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If you use data from The Cancer Image Archive (TCIA) in your research we ask that you include appropriate references in your publications and presentations. 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 help@cancerimagingarchive.net 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

April 2016 Publications Bar Graph


Table of Contents

TCIA General

  1. Toga AW, Dinov ID. Sharing big biomedical data. Journal of Big Data. 2015;2(1):1-12.
  2. Herskovits EH. Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 2014;124:1-30.
  3. 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-.
  4. 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.
  5. Bourne PE. DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 2015;275(1):3-4. doi: doi:10.1148/radiol.15150144. PubMed PMID: 25799330.
  6. 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)
  7. 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)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. Jaffe, C Carl. Imaging and Genomics: Is There a Synergy?Radiology. 2012. 264(2):329-31.(paper).
  13. 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).
  14. 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)
  15. 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)
  16. 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

Radiogenomics

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

  2. Pope WB. Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 2015;25(1):105-19.

  3. 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.
  4. 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.
  5. 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. doi: 10.1148/radiol.12111607. PubMed PMID: 22723499; PubMed Central PMCID: PMCPMC3401348.
  6. Feldman, M., M. G. Piazza, et al. (2015). 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target. Neurosurgery 62: 209-210.

  7. Gutman, D. A., W. D. Dunn Jr, et al. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology: 1-11.
  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. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. doi: 10.1007/s40484-016-0061-6
  10. 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

Radiomics

  1.  Parmar, C., P. Grossmann, et al. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.
  2. 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.

Algorithm Development

  1. 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.
  2. 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.
  3. 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.
  4. Benninghoff H, Garcke H. Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes. arXiv preprint arXiv:150607136. 2015.
  5. 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.
  6. Guvenis A, Koc A. OPTIMISING DELINEATION ACCURACY OF TUMOURS IN PET FOR RADIOTHERAPY PLANNING USING BLIND DECONVOLUTION. Radiation Protection Dosimetry. 2015:ncv110.
  7. 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).
  8. 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.
  9. 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.
  10. Blessy SPS, Sulochana CH. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation. Technology and Health Care. 2014.
  11. 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.
  12. Hong S, Huang Y, Cao Y, Chen X, Han J-DJ. Approaches to uncovering cancer diagnostic and prognostic molecular signatures. Molecular & Cellular Oncology. 2014.
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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.

  21. 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)
  22. 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.

  23. 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)
  24. 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)
  25. Codella N, Merler M. IBM TJ Watson Research Center. Semantic Model Vector for ImageCLEF2013. June 18, 2014. (link)
  26. 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)
  27. Agostinelli F, Anderson MR, Lee H, editors. Robust Image Denoising with Multi-Column Deep Neural Networks. Advances in Neural Information Processing Systems; 2013.

  28. 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
  29. 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)
  30. 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
  31. 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)
  32. 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)
  33. 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)
  34. 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)
  35. 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
  36. 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)
  37. Huang L-C, Tseng L-Y, Hwang M-S. A reversible data hiding method by histogram shifting in high quality medical images. Journal of Systems and Software. 2013;86(3):716-27. doi: 10.1016/j.jss.2012.11.024.
  38. 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)
  39. 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)
  40. 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)
  41. 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)
  42. 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.
  43. 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)
  44. 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
  45. 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
  46. 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.

  47. 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.

  48. 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.

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

  50. Chaddad, A. and C. Tanougast "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015.  doi: 10.1155/2015/728164 
  51. 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.

Theses

  1. 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)

  2. Nabizadeh N. Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images. Miami, FL: University of Miami; 2015. (link to thesis)

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

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

  6. 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)

  7. 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.

TCIA DOI

  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. Colen RR, Wang J, et al. (2014). Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death. TCIA. Saint Louis, MO. (link)
  4. Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. TCIA. Saint Louis, MO. (link)

  5. 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)
  6. 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)
  7. 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)

  8. 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)

  9. 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)

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

  11. 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)
  12. 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)
  13. 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)

  14. Morris E, Burnside M, et al. (2014). TCGA Breast Phenotype Research Group Data sets. TCIA. Saint Louis, MO (link)
  15. Bloch N, Rusu M, et al. (2015) NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures. TCIA. St. Louis, MO. (link)
  16. Roth H, Lu L, et al. (2015). A new 2.5D representation for lymph node detection in CT. TCIA. Saint Louis, MO. (link)

  17. 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)

  18. 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)

QIN

  1. Clarke, L. P., R. J. Nordstrom, et al. (2014). "The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals." Translational Oncology 7(1): 1-4. (link)

  2. 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.

  3. Kalpathy-Cramer, J., J. B. Freymann, et al. (2014). "Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive." Translational oncology 7(1): 147-152.

  4. Levy, M. A., J. B. Freymann, et al. (2012). "Informatics methods to enable sharing of quantitative imaging research data." Magnetic Resonance Imaging.

