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  1. Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Cancer Genome Atlas Research N, Shmulevich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep.

    2018;23(1):181-93 e7. DOI: http://doi.org/10.1016/j.celrep.2018.03.086
  2. Gutman, D.A., Cobb, J., Somanna, D., Park, Y., Wang, F., Kurc, T., Saltz, J.H., Brat, D.J., Cooper, L.A. (2013) Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. Journal of the American Medical Informatics Association. 20(6); 1091-1098. doi:10.1136/amiajnl-2012-001469 (paper)

Algorithm Development

  1. Yassine, A.-A., Lilge, L., & Betz, V. (2018). Optimizing interstitial photodynamic therapy with custom cylindrical diffusers. Journal of Biophotonics. DOI: 10.1002/jbio.201800153

  2. Men, K., Geng, H., Cheng, C., Zhong, H., Huang, M., Fan, Y., Plastaras, J. P., Lin, A., Xiao, Y. (2018). More accurate and efficient segmentation of organs-at-risk in radiotherapy with Convolutional Neural Networks Cascades. Medical Physics. DOI: 10.1002/mp.13296 

  3. Edalati-rad, A., & Mosleh, M. (2018). Improving brain tumor diagnosis using MRI segmentation based on collaboration of beta mixture model and learning automata. Arabian Journal for Science and Engineering, 1-13. DOI:10.1007/s13369-018-3320-1

  4. Taghanaki, S. A., Duggan, M., Ma, H., Hou, X., Celler, A., Benard, F., Hamarneh, G. (2017). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Computerized Medical Imaging and Graphics, 63, 53-56. DOI: 10.1016/j.compmedimag.2017.12.004

  5. Y Ren, J Ma, J Xiong, Y Chen, L Lu, J Zhao (2018) Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography. IEEE Journal of Biomedical and Health Informatics. DOI:10.1109/JBHI.2018.2808199

  6. Babu, J. S., Mathew, S., & Simon, R. (2017). Biomedical image retrieval using LBWP. International Research Journal of Engineering and Technology (IRJET), 4(9), 839-843. https://www.irjet.net/archives/V4/i9/IRJET-V4I9147.pdf

  7. Hostetter, J. M., Morrison, J. J., Morris, M., Jeudy, J., Wang, K. C., & Siegel, E. (2017). Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset. Journal of the American Medical Informatics Association, 24(6), 1046-1051. DOI:10.1093/jamia/ocx012

  8. Mason J, Perelli A, Nailon W, Davies M. (2017) Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT? arXiv 1703.07179

  9. 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)

  10. 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. DOI: 10.1016/j.cmpb.2016.10.021

  11. 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)

  12. 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)

  13. 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)

  14. 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.
  15. 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.
  16. 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.
  17. Benninghoff H, Garcke H. Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes. arXiv:1506.07136. 2015.
  18. 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.
  19. Guvenis A, Koc A. OPTIMISING DELINEATION ACCURACY OF TUMOURS IN PET FOR RADIOTHERAPY PLANNING USING BLIND DECONVOLUTION. Radiation Protection Dosimetry. 2015:ncv110.
  20. 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).
  21. 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.
  22. 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.
  23. Blessy SPS, Sulochana CH. Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation. Technology and Health Care. 2014.
  24. 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.
  25. Hong S, Huang Y, Cao Y, Chen X, Han J-DJ. Approaches to uncovering cancer diagnostic and prognostic molecular signatures. Molecular & Cellular Oncology. 2014.
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. 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)

  33. 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.

  34. 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)

  35. 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.

  36. 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)
  37. 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)
  38. Codella N, Merler M. IBM TJ Watson Research Center. Semantic Model Vector for ImageCLEF2013. June 18, 2014. (link)
  39. 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)

  40. Agostinelli F, Anderson MR, Lee H, editors. Robust Image Denoising with Multi-Column Deep Neural Networks. Advances in Neural Information Processing Systems; 2013.

  41. 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
  42. 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)
  43. 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
  44. 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)
  45. 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)
  46. 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)
  47. 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)
  48. 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
  49. 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) .
  50. 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)
  51. 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)
  52. 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)
  53. 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)
  54. 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.
  55. 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)
  56. 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
  57. 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

  58. 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.

  59. 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.

  60. 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.

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

  62. Chaddad, A. and C. Tanougast "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015. DOI: 10.1155/2015/728164 
  63. 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

...

  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. Fedorov A, Schwier M, Clunie D, Herz C, Pieper S, Kikinis R, Tempany C, Fennessy F. (2018) An annotated test-retest collection of prostate multiparametric MRI Scientific Data 5:180281.( link )

  6. Gevaert O, Mitchell LA, et al. (2014). Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. TCIA. Saint Louis, MO. (link)

  7. 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)
  8. 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)

  9. 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)

  10. 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. 

