)
  24. Mongkolwat P, Channin DS, Kleper V, Rubin DL. Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder.Radiographics .2012. 32(4):1223-32. (paper).
  25. Jaffe, C Carl. Imaging and Genomics: Is There a Synergy?Radiology. 2012. 264(2):329-31.(paper).
  26. Freymann JB, Kirby JS, Perry JH, Clunie DA, and Jaffe CC. Image data sharing for biomedical research—meeting HIPAA requirements for de-identification.Journal of Digital Imaging 25, no. 1 (2012): 14-24. (paper)


  1. Demerath T, Simon-Gabriel CP, Kellner E, et al. Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J. 2017;30(1):36-47. doi: 10.1177/1971400916678225
  2. Liu TT, Achrol AS, Mitchell LA, Rodriguez SA, Feroze A, Iv M, Kim C, Chaudhary N, Gevaert O, Stuart JM, Harsh GR, Chang SD, Rubin DL. Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro-Oncology. 2016:1-11. doi: 10.1093/neuonc/now270

  3. Schrock M, Batar B, Lee J, Druck T, Ferguson B, Cho J, Akakpo K, Hagrass H, Heerema N, Xia F. Wwox–Brca1 interaction: role in DNA repair pathway choice. Oncogene. 2016:1-13. doi: 10.1038/onc.2016.389.

  4. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiologica. 2016:0284185116673119.

  5. McCann SM, Jiang Y, Fan X, Wang J, et al. Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. AJR Am J Roentgenol (2016). 206(3):559-565 (link)

  6. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. (link)  

  7. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. Imaging genomics in cancer research: limitations and promises. The British Journal of Radiology. 2016:20151030. doi:10.1259/bjr.20151030
  8. Zhu, Y., H. Li, et al. (2015). TU-CD-BRB-06: Deciphering Genomic Underpinnings of Quantitative MRI-Based Radiomic Phenotypes of Invasive Breast Carcinoma. Medical physics 42(6): 3603-3603.

  9. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-A77.

  10. Shinegare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear renal cell carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdominal Imaging (2015). 40(6)1684-1692. (link)
  11. Pope WB. Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 2015;25(1):105-19.

  12. Gutman, D. A., W. D. Dunn Jr, et al. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology: 1-11.
  13. Feldman, M., M. G. Piazza, et al. (2015). 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target. Neurosurgery 62: 209-210.

  14. Colen R, Foster I, Gatenby R, Giger ME, Gillies R, Gutman D, Heller M, Jain R, Madabhushi A, Madhavan S, Napel S, Rao A, Saltz J, Tatum J, Verhaak R, Whitman G. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology. 2014;7(5):556-69. doi: 10.1016/j.tranon.2014.07.007.
  15. Rao A. Exploring relationships between multivariate radiological phenotypes and genetic features: A case-study in Glioblastoma using the Cancer Genome Atlas, Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE.
  16. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 2012;264(2):387-96. Epub 2012/06/23.  (link)


  1. Beichel RR, Smith BJ, Bauer C, et al. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med. Phys.(2017). 44(2)479-496. doi: 10.1002/mp.12041
  2. Vallières M, Kay-Rivest E, Perrin LJ, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports (2017) (arXiv 1703.08516)
  3. D Paredes, A Saha, MA Mazurowski. Deep learning for segmentation of brain tumors: can we train with images from different institutions? Proc. SPIE Medical Imaging: Computer-Aided Diagnosis (2017).International Society for Optics and Photonics. doi: 10.1117/12.2255696
  4. Shijin Kumar PS, Dharun VS. Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research 2017;28(5) (link)
  5. Nabizadeh N, Kubat M. Automatic Tumor Segmentation in Single-Spectral MRI Using A Texture-Based and Contour-Based Algorithm.doi: 10.1016/j.eswa.2017.01.036
  6. Kaur T, Saini BS, Gupta S. A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Computing and Applications. 2016:1-24. doi: 10.1007/s00521-016-2751-4

  7. Song J, Liu Z, Zhong W, Huang Y, Ma Z, Dong D, Liang C, Tian J. Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis. Scientific reports. 2016;6:38282:1-9. doi: 10.1038/srep38282

  8. Crawford L, Monod A, Chen AX, Mukherjee S, Rabadán R. Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme. arXiv preprint arXiv:161106818. 2016:1-29.

  9. Korfiatis P, Kline TL, Erickson BJ. Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. J Tomography, 2016, 2:4(334-340) DOI: 10.18383/j.tom.2016.00166
  10. Zheng C, Wang X, Feng D, editors. Topology guided demons registration with local rigidity preservation. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016: IEEE.

  11. Kotrotsou A, Zinn PO, Colen RR. Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America. 2016;24(4):719-29.

  12. Zhao B, Tan Y, Tsai WY, Qi J et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016 Mar 24;6:23428. (link)
  13. Li H, Zhu Y, Burnside ES, Huang E, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. npj Breast Cancer (2016). (link)
  14. Grossmann P, Gutman DA, et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma. BMC Cancer (2016). (link)
  15. Zhu Y, Li H, Guo W, Drukker K, et al. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep (2015). (link) 
  16. Rajakumar K, Muttan S, Deepa G, Revathy S, Priya BS. Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF. Advances in Natural and Applied Sciences. 2015 Jun 1;9(6 SE):506-13. (link)
  17. Parmar, C., R. T. Leijenaar, et al. (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer." Sci Rep 5: 11044.

  18.  Parmar, C., P. Grossmann, et al. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.
  19. Tanougast C, Chaddad A. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Adv Bioinformatics (2015). (link)
  20. Chaddad A. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.  International Journal of Biomedical Imaging, 2015. (link)
  21. Dhara AK, Mukhopadhyay S, Khandelwal N. 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. SPIE 2013. (link)
  22. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N. Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700K. (link)

Algorithm Development

  1. Mason J, Perelli A, Nailon W, Davies M. Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT? arXiv 1703.07179 2017.
  2. Hsieh KL-C, Tsai R-J, Teng Y-C, Lo C-M. Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI. PloS one. 2017;12(2):e0171342 (link)

  3. Hsieh KL-C, Lo C-M, Hsiao C-J. Computer-aided grading of gliomas based on local and global MRI features. Computer Methods and Programs in Biomedicine. 2017;139:31-8. (link)

  4. Yang H, Liu F, Wang Z, Tang H, Sun S, Sun S. Research on the Content-Based Classification of Medical Image. Journal of Medical Imaging and Health Informatics. 2017;7(1):129-36. (link)

  5. Rezaie AA, Habiboghli A. Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation. International Journal of Interactive Multimedia and Artificial Inteligence. 2017;4(Special Issue on 3D Medicine and Artificial Intelligence):15-9. (link)

  6. Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G. Low-dose CT via convolutional neural network. Biomedical Optics Express. 2017;8(2):679-94.(link)