Publications relating to specific data collections:

Collection:  CT Colonography

  1. 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.
  2. Namías R, et al., Automatic rectum limit detection by anatomical markers correlation. Computerized Medical Imaging and Graphics, 2014. 38(4):245-250.(link)
  3. 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)
  4. 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)
  5. C.D. Johnson, MD, MMM,M-H. Chen, PhD, A.Y. Toledano, ScD, J.P. Heiken, MD, A. Dachman, MD, M.D. Kuo, MD, C. Menias, MD, B. Siewert, MD, J.I. Cheema, MD, R.G. Obregon, MD, J.L. Fidler, MD, P. Zimmerman, MD, K.M. Horton, MD, K. Coakley, MD, R.B. Iyer, MD, A.K. Hara, MD, R.A. Halvorsen, Jr., MD, G. Casola, MD, J. Yee, MD, B. A. Herman, SM, L.J. Burgart, MD, and P.J. Limburg, MD, MPH. Accuracy of CT Colonography for Detection of Large Adenomas and Cancers. N Engl J Med. 2008 Sep 18; 359(12): 1207–1217. doi:  10.1056/NEJMoa0800996. (paper)

Collection:  LIDC-IDRI

  1. Wang W, Luo J, Yang X, Lin H. Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Academic Radiology. 2015.
  2. 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.
  3. 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)
  4. 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)
  5. 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)
  6. 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)
  7. Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
  8. 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)
  9. 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)
  10. 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)
  11. Kumar, D., M. J. Shafiee, et al. (2015). "Discovery Radiomics for Computed Tomography Cancer Detection." arXiv preprint arXiv:1509.00117.

  12. 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).

  13. Magdy, E., N. Zayed, et al. "Automatic Classification of Normal and Cancer Lung CT Images using Multi-scale AM-FM."

  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."

Collection:  NLST

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:  Quantitative Imaging Network (QIN)

  1. 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)
  2. Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpahthy-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 Feb;7(1):153-66. (link)
  3. 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: http://dx.doi.org/10.1593/tlo.13832. (link)
  4. Levy MA, Freymann JB, Kirby JS, Fedorov A, Fennessy FM, Eschrich SA, Berglund AE, Fenstermacher DA, Tan Y, Guo X, Casavant TL, Brown BJ, Braun TA, Dekker A, Roelofs E, Mountz JM, Boada F, Laymon C, Oborski M, Rubin DL. 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)

Collection:  QIN Breast

  1. 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.
  2. 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.

  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomathematics, 2012; Article ID 287394. PMCID: PMC3729405
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. Smith DA, Yankeelov TE, Welch EB. Potential of Compressed Sensing in Quantitative MR Imaging of Cancer. Cancer Imaging, 2013; 13:633-44. PMCID: PMC3893904
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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


Collection:  QIN HeadNeck

 

  1. Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (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 https://doi.org/10.7717/peerj.2057 (link)
  2. Beichel RR., Van Tol M., Ulrich EJ., Bauer C., Chang T., Plichta KA., Smith BJ., Sunderland JJ., Graham MM., Sonka M., Buatti JM. 2016. Semiautomatedsegmentation of head and neck cancers in 18F-FDG PET scans: Ajust-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. 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

  2. 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.
  3. 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)
  4. 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.
  5. 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)
  6. 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)
  7. 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)
  8. 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)
  9. 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)
  10. 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.

  11. 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. http://dx.doi.org/10.1007/s00259-016-3325-5

Collection:  TCGA-BRCA

  1. 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)
  2. 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.

  3. 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.

  4. 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. Taman Upadhaya ; Yannick Morvan ; Eric Stindel ; Pierre-Jean Le Reste and Mathieu Hatt. 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); doi:10.1117/12.2217151
  2. Upadhaya, Taman, Yannick Morvan, Eric Stindel, Le Reste, and Mathieu Hatt. 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.

  3. Upadhaya T, Morvan Y, Stindel E, Le Reste PJ, Hatt M. 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.

  4. 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.

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

  6. 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)

  7. 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.

  8. 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.

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

  10. 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)
  11. 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)
  12. 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 Aug 24 [Epub ahead of print] doi: 10.1007/s11060-014-1580-5 (link)
  13. 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)
  14. 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
  15. Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE FOR EGFR MUTATION IDENTIFICATION IN GLIOBLASTOMA. Neuro-Oncology. 2014;16(suppl 5):v156-v7.
  16. Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE PREDICTS IDH-1 MUTATION IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 2014;16(suppl 5):v157-v.

  17. 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.

  18. Amer A, Zinn P, Colen R. IMMEDIATE POST OPERATIVE VOLUME OF ABNORMAL FLAIR SIGNAL PREDICTS PATIENT SURVIVAL IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 2014;16(suppl 5):v138-v.
  19. Amer A, Zinn P, Colen R. IMMEDIATE POST-RESECTION PERICAVITARIAN DWI HYPERINTENSITY IN GLIOBLASTOMA PATIENTS IS PREDICTIVE OF PATIENT OUTCOME. Neuro-Oncology. 2014;16(suppl 5):v138-v9.
  20. 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)
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Collection: TCGA-KIRC 

  1. 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.
  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. http://dx.doi.org/10.18632/oncotarget.7129

 

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