  11. 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)

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

  13. 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)
  14. 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)  
  15. 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)

  16. 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)

  17. Morris E, Burnside M, et al. (2014). TCGA Breast Phenotype Research Group Data sets. TCIA. Saint Louis, MO (link)

  18. Roth H, Lu L, et al. (2015). A new 2.5D representation for lymph node detection in CT. TCIA. Saint Louis, MO. (link)

  19. 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)

  20. 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. Gruselius, H. (2018). Generative models and feature extraction on patient images and structure data in radiation therapy. Retrieved from http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1215620&dswid=2429

  2. Scarpelli, M., Eickhoff, J., Cuna, E., Perlman, S., & Jeraj, R. (2018). Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values. Physics in Medicine & Biology, 63(3), 035021. DOI: 10.1088/1361-6560/aaa175

  3. Ryalat MH, Laycock S, Fisher M, editors. Automatic Removal of Mechanical Fixations from CT Imagery with Particle Swarm Optimisation. International Conference on Bioinformatics and Biomedical Engineering; 2017: Springer. DOI: 10.1007/978-3-319-56148-6_37

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  2. Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via

  3. graph‐cuts incorporating shape prior and motion from 4D CT. Medical physics. 2018;45(1):297-306. doi: 10.1002/mp.12690.

  4. Agnes, S. A., Anitha, J., & Peter, J. D. (2018). Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications. DOI:  10.1007/s00521 Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications. DOI:  10.1007/s00521-018-3877-3 

  5. Kohl, S. A., Romera-Paredes, B., Meyer, C., De Fauw, J., Ledsam, J. R., Maier-Hein, K. H., Eslami, S., Rezende, D. J., Ronneberger, O. (2018). A probabilistic U-Net for segmentation of ambiguous images. Retrieved from https://arxiv.org/pdf/1806.05034.pdf

  6. Kang, G., Liu, K., Hou, B., & Zhang, N. (2017). 3D multi-view convolutional neural networks for lung nodule classification. (Y. Deng, Ed.) PLOS One, 12(11).  DOI: 10.1371/journal.pone.0188290 
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  8. Farag, A. A., Ali, A., Elshazly, S., & Farag, A. A. (2017). Feature fusion for lung nodule classification. International Journal of Computer Assisted Radiology and Surgery, 1-10. DOI:10.1007/s11548-017-1626-1

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Info
titleThese 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.

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  1. .MRI.Ultrasound.Summary.pdf).
  2. 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. L Yang, J Yang, X Zhou, L Huang, W Zhao, T Wang, J Zhuang, J Tian. (2018) Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. European Radiology, 2018 DOI:  10.1007/s00330-018-5770-y
  2. Lee, J., Cui, Y., Sun, X., Li, B., Wu, J., Li, D., Gensheimer, M. F., Loo Jr., B. W., Diehn, M., Li, R. (2017). Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLC. European Radiology, 1-11. DOI:  10.1007s00330-017-4996-4 

  3. Soufi M, Arimura H, Nakamoto T, Hirose T-A, Ohga S, Umezu Y, Honda H, Sasaki T. (2018). Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images. Physica Medica, 46:32-44. DOI: 10.1016/j.ejmp.2017.11.037

  4. 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)

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  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)
  4. SPIE-AAPM-NCI PROSTATEx Challenges

Collection: TCGA-BRCA

  1. Lehrer, M., Bhadra, A., Aithala, S., Ravikumar, V., Zheng, Y., Dogan, B., Bonaccio, E., Burnside, E. S., Morris, E., Sutton, E., Whitman, G. J., Net, J., Brandt, K., Ganott, M., Zuley, M., Rao, A., & TCGA Breast Phenotype Research Group. (2018). High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma. Oncoscience, 5(1-2), 39-48. (link)

  2. Al-Dabagh MZ, AL-Mukhtar FH. Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine. IJAERS. 2017;4(3):258-63. DOI: 10.22161/ijaers.4.3.41

  3. 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
  4. 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 
  5. 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

  6. 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)

  7. 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)

  8. 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.

  9. 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.

  10. 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.

  11. Kim, G. R., Ku, Y. J., Cho, S. G., Kim, S. J., & Min, B. S. (2017). Associations between gene expression profiles of invasive breast cancer and breast imaging reporting and data system MRI lexicon. Annals of Surgical Treatment and Research, 93(1), 18-26. DOI: 10.4174/astr.2017.93.1.18

     

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  1. Halani, S. H., Yousefi, S.; Vega, J. V.; Rossi, M. R.; Zhao, Z.; Amrollahi, F.; Holder, C. A.; Baxter-Stoltzfus, A.; Eschbacher, J.; Griffith, B.; Olson, J. J.; Jiang, T.; Yates, J. R.; Eberhart, C. G.; Poisson, L. M.; Cooper, L. A. D.; Brat, D. J. (2018). Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. Precision Oncology. DOI: 10.1038/s41698-018-0067-9 

  2. Liu, Z., Zhang, T., Jiang, H., Xu, W., & Zhang, J. (2018). Conventional MR-based preoperative nomograms for prediction of IDH/1p19q subtype in low-grade glioma. Academic Radiology. DOI: 10.1016/j.acra.2018.09.022 

Collection: TCGA-LUAD

  1. Dara S, Tumma P, Eluri N, Kancharla G. Feature Extraction In Medical Images by Using Deep Learning Approach. International Journal of Pure and Applied Mathematics. 2018;120(6):305-12.

  2. Pathak, Y., Arya, K. V., & Tiwari, S. (2018). An efficient low-dose CT reconstruction technique using partial derivatives based guided image filter. Multimedia Tools and Applications, 1-20. DOI: 10.1007/s11042-018-6840-5 

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