  7. Vallières M, Freeman C, Skamene S, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in medicine and biology. 2015;60(14):5471.
  8. Kazdal S, Dogan B, Camurcu AY, editors. Computer-aided detection of brain tumors using image processing techniques. Signal Processing and Communications Applications Conference (SIU), 2015 23th; 2015: IEEE.
  9. Gupta A, Martens O, Le Moullec Y, Saar T, editors. A tool for lung nodules analysis based on segmentation and morphological operation. Intelligent Signal Processing (WISP), 2015 IEEE 9th International Symposium on; 2015: IEEE.
  10. Benninghoff H, Garcke H. Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes. arXiv:1506.07136. 2015.
  11. Zabala-Travers S, Choi M, Cheng W-C, Badano A. Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images. Medical Physics. 2015;42(6):2942-54.
  13. Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, Velazquez ER, Lambin P, Gu Y, Balagurunathan Y. Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. PloS one. 2015;10(3).
  14. Buerger C, Sénégas J, Kabus S, Carolus H, Schulz H, Agarwal H, Turkbey B, Choyke P, Renisch S. Comparing nonrigid registration techniques for motion corrected MR prostate diffusion imaging. Medical physics. 2015;42(1):69-80.
  15. Abedini M, Codella N, Connell J, Garnavi R, Merler M, Pankanti S, Smith J, Syeda-Mahmood T. A generalized framework for medical image classification and recognition. IBM Journal of Research and Development. 2015;59(2/3):1: -: 18.
  16. Blessy SPS, Sulochana CH. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation. Technology and Health Care. 2014.
  17. ElNawasany AM, Ali AF, Waheed ME. A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors.  Advanced Machine Learning Technologies and Applications: Springer; 2014. p. 357-66.
  18. Hong S, Huang Y, Cao Y, Chen X, Han J-DJ. Approaches to uncovering cancer diagnostic and prognostic molecular signatures. Molecular & Cellular Oncology. 2014.
  19. Codella N, Connell J, Pankanti S, Merler M, and Smith JR. Automated Medical Image Modality Recognition by Fusion of Visual and Text Information. Medical Image Computing and Computer-Assisted Intervention. 2014, Springer. 487-495. (link)
  20. Ertugrul OF. Adaptive Texture Energy Measure Method. International Journal of Intelligent Information Systems. 2014. 3(2):13-18. doi:10.11648/j.ijiis.20140302.11 (link)
  21. Kawa J, Juszczyk J, Pyciński B, Badura P, Pietka E. Radiological Atlas for Patient Specific Model Generation. Information Technologies in Biomedicine, 2014 4:69-82. 10.1007/978-3-319-06596-0_7. (link)
  22. Kowalik-Urbaniak I, Brunet D, Wang J, Koff D, Smolarski-Koff N, Vrscay ER, Wallace B, Wang Z.The quest for ‘diagnostically lossless’ medical image compression: a comparative study of objective quality metrics for compressed medical images. SPIE Medical Imaging. 2014. Vol. 9073. International Society for Optics and Photonics. doi:10.1117/12.2043196 (link)
  23. Naresh P and Shettar R. Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey. International Journal of Recent Trends in Engineering & Technology, 2014. 11:595-601 (link)
  24. Patel NP, Parmar SK, and Jain KR. Swift Pre Rendering Volumetric Visualization of Magnetic Resonance Cardiac Images based on Isosurface Technique. Procedia Technology, 2014. 14:422-429. doi:10.1016/j.protcy.2014.08.054 (link)
  25. Roy S, Brown MS, and Shih GL. Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting. Journal of Digital Imaging, 2014. 27(1):49-57. doi: 10.1007/s10278-013-9624-5 (link)
  26. Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations.  Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 520-7.

  27. Sivakumar S, and Chandrasekar C. A Study on Image Denoising for Lung CT Scan Images.International Journal of Emerging Technologies in Computational and Applied Sciences, 2014. 7(1):86-91 (link)
  28. Seff A, Lu L, Cherry KM, Roth HR, Liu J, Wang S, Hoffman J, Turkbey EB, Summers RM. 2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers.  Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 544-52.

  29. Harmon S, Wendelberger B, and Jeraj R. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis. Medical Physics, 2014. 41(6): p. 178-178. doi:10.1118/1.4888150 (link)
  30. Krishnakumar V. and Parthiban L. Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and NonSubsampled Contourlet Transforms. Annual Review & Research in Biology, 2014. 4(19):2938-2956. doi:10.9734/ARRB/2014/9131#sthash.qFePVdL1.dpuf (link)
  31. Codella N, Merler M. IBM TJ Watson Research Center. Semantic Model Vector for ImageCLEF2013. June 18, 2014. (link)
  32. Agostinelli F, Anderson MR, and Lee H. Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising. Advances in Neural Information Processing Systems. 2013. (link)
  33. Agostinelli F, Anderson MR, Lee H, editors. Robust Image Denoising with Multi-Column Deep Neural Networks. Advances in Neural Information Processing Systems; 2013.

  34. Breseman K, Lee C, Bloch BN, and Jaffe C. Constructing 3D-Printable CAD Models of Prostates from MR Images. Bioengineering Conference (NEBEC),
    39th Annual Northeast , IEEE, 27-28. 5-7 April 2013. doi:10.1109/NEBEC.2013.8
  35. Buckler A, Liu TT, Savig E, Suzek BE, Rubin DL, and Paik D. Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. Journal of Digital Imaging, 2013. 26(4):630-641. doi:10.1007/s10278-013-9599-2 (link)
  36. Heyns M, Breseman K, Lee C, Bloch BN, Jaffe C, and Xiang H. Design of a Patient-Specific Radiotherapy Treatment Target. Bioengineering Conference (NEBEC), 2013 39th Annual Northeast. 2013.171-172. IEEE.doi:10.1109/NEBEC.2013.75
  37. Kumar A, Kim J, Cai W, Fulham M, and Feng D. Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data. Journal of Digital Imaging, 2013. 26(6):1025-1039. doi: 10.1007/s10278-013-9619-2.(link)
  38. Lundström C. vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging. International Journal of Computer Assisted Radiology and Surgery, 2013. 8(3):437-450. doi: 10.1007/s11548-012-0792-4 (link)
  39. Olmedo I, Guerra Perez Y, Johnson JF, Raut L, Hoe DHK. Image segmentation on GPGPUs: a cellular automata-based approach. Proceedings of the 2013 Summer Computer Simulation Conference. Society for Modeling & Simulation International. 2013. 51. (link)
  40. Pambrun JF, Noumeir R. Compressibility variations of JPEG2000 compressed computed tomography. Conference Proceedings, 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013:3375-3378. doi: 10.1109/EMBC.2013.6610265 (link)
  41. Roozgard A, Barzigar N, Verma P, and Cheng S. 3D medical image denoising using 3D block matching and low-rank matrix completion. Signals, Systems and Computers, Asilomar Conference, 3-6 Nov. 2013, 253 – 257 IEEE. doi:10.1109/ACSSC.2013.6810271
  42. Yankeelov TE, Atuegwu N, Hormuth D, et al. Clinically Relevant Modeling of Tumor Growth and Treatment Response. Sci Transl Med. 2013 May 29;5(187):187ps9 doi: 10.1126/scitranslmed.3005686 (link) .
  43. Huang LC, Yseng LY, Hwang MS. A reversible data hiding method by histogram shifting in high quality medical images. Journal of Systems and Software 2013 March;86(3):716-27 doi: 10.1016/j.jss.2012.11.024 (link)
  44. Pheng HS and Shamsuddin SM. Texture classification of lung computed tomography images. 2012 International Conference on Graphic and Image Processing. 2013. Vol. 8768. International Society for Optics and Photonics. doi:10.1117/12.2011108 (link)
  45. Barzigar N, Roozgard A, Verma P, Cheng S. Removing Mixture Noise from Medical Images Using Block Matching Filtering and Low-Rank Matrix Completion. Healthcare Informatics, Imaging and Systems Biology, IEEE International Conference. 2012.134. doi:10.1109/HISB.2012.59 (link)
  46. Otake Y, Schafer S, Stayman JW, Zbijewski W, Kleinszig G, Graumann R, Khanna AJ, Siewerdsen JH. Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration. SPIE Medical Imaging, 2012. Volume: 8316. International Society for Optics and Photonics. doi:10.1117/12.911308 (link)
  47. Roozgard A, Cheng AS, Liu H. Malignant nodule detection on lung ct scan images with kernel rx-algorithm. Biomedical and Health Informatics (BHI), 2012 IEEE-EMBS International Conference on 5-7 Jan. 2012 499 – 502. IEEE. doi: 10.1109/BHI.2012.6211627.
  48. Biancardi AM, Jirapatnakul AC, Reeves AP. A comparison of ground truth estimation methods. International Journal of Computer Assisted Radiology and Surgery, 2010. 5(3):295-305. doi: 10.1007/s11548-009-0401-3 (link)
  49. Soysal OM, Chen P, Schneider H. An Image Processing Tool for Efficient Feature Extraction in Computer-Aided Detection Systems. Granular Computing (GrC) IEEE International Conference 2010. 14-16 Aug. 438-442. doi:10.1109/GrC.2010.128
  50. Tseng LY and Huang LC. Automatic fissure detection in CT images based on the genetic algorithm. Machine Learning and Cybernetics (ICMLC), International Conference. IEEE. 2010. 5: 2583 – 2588. doi: 10.1109/ICMLC.2010.5580871
  51. Kumar, D., A. Wong, et al. (2015). Lung Nodule Classification Using Deep Features in CT Images. Computer and Robot Vision (CRV), 2015 12th Conference on, IEEE.

  52. Kanas, V. G., E. I. Zacharaki, et al. (2015). "A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker." Biomedical Signal Processing and Control 22: 19-30.

  53. Magdy, E., N. Zayed, et al. (2015). "Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features." International Journal of Biomedical Imaging 2015.

  54. Zayed, N. and H. A. Elnemr (2015). "Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities." International Journal of Biomedical Imaging 2015.

  55. Chaddad, A. and C. Tanougast "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015.  doi: 10.1155/2015/728164 
  56. Li M, Miller K, Joldes GR, Kikinis R, Wittek A. Biomechanical model for computing deformations for whole-body image registration: A meshless approach. International Journal for Numerical Methods in Biomedical Engineering. 2016. doi: 10.1002/cnm.2771

Radiation Oncology

  1. Jaffray D, Chung C, Coolens C, Foltz W, Keller H, Menard C, Milosevic M, Publicover J, Yeung I, editors. Quantitative imaging in radiation oncology: An emerging science and clinical service. Seminars in Radiation Oncology; 2015: Elsevier.


  1. Emirzade, Erkan.  A COMPUTER AIDED DIAGNOSIS SYSTEM FOR  LUNG CANCER DETECTION USING SVM. The Graduate School Of Applied Sciences Of Near East University, 2016. (link to thesis)
  2. Yu, Zexi. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. University of Saskatchewan 2016. (link to thesis)
  3. Albalooshi FA. Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments. University of Dayton; 2015. (link to thesis)

  4. Camlica Z. Image Area Reduction for Efficient Medical Image Retrieval. Waterloo, Ontario, Canada,: University of Waterloo; 2015. (link to thesis)

  5. Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. Thesis, University of Texas; 2013.  (link to thesis)
  6. Karnayana PM. Radiogenomic correlation for prognosis in patients with glioblastoma multiformae. San Diego State University; 2013. (link to thesis)

  7. Nabizadeh, N. Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images. Electrical and Computer Engineering. Miami, FL, University of Miami. PhD., 2015. (link to thesis)

  8. Wieser, H.-P.  Supervised Machine Learning Approach Utilizing Artificial Neural Networks for Automated Prostate Zone Segmentation in Abdominal MR images. Klagenfurt, Austria, Fachhochschule Kärnten/Carinthia University of Applied Sciences; 2013.(link to thesis)


  1. Aerts HJ, Velazquez ER, et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. TCIA. Saint Louis, MO. (link)
  2. Armato SG and Drukker K, et al. (2015). SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset. TCIA. Saint Louis, MO. (link)
  3. Bloch N, Rusu M, et al. (2015) NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. TCIA. St. Louis, MO. (link)
  4. Colen RR, Wang J, et al. (2014). Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. TCIA. Saint Louis, MO. (link)
  5. Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. TCIA. Saint Louis, MO. (link)

  6. Gevaert O, Xu J, et al. (2014). Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. TCIA. Saint Louis, MO. (link)
  7. Grove O, Berglund AE, et al. (2015). Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. TCIA. Saint Louis. MO. (link)
  8. Gutman DA, Cooper LA, et al. (2014). MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. TCIA. Saint Louis, MO. (link)

  9. Huang W, Li X, et al. (2014). Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. TCIA. Saint Louis, MO. (link)

  10. Jain R, Poisson LM, et al. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. TCIA. Saint Louis, MO. (link)

  11. Kalpathy-Cramer J, Napel S, et al. (2015). QIN multi-site collection of Lung CT data with Nodule Segmentations. TCIA. Saint Louis, MO. (link)

  12. Lee J, Narang S, et al. (2015). Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases. TCIA. Saint Louis, MO. (link)
  13. Liu F,  Hernandez-Cabronero M, et al. (2016). Image Data Used in the Simulations of "The Role of Image Compression Standards in Medical Imaging: Current Status and Future Trends". TCIA. Saint Louis, MO. (link 
  14. Mazurowski MA, Zhang J, et al. (2014). Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. TCIA. Saint Louis, MO. (link)
  15. Messay T, Hardie RC, et al. (2014). Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset. TCIA. Saint Louis, MO. (link)

  16. Morris E, Burnside M, et al. (2014). TCGA Breast Phenotype Research Group Data sets. TCIA. Saint Louis, MO (link)
  17. Roth H, Lu L, et al. (2015). A new 2.5D representation for lymph node detection in CT. TCIA. Saint Louis, MO. (link)

  18. Shinagare AB, Vikram R, et al. (2015). Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group. TCIA. Saint Louis, MO. (link)

  19. Vallières M, Freeman CR, et al. (2015). Data from: A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. TCIA. Saint Louis, MO. (link)


  1. Semmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. TOMOGRAPHY, 2017; 3(1)41-9. doi: 10.18383/j.tom.2016.00286
  2. Farahani K, Kalpathy-Cramer J, Chenevert TL, et al. Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. Tomography, 2016;2(4):242-9. (link)

  3. Kalpathy-Cramer J, Mamomov A, Zhao B,et al.. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography,2016;2(4):430-7. doi: 10.18383/j.tom.2016.00235.

  4. Huang, W., X. Li, et al. (2014). "Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge." Transl Oncol 7(1): 153-166. (link)

  5. Kalpathy-Cramer J, Freymann JB, Kirby JS, et al. Quantitative Imaging Network: Data Sharing and Competitive Algorithm Validation Leveraging The Cancer Imaging Archive Translational Oncology. 2014 Feb;7(1):147-52. doi: 10.1593/tlo.13862. (link)

  6. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals Translational Oncology. 2014 Feb;7(1):1-4. doi: 10.1593/tlo.13832. (link)
  7. Levy MA, Freymann JB, Kirby JS, et al. Informatics methods to enable sharing of quantitative imaging research data. Magnetic Resonance Imaging. 2012 Nov;30(9):1249-56. doi: 10.1016/j.mri.2012.04.007. Epub 2012 Jul 6. (link)

Publications relating to specific data collections:

Collection: CT Colonography

  1. Pang S, Yu Z, Orgun MA. A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images. Computer Methods and Programs in Biomedicine. 2017. (link)
  2. Yahya-Zoubir B, Hamami L. et al. Automatic 3D Mesh-Based Centerline Extraction from a Tubular Geometry Form. Information Technology and Control, 2016. 45(2):156-163. (link)
  3. Alazmani A, Hood A, et al. Quantitative Assessment of Colorectal Morphology: Implications for Robotic Colonoscopy. Medical Engineering and Physics, 2016. 38(2):148-154. (link)
  4. Gayathri Devi K, Radhakrishnan R. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network. Computational and Mathematical Methods in Medicine. 2015;2015.
  5. Namías R, et al., Automatic rectum limit detection by anatomical markers correlation. Computerized Medical Imaging and Graphics, 2014. 38(4):245-250.(link)
  6. Boone DJ, Halligan S, Roth HR, et al., CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration. Radiology, 2013. 268(3):752-760.(link)
  7. Roth HR, et al., External clinical validation of prone and supine CT colonography registration in Abdominal Imaging. Computational and Clinical Applications 2012, Springer. 7601:10-19.(link)

Collection: LIDC-IDRI

  1. MC Hancock, JF Magnan. Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset. Proc. SPIE Medical Imaging: Computer-Aided Diagnosis (2017). International Society for Optics and Photonics. doi: 10.1117/12.2254446
  2. Wang, D; Fong, S; Wong, RK.; Mohammed, S; Fiaidhi, J; Wong, KKL. Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT. Scientific Reports 7, article number 43167 doi: 10.1038/srep43167
  3. Mhetre RR, Sache RG. Detection of Lung Cancer Nodule on CT scan Images by using Region Growing Method. International Journal of Current Trends in Engineering & Research. 2016;2(7):215-9. (link)

  4. Setio AAA, Traverso A, de Bel T, Berens MS, Bogaard Cvd, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B. Validation, comparison, and combination of algorithms for automaticdetection of pulmonary nodules in computed tomography images: the LUNA16 challenge. arXiv preprint arXiv:161208012. 2016:1-16.

  5. Firmino M, Angelo G, et al. Computer-aided Detection (CADe) and Diagnosis (CADx) System for Lung Cancer with Likelihood of Malignancy Biomed Eng Online (2016) 15(1):2 (link)
  6. Deep G, Kaur L, et al. Directional Local Ternary Quantized Extrema Pattern: A new descriptor for Biomedical Image Indexing and Retrieval Eng Sci and Tech, an International Journal (2016) (link)
  7. Wang W, Luo J, Yang X, Lin H. Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Academic Radiology. 2015.
  8. Sivakumar, S. and C. Chandrasekar (2015). "A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques." International Journal of Algorithms Design and Analysis 1(1).

  9. Shen S, Bui AA, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Computers in biology and medicine. 2015;57:139-49.
  10. Messay T, Hardie RC, Tuinstra TR. Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset. Medical Image Analysis. 2015.(paper)
  11. Magdy, E., N. Zayed, et al. Automatic Classification of Normal and Cancer Lung CT Images using Multi-scale AM-FM Features. Intl Journal of Biomedical Imaging, 2015. (link)

  12. Lassen BC, Jacobs C, et al. Robust Semi-automatic Segmentation of Pulmonary Subsolid Nodules in Chest Computed Tomography Scans. Phys Med Biol (2015) 60(3):1307-1323. (link)

  13. Kumar, D., M. J. Shafiee, et al. Discovery Radiomics for Computed Tomography Cancer Detection. arXiv e-print, 2015. (arXiv link)

  14. Demir, Ö. and A. Yılmaz Çamurcu (2015). "Computer-aided detection of lung nodules using outer surface features." Bio-Medical Materials and Engineering 26(s1): 1213-1222.

  15. Kumar, A., F. Nette, et al. (2014). "A Visual Analytics Approach using the Exploration of Multi-Dimensional Feature Spaces for Content-based Medical Image Retrieval  IEEE J Biomed Health Inform (2014) 19(5):1734:1746 (pubmed link)

  16. Sivakumar S and Chandrasekar C, Lung nodule detection using fuzzy clustering and support vector machines. International Journal of Engineering and Technology, 2013. 5(1):179-185.(link)
  17. Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume. Academic Radiology, 2013. 20(2):173-180. doi: 10.1016/j.acra.2012.08.014. (link)
  18. Aggarwal P, Vig R, and Sardana H Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases. Journal of Computers, 2013. 8(9):2245-2255. (link)
  19. Sivakumar S and Chandrasekar C, Lungs image segmentation through weighted FCM.Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference. 25-27 April 2012 pages 109-113. IEEE. doi:10.1109/RACSS.2012.6212707 (link)
  20. Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
  21. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. Biomedical Engineering, IEEE Transactions. 2011. 58(12):3418-3428. doi: 10.1109/TBME.2011.2167621. (link)
  22. Raicu DS, Varutbangkul E, Furst JD, Armato SG III: Modeling semantics from image data: Opportunities from LIDC. International Journal of Biomedical Engineering and Technology 3: 83–113, 2010.

  23. Zinovev D, Duo Y, Raicu DS, Furst JD, Armato SG III: Consensus versus disagreement in imaging research: A case study using the LIDC Database. Journal of Digital Imaging 25: 423–436, 2012.


The following refer to the LIDC Collection data, created before submission to TCIA

  1. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Medical Physics, 38: 915–931, 2011. (link)
  2. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, The Lung Image Database Consortium Research Group: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232: 739–748, 2004.
  3. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, Mullan BF, Yankelevitz DF, van Beek EJR, Armato SG III, McNitt-Gray MF, Reeves AP, Gur D, Henschke CI, Hoffman EA, Bland PH, Laderach G, Pais R, Qing D, Piker C, Guo J, Starkey A, Max D, Croft BY, Clarke LP: Evaluation of lung MDCT nodule annotation across radiologists and methods. Academic Radiology 13: 1254–1265, 2006.
  4. Armato SG III, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology 14: 1409–1421, 2007.
  5. Armato SG III, Roberts RY, McNitt-Gray MF, Meyer CR, Reeves AP, McLennan G, Engelmann RM, Bland PH, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth.” Academic Radiology 14: 1455–1463, 2007.
  6. McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais R, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology 14: 1464–1474, 2007.
  7. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJR, Kazerooni EA, Yankelevitz DF, McNitt-Gray MF, McLennan G, Armato SG III, Henschke CI, Aberle DR, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 14: 1475–1485, 2007.
  8. Armato SG III, Roberts RY, Kocherginsky M, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz DF, McLennan G, McNitt-Gray MF, Meyer CR, Reeves AP, Caligiuri P, Quint LE, Sundaram B, Croft BY, Clarke LP: Assessment of radiologist performance in the detection of lung nodules: Dependence on the definition of “truth”. Academic Radiology 16: 28–38, 2009.

Collection: Mouse-Mammary


These refer to the Mouse-Mammary Collection data, created before submission to TCIA

  1. Jansen SA et al, NMR Biomed. 2011 Aug;24(7):880-7. 
  2. Jansen SA et al, Breast Cancer Res. 2009;11(5):R65. 
  3. Jansen SA et al, Radiology. 2009 Nov;253(2):399-406.
  4. Jansen SA et al, Phys Med Biol. 2008 Oct 7;53(19):5481-93.
  5. Jansen SA., Ductal carcinoma in situ: magnetic resonance and ultrasound imaging in mouse models of breast cancer (Mouse.Mammary.MRI.Ultrasound.Summary.pdf).
  6. Jansen S., Investigating genetic events in the progression of ductal carcinoma in situ (Mouse.Mammary.Genetics.DCIS.pdf).

Collection: NLST

Please see List of NLST Publications at NIH to browse publications from this Data Collection.

Collection: NSCLC-Radiomics

  1. Patil R, Mahadevaiah G, Dekker A. An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features. Tomography: a journal for imaging research. 2016;2(4):374-7. (link)


Collection: Phantom FDA

  1. Peskin AP, Dima AA, Saiprasad G. An Automated Method for Locating Phantom modules in Anthropomorphic Thoracic Phantom CT Studies. The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2012.(link)
  2. Gavrielides MA, Kinnard LM, Myers KJ ,Peregoy J, Pritchard WF, Zeng R, Esparza J, Karanian J, Petrick N, A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom, Optics Express , vol. 18, n.14, pp. 15244-15255, 2010. (link)

Collection: QIN Breast

  1. Mohammed Ammar, Saïd Mahmoudi, Drisis Stylianos. Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis. Procedia Computer Science, Volume 100, 2016, Pages 812-817, ISSN 1877-0509, doi: 10.1016/j.procs.2016.09.229
  2. Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Yankeelov TE. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Investigative Radiology, 2015 Apr;50(4):195-204. PMCID: PMC4471951 doi: 10.1097/RLI.0000000000000100.
  3. Weis JA, Miga MI, Arlinghaus LR, Li X, Abramson V, Chakravarthy AB, Pendyala P, Yankeelov TE. Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res. 2015 Nov 15;75(22):4697-707. doi: 10.1158/0008-5472.CAN-14-2945.

  4. Atuegwu NC, Arlinghaus L, Li X, Welch EB, Chakravarthy AB, Gore JC, Yankeelov TE. Integration of diffusion weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy. Magnetic Resonance in Medicine 2011; 66:1689-96. PMCID: PMC3218213
  5. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer. Magnetic Resonance in Medicine, 2012; 68:261-71. PMCID: PMC3291742
  6. Smith DS, Gambrell JV, Li X, Arlinghaus LA, Quarles CC, Yankeelov TE, Welch EB. Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Acquisitions on Derived Parameters for DCE and DSC-MRI. IEEE Transactions in Medical Imaging, 2012; 31:504-11. PMCID: PMC3289060
  7. Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time Compressive Sensing MRI Reconstruction using GPU Computing and Split Bregman Methods. International Journal of Biomedical Imaging, 2012; 2012:864827. PMCID: PMC3296267
  8. Dula AN, Arlinghaus LR, Dortch RD, Dewey BE, Whisenant JE, Ayers GD, Yankeelov TE, Smith SE. Amide Proton Transfer Imaging of the Breast at 3 T: Establishing reproducibility and possible feasibility for assessing chemotherapy response. Magnetic Resonance in Medicine, 2013; 70: 216-24. PMCID: PMC3505231
  9. Yankeelov TE, Peterson TE, Abramson RG, Garcia-Izquierdo D, Arlinghaus LR, Li X, Atuegwu NC, Catana C, Manning HC, Fayad ZA, Gore JC. Simultaneous PET-MRI in Oncology: A Solution Looking for a Problem? Magnetic Resonance Imaging, 2012; 30:1342-56. Selected as a Top 25 paper in Magnetic Resonance Imaging, 2012. PMCID: PMC3466373
  10. Abramson RG, Arlinghaus LR, Weis JA, Li X, Dula AN, Chekmenev EY, Smith SA, Miga MI, Abramson VG, Yankeelov TE. Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy. Breast Cancer: Targets and Therapies, 2012; 4: 139-154. PMCID: PMC3496377
  11. Li X, Abramson RG, Arlinghaus LR, Chakravarthy AB, Abramson V, Mayer I, Farley J, Delbeke D, Yankeelov TE. An Algorithm for Longitudinal Registration of PET/CT Images Acquired During Neoadjuvant Chemotherapy in Breast Cancer: Preliminary Results. European Journal of Nuclear Medicine and Molecular Imaging Research, 2012; 16:62. PMCID: PMC3520720
  12. Fluckiger U, Loveless ME, Barnes SL, Lepage M, Yankeelov TE. A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations, and experimental results. Physics in Medicine and Biology, 2013; 58:1983-98. PMCID: PMC3646091
  13. Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomathematics, 2012; Article ID 287394. PMCID: PMC3729405
  14. Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity During Neoadjuvant Chemotherapy. Translational Oncology, 2013; 6:253-64. PMCID: PMC3660793
  15. Klomp DWJ, Dula AN, Arlinghaus LR, Italiaander M, Dortch RD, Zu Z, Williams JM, Gochberg DF, Luijten PR, Gore JC, Yankeelov TE, Smith SA. Amide Proton Transfer Imaging of the Human Breast at 7 Tesla: Development and Reproducibility. NMR in Biomedicine, 2013; 26:1271-7. PMCID: PMC3726578
  16. Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE. Machine Learning for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy. Journal of the American Medical Informatics Association, 2013; 20:688-95. PMCID: PMC3721158
  17. Li X, Arlinghaus LR, Ayers GD, Chakravarthy AB, Abramson RG, Abramson VG, Atuegwu N, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Bhave SR, Yankeelov TE. DCE-MRI Analysis Methods for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy: Pilot Study Findings. Magnetic Resonance in Medicine, 2014; 71(4):1592-602. PMCID: PMC3742614
  18. Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Science Translational Medicine 2013; 5:187ps9. PMCID: PMC3938952
  19. Abramson RG, Hoyt TL, Wilson KJ, Li X, Arlinghaus LR, Su P-F, Abramson VG, Chakravarthy AB, Yankeelov TE. Early Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy by Semi- Quantitative Analysis of High Temporal Resolution DCE-MRI: Preliminary Results. Magnetic Resonance Imaging, 2013 ; 31:1457-64. PMCID: PMC3807825
  20. Weis JA, Miga MI, Arlinghaus LA, Li X, Chakravarthy AB, Abramson VG, Farley J, Yankeelov TE. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Physics of Medicine and Biology, 2013; 58:5851-66. PMCID: PMC3791925
  21. Smith DA, Yankeelov TE, Welch EB. Potential of Compressed Sensing in Quantitative MR Imaging of Cancer. Cancer Imaging, 2013; 13:633-44. PMCID: PMC3893904
  22. Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE. Using dynamic contrast enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. International Journal of Biomedical Imaging, 2013; 2013:576470. PMCID: PMC3814089
  23. Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Mulkern R, Yankeelov TE, Fennessy FM. A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step Towards Practical Implementation. Magnetic Resonance Imaging, 2014; 32:321-9. PMCID: PMC3965600
  24. Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational Oncology, 2014; 7:14-22. PMCID: PMC3998687
  25. Chenevert TL, Malyarenko DI, Newitt D, Hylton N, Huang W, Li X, Tudorica A, Fedorov A, Fennessy F, Kikinis R, Arlinghaus L, Li X, Yankeelov TE, Muzi M, Marro KI, Kinahan PE, Jajamovich GH, Dyvorne HA, Taouli B, Kalpathy-Cramer J, Oborski MJ, Laymon CM, Mountz JM, Ross BD. Error in Quantitative Image Analysis Due to Platform-Dependent Image Scaling. Translational Oncology, 2014; 7:65-71. PMCID: PMC3998685
  26. Huang W, Li X, Chen Y, Li X, Chang M-C, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Federov A, Tudorica A, Gupta S, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge. Translational Oncology, 2014; 7:153-66. PMCID: PMC3998693
  27. Atuegwu NC, Li X, Arlinghaus LR, Abramson RG, Williams JM, Chakravarthy AB, Abramson V, Yankeelov TE. Longitudinal, Inter-modality Registration of Quantitative Breast PET and MRI Data Acquired Before and During Neoadjuvant Chemotherapy: Preliminary Results. Medical Physics, 2014; 41:052302. PMCID: PMC4000383

These refer to the QIN-Breast Collection data, created before submission to TCIA

  1. Li X, Dawant BM, Welch EB, Chakravarthy AB, Freehardt D, Mayer I, Kelley M, Meszoely I, Gore JC, Yankeelov TE. Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms. Medical Physics, 2010; 37:2541-52. PMCID: PMC2881925
  2. Atuegwa NC, Gore JC, Yankeelov TE. Using Quantitative Imaging Data to Drive Mathematical Models of Tumor Growth and Treatment Response. Physics in Medicine and Biology, 2010; 55:2429-49. PMCID: PMC2897238
  3. Yankeelov TE, Arlinghaus L, Li X, Gore JC. The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer. Seminars in Oncology, 2011; 38:16-25. PMCID: PMC3073543
  4. Arlinghaus L, Li X, Levy M, Smith D, Welch WB, Gore JC, Yankeelov TE. Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy. Journal of Oncology, 2010. pii: 919620. Epub 2010 Sep 29. PMCID: PMC2952974
  5. Arlinghaus LR, Welch EB, Chakravarthy AB, Farley JS, Gore JC, Yankeelov TE. Motion and distortion correction in diffusion-weighted MRI of the breast at 3T. Journal of Magnetic Resonance Imaging, 2011; 33:1063-70. PMCID: PMC3081111
  6. Gore JC, Manning HC, Quarles CC, Waddell KW, Yankeelov TE. Magnetic Resonance in the Era of Molecular Imaging of Cancer. Magnetic Resonance Imaging, 2011; 29:587-600. PMCID: PMC3285504
  7. Arlinghaus LR, Li X, Rahman AR, Welch EB, Xu L, Gore JC, Yankeelov TE. On the Relationship Between the Apparent Diffusion Coefficient and Extravascular Extracellular Volume Fraction in Human Breast Cancer. Magnetic Resonance Imaging, 2011; 29:630-8. PMCID: PMC3100356
  8. Smith DS, Welch EB, Li X, Arlinghaus LD, Loveless ME, Koyama T, Gore JC, Yankeelov TE. Quantitative effects of accelerated dynamic contrast enhanced MRI data using compressed sensing. Physics in Medicine and Biology, 2011; 56:4933-46. PMCID: PMC3192434
  9. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 2011; 56:5753-69. PMCID: PMC3176673


Collection: QIN Breast DCE-MRI

  1. Nowaková J, Prílepok M, Snášel V. Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree. Journal of Medical Systems. 2017;41(2):18. (link)

Collection: QIN GBM DCE-MRI

  1. Gerstner ER, Zhang Z, Fink JR, Muzi M, Hanna L, Greco E, Mintz A, Kostakoglu L, Eikman EA, Prah MA, Ellingson BM, Ratai EM, Schmainda KM, Sorensen G, Barboriak DP,  Mankoff DA. ACRIN 6684: Assessment of tumor hypoxia in newly diagnosed GBM using 18F-FMISO PET and MRI. Clin Cancer Res 2016. Accepted.
  2. Gerstner ER, Zhang Z, Fink JR, Muzi M, Hanna L, Greco E, Mintz A, Kostakoglu L, Eikman EA, Prah M, Schmainda KM, Sorensen GA, Barboriak D,  Mankoff DA. ACRIN 6684: Assessment of tumor hypoxia in newly diagnosed GBM using 18F-FMISO PET and MRI. J Clin Oncol 33(Suppl):2024. 2015.
  3. Fink JR, Zhang Z, Gerstner ER, Muzi M, Kostakoglu L, Mintz A, Eikman EA, Barboriak D,  Mankoff DA. ACRIN 6684: Multicenter phase II assessment of tumor hypoxia in glioblastoma using 18F-Fluoromisonidazole (FMISO) PET and MRI. J Nucl Med 56(Suppl3):325. 2015.
  4. Fink JR, Muzi M, Peck M,  Krohn KA. Multimodality Brain Tumor Imaging: MR Imaging, PET, and PET/MR Imaging. J Nucl Med 56(10):1554-1561. 2015.
  5. Muzi M, Fink JR, Richards TL, Marro KI, Wong T, Muzi JP, Eary JF, Rockhill JK,  Krohn KA. Evaluation of PET and MR measurements to examine progression in glioma patients. J Nucl Med 55(Suppl1):1512-. 2014.

Collection:  QIN HeadNeck

  1. Stoll M, Stoiber EM, Grimm S, Debus J, Bendl R, Giske K. Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations. PloS one. 2016;11(12):e0168916. DOI: 10.1371/journal.pone.0168916
  2. Ahmadvand P, Duggan N, Bénard F, Hamarneh G. Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface. MLMI 2016 in conjunction with the 19th Int'l Conference on MICCAI. (link)
  3. Fedorov A, Clunie D, Ulrich E, et al. (2016DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer researchPeerJ 4:e2057 DOI: 10.7717/peerj.2057
  4. Beichel RR, Van Tol M, Ulrich EJ, et al. (2016) Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Medical Physics 43:2948–2964. DOI: 10.1118/1.4948679.

Collection: QIN Prostate

  1. Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Tempany C, Mulkern R, Yankeelov TE, Fennessy FM. A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magnetic resonance imaging. 2014;32(4):321-9.
  2. Hegde JV, Mulkern RV, Panych LP, Fennessy FM, Fedorov A, Maier SE, Tempany C. Multiparametric MRI of prostate cancer: An update on state‐of‐the‐art techniques and their performance in detecting and localizing prostate cancer. Journal of Magnetic Resonance Imaging. 2013;37(5):1035-54.
  3. Benalcázar, M. E., M. Brun, et al. (2015). Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, Springer.
  4. Li, A., C. Li, et al. (2013). Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker. Digital Image Computing: Techniques and Applications (DICTA), 2013 International Conference on, IEEE.

  5. Qiu, W., J. Yuan, et al. (2014). Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images. Medical Imaging, IEEE Transactions on 33(4): 947-960.

  6. Xie, Q. and D. Ruan (2014). Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics. Medical physics 41(4): 041909.

  7. Zhao, T. and D. Ruan (2015). Two-stage fusion set selection in multi-atlas-based image segmentation. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, IEEE.

Collection:  QIN Sarcoma

  1. Meyer JM, Perlewitz KS, Hayden JB, Doung Y-C, Hung AY, Vetto JT, Pommier RF, Mansoor A, Beckett BR, Tudorica A. Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates. Clinical Cancer Research. 2013;19(24):6902-11.

Collection: RIDER Collections

  1. Barani R, Sumathi M. A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion. International Journal of Signal Processing, Image Processing and Pattern Recognition. 2016;9(11):271-82. DOI: 10.14257/ijsip.2016.9.11.25
  2. Aerts, H. J. W. L. et al. Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC. Sci. Rep.(2016) 6, 33860 (link)
  3. Oliveira B, O'Halloran M, Conceicao R, Glavin M, Jones E. Development of Clinically-Informed 3D Tumor Models for Microwave Imaging Applications. IEEE Antennas and Wireless Propagation Letters 2016;15:520-3. DOI: 10.1109/LAWP.2015.2456051

  4. Melouah A. Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique.  Computer Science and Its Applications: Springer; 2015. p. 119-28.
  5. Aerts HJ, Velazquez ER, Leijenaar RTH, Parmar C, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014. 5(4006). DOI:10.1038/ncomms5006 (link)
  6. Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B. Test–Retest Reproducibility Analysis of Lung CT Image Features. Journal of digital imaging. 2014:1-19.
  7. Melouah, A. (2015). Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications, Springer: 119-128.
    Desseroit M-C, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Le Rest CC, Hatt M. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III. European journal of nuclear medicine and molecular imaging. 2016:1-9. DOI: 10.1007/s00259-016-3325-5  


These refer to the RIDER Collections data, created before submission to TCIA

  1. Meyer CR, Armato SG III, Fenimore CP, McLennan G, Bidaut LM, Barboriak DP, Gavrielides MA, Jackson EF, McNitt-Gray MF, Kinahan PE, Petrick N, Zhao B. Quantitative imaging to assess tumor response to therapy: Common themes of measurement, truth data and error sources. Translational Oncology 2: 198–210, 2009. (link)
  2. McNitt-Gray MF, Bidaut LM, Armato SG III, Meyer CR, Gavrielides MA, Fenimore CP, McLennan G, Petrick N, Zhao B, Reeves AP, Beichel R, Kim H-J, Kinnard L. CT assessment of response to therapy: Tumor volume change measurement, truth data and error.Translational Oncology2009. 2:216–222. (link)
  3. Kinahan PE, Doot RK, Wanner-Roybal M, Bidaut LM, Armato SG III, Meyer CR, McLennan G.PET/CT assessment of response to therapy: Tumor change measurement, truth data and error.Translational Oncology 2:223–230, 2009. (link)
  4. Jackson EF, Barboriak DP, Bidaut LM, Meyer CR. Magnetic resonance assessment of response to therapy: tumor change measurement, truth data and error sources.Translational Oncology 2009 Dec;2(4):211-5. PubMed PMID: 19956380; PubMed Central PMCID: PMC2781079. (link)
  5. Armato SG 3rd, Meyer CR, Mcnitt-Gray MF, McLennan G, Reeves AP, Croft BY, Clarke LP;RIDER Research Group. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.Clin Pharmacol Ther. 2008 Oct;84(4):448-56. PubMed PMID: 18754000. (link)

Collection: SPIE-AAPM Lung CT Challenge

  1. Park SY and Sargent D. Tumor propagation model using generalized hidden Markov model. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G February 24, 2017); 10.1117/12.2254583
  2. Sargent D, Park S-Y. Semi-automatic 3D lung nodule segmentation in CT using dynamic programming. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332R (February 24, 2017) doi: 10.1117/12.2254575
  3. Nishio M, Nagashima C. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Academic Radiology. 2017;24(3):328-36. (link)

Collection: TCGA-BRCA

  1. Angela Giardino, Supriya Gupta, Emmi Olson, Karla Sepulveda, Leon Lenchik, Jana Ivanidze, Rebecca Rakow-Penner, Midhir J. Patel, Rathan M. Subramaniam, Dhakshinamoorthy Ganeshan. Role of Imaging in the Era of Precision Medicine. Academic Radiology, Available online 25 January 2017 doi: 10.1016/j.acra.2016.11.021
  2. Albiol, Alberto; Corbi, Alberto; Albiol, Francisco. Automatic intensity windowing of mammographic images based on a perceptual metric. Medical Physics, 2473-4209.10.1002/mp.12144 
  3. Wu, J; Sun, X; Wang, J; Cui, Y;  Kato, F; Shirato, H; Ikeda, DM.; Li, R. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of Magnetic Resonance Imaging, 2586 doi: 10.1002/jmri.25661
  4. Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li R. Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical Cancer Research. 2017:clincanres. 2415.016. (link)

  5. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology, 2014. DOI: 10.1148/radiol.14132641 (link)
  6. Lavasani, S. N., A. F. Kazerooni, et al. (2015). Discrimination of Benign and Malignant Suspicious BreastTumors Based on Semi-Quantitative DCE-MRI ParametersEmploying Support Vector Machine. Frontiers in Biomedical Technologies 2(2): 397-403.

  7. Anand, S., V. Vinod, et al. Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images. International Journal of Applied Engineering Research 10(64): 2015.

  8. Guo, W., H. Li, et al. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging 2(4): 041007-041007.

Collection: TCGA-GBM

  1. Lee, J.K., Wang, J., Sa, J.K., et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics.(2017) DOI: 10.1038/ng.3806
  2. Cui Y, Ren S, Tha KK, Wu J, Shirato H, Li R. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. European Radiology. 2017:1-10. (link)

  3. Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Computer Methods and Programs in Biomedicine. 2017;140:249-57.(link)

  4. Czarnek N, Clark K, Peters KB, Mazurowski MA. Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. Journal of Neuro-Oncology. 2017:1-8. (link)

  5. Chaddad A, Desrosiers C, Toews M, editors. Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016.

  6. Prasanna, P., Patel, J., Partovi, S. et al. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.  Eur Radiol (2016) pp 1–10. DOI:10.1007/s00330-016-4637-3

  7. Mulvey M, Muhyadeen S,  Sinha U. Classification of Glioblastoma Multiforme Molecular Subtypes Using Three-Dimensional Multi-Modal MR Imaging Features. Med. Phys. 43, 3373 (2016); (link)

  8. Ren X, Cui Y, Gao H,  Li, R. Identifying High-Risk Tumor Volume Based On Multi-Region and Integrated Analysis of Multi-Parametric MR Images for Prognostication of Glioblastoma. Med. Phys. 43, 3751 (2016); (link)
  9. Dunn WD Jr,  Aerts HJWL, et al.  Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma.   J   Neuroimaging Psychiatry Neurol 2016. 1(2): 64-72.
  10. Upadhaya T, Morvan Y, et al. Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices. Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850W (March 24, 2016); (link)
  11. Upadhaya T, Morvan Y, et al. Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, pp. 50-54. IEEE, 2015.

  12. Upadhaya T, Morvan Y, et al. A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme. IRBM. 2015 Nov 30;36(6):345-50.

  13. Reza SM, Mays R, Iftekharuddin KM, editors. Multi-fractal detrended texture feature for brain tumor classification. SPIE Medical Imaging; 2015: International Society for Optics and Photonics.

  14. Nabizadeh N, Kubat M. Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Computers & Electrical Engineering. 2015.

  15. Natteshan N, Jothi JAA. Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers.  Advances in Intelligent Informatics: Springer; 2015. p. 19-30. (link)

  16. Zhang J, Barboriak DP, Hobbs H, Mazurowski MA. A fully automatic extraction of magnetic resonance image features in Glioblastoma patients. Medical physics. 2014;41(4):042301.

  17. Wangaryattawanich P, Wang J, Thomas GA, Chaddad A, Zinn PO, Colen RR, editors. Survival analysis of pre-operative GBM patients by using quantitative image features. Control, Decision and Information Technologies (CoDIT), 2014 International Conference on; 2014: IEEE.

  18. Colen RR, Wang J, Singh SK, Gutman DA, Zinn PO. Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. Radiology. 2014.

  19. Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Rajan J, Jilwan-Nicola M, Chen JY, Raghavan P, Holder CA, Rubin D, Huang E, Kirby J, Freymann J, Jaffee CC, Flanders A, Zinn PO. Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.BMC Medical Genomics, 2014. 7(1):30. DOI: 10.1186/1755-8794-7-30 (link)
  20. Gevaert O, Mitchell LA, Achrol AS, Xu J, Echegaray S, Steinberg GK, Chesier SH, Napel S, Zaharchuk G, Plevritis SK. Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology, 2014. doi: 10.1148/radiol.14131731 (link)
  21. Mazurowski MA, Zhang J, Peters KB, and Hobbs H. Computer-extracted MR imaging features are associated with survival in glioblastoma patients. Journal of Neuro-Oncology, 2014. 120(3):483–488 DOI: 10.1007/s11060-014-1580-5 (link)
  22. Jain R, Poisson L, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Jaffe C, Mikkelsen T, Flanders A. Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology. 2014 Aug;272(2):484-93. doi: 10.1148/radiol.14131691. Epub 2014 Mar 19. 2014 (link)
  23. Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, July 2014. doi: 10.1016/j.neurad.2014.02.006

  26. Kwon D, Shinohara RT, Akbari H, Davatzikos C. Combining Generative Models for Multifocal Glioma Segmentation and Registration.  Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: Springer; 2014. p. 763-70.

  29. Gutman DA, Cooper LAD, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Monqkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set. Radiology. 2013 May:267(2):560-569,doi:10.1148/radiol.13120118 (link)
  30. Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology, 2013 Apr:267(1):212 –220, doi:10.1148/radiol.12120846 (link)
  31. Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro-oncology, 2013. 15(10):1389-1394 (link)
  32. Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
  33. Jain R, Poisson L, Narang J, Scarpace L, Rosenblum ML, Rempel S, Mikkelson T. Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study. American Journal of Neuroradiology, 2012. 33(7):1343-1348 [Epub ahead of print] (link)
  34. Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, et al. A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 2012 7(8): e41522. doi:10.1371/journal.pone.0041522 (link)
  35. Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE, 2011 6(10): e25451. doi:10.1371/journal.pone.0025451 (link)
  36. Wangaryattawanich, P., M. Hatami, et al.  "Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival." Neuro-oncology, (2015): nov117 .

  37. Kuo, J. S., K. B. Pointer, et al. (2015). "139 Human Ether-a-Go-Go-Related-1 Gene (hERG) K+ Channel as a Prognostic Marker and Therapeutic Target for Glioblastoma." Neurosurgery 62: 210-211.

  38. Zinn, P. O., M. Hatami, et al. (2015). "138 Diffusion MRI ADC Mapping of Glioblastoma Edema/Tumor Invasion and Associated Gene Signatures." Neurosurgery 62: 210.

  39. Steed, T., J. Treiber, et al. (2015). "Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images." American Journal of Neuroradiology 36(4): 678-685.

  40. Lee, J., S. Narang, et al. (2015). "Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation." Journal of Medical Imaging 2(4): 041006-041006.

  41. Itakura, H., A. S. Achrol, et al. (2015). "Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities." Science Translational Medicine 7(303): 303ra138-303ra138.

  42. Cui, Y., K. K. Tha, et al. (2015). "Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images." Radiology: 150358.

  43. Lee, J., S. Narang, et al. (2015). "Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme." PloS one 10(9): e0136557.

  44. Rios Velazquez E, Meier R, Dunn WD Jr, Alexander B, Wiest R, Bauer S, Gutman DA, Reyes M, Aerts HJ. "Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features." Sci Rep. 2015 Nov 18;5:16822. doi: 10.1038/srep16822.

Collection: TCGA-KIRC 

  1. Chen X, Zhou Z, Thomas K, Wang J. Predicting Gene Mutations in Renal Cell Carcinoma Based On CT Imaging Features: Validation Using TCGA-TCIA Datasets. Med. Phys. 43, 3705 (2016); (link)
  2. Zhu H, Chen H, Lin Z, Shi G, Lin X, Wu Z, Zhang X. Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system. Oncotarget. 2016. (link)
  3. Shinagare AB, Vikram R, Jaffe C, Akin O, Kirby J, Huang E, Freymann J, Sainani NI, Sadow CA, Bathala TK. Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group. Abdominal imaging. 2015:1-9.

Collection: 4D-Lung 

  1. Woodruff, H. C., Shieh, C.-C., Hegi-Johnson, F., Keall, P. J. and Kipritidis, J. (2017), Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT. Med. Phys. DOI: 10.1002/mp.12199
  2. Hugo GD, Weiss E,  Sleeman WC, Balik S, Keall PJ, Lu J, Williamson JF. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med. Phys. (2017) doi: 10.1002/mp.12059


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