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  1. Vidya, K., & Kurian, M. (2018). Novel framework for breast cancer classification for retaining computational efficiency and precise diagnosis. Communications Applied Electronics, 7(15), 1-6. Retrieved from

  2. Brassey, C. A., O'Mahoney, T. G., Chamberlain, A. T., & Sellers, W. I. (2017). A volumetric technique for fossil body mass estimation applied to Australopithecus afarensis. Journal of Human Evolution, 115, 47-64. doi:10.1016/j.jhevol.2017.07.014

  3. Webb, G. (2018). A Caussian mixture model based level set method for volume segmentation in medical images. Retrieved from

  4. Omotosho, A., Oluwatobi, A. E., Oluwaseun, O. R., Chukwuka, A. E., & Adekanmi, A. (2018). A neuro-fuzzy based system for the classification of cells as cancerous or non-cancerous. International Journal of Medical Research & Health Sciences, 7(5), 155-166. Retrieved from

  5. Russell, P., Fountain, K., Wolverton, D., & Ghosh, D. (2018). TCIA pathfinder: An R client for The Cancer Imaging Archive REST API. Cancer Research. doi:10.1158/0008-5472.CAN-18-0678

  6. Bennett, W., Smith, K., Jarosz, Q., Nolan, T., & Bosch, W. (2018). Reengineering workflow for curation of DICOM datasets. Journal of Digital Imaging, 1-9. doi:10.1007/s10278-018-0097-4

  7. Yassine, A.-A., Kingsford, W., Xu, Y., Cassidy, J., Lilge, L., & Betz, V. (2018). Automatic interstitial photodynamic therapy planning via convex optimization. Biomedical Optics Express, 9(2), 898-920. doi:10.1364/BOE.9.000898

  8. Sharma, M., Bhatt, J. S., & Joshi, M. V. (2018). Early detection of lung cancer from CT images: Nodule segmentation and classification using deep learning. Tenth International Conference on Machine Vision. 106960W. Vienna: SPIE. doi:10.1117/12.2309530
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  22. Lan, R., Zhong, S., Liu, Z., Shi, Z., & Luo, X. (2017). A simple texture feature for retrieval of medical images. Multimedia Tools and Applications.  DOI:10.1007/s11042-017-5341-2

  23. Prior, F., Smith, K., Sharma, A., Kirby, J., Tarbox, L., Clark, K., Bennett, W., Nolan, T., Freymann, J. (2017). The public cancer radiology imaging collections of The Cancer Imaging ArchiveNature Scientific Data, 4; 1-7. doi:10.1038/sdata.2017.124

  24. Kohli, M., Morrison, J. J., Wawira, J., Morgan, M. B., & Hostetter, J., Genereaux, B., Hussain, M., Langer S. G. (2017). Creation and curation of the society of imaging informatics in medicine hackathon datasetJournal of Digital Imaging, 1-4. doi:10.1007/s10278-017-0003-5

  25. Williamson, J.F., Das, S.K., Goodsitt, M.S., Deasy, J.O. (2017). Introducing the Medical Physics Dataset Article. Med. Phys. 44(2); 349-350. doi:10.1002/mp.12003
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  29. Huang, B.E., Mulyasasmita, W., Rajagopal, G. (2016). The Path from Big Data to Precision Medicine.Expert Review of Precision Medicine and Drug Development,1(2):129-143. (link)

  30. Chatellier, G., Varlet, V., Blachier-Poisson, C. (2016). "Big data" and "open data": What kind of access should researchers enjoy?Therapie. 71(1); 97-105, 107-114.(link)
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  33. Moore, S.M., Maffitt, D.R., Smith, K.E., Kirby, J.S., Clark, K.W., Freymann, J.B., Vendt, B.A., Tarbox, L.R., Prior, F.W. (2015). De-identification of Medical Images with Retention of Scientific Research Value. RadioGraphics. 35(3); 727-35. doi:10.1148/rg.2015140244.
  34. Mayo, C.S., Deasy, J.O., Chera, B.S., Freymann, J., Kirby, J.S., Hardenberg, P.H. (2016). How Can We Effect Culture Change Toward Data-Driven Medicine?International Journal of Radiation Oncology: Biology, Physics95(3); 916-21. (link)
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  1. Li, Z.-C., Bai, H., Sun, Q., Zhao, Y., Lv, Y., Zhou, J., Liang, C., Chen, Y., Liang, D., Zheng, H. (2018). Multiregional radiomics profiling from multiparametric MRI: Identifying an imaging predictor of IDH1 mutation status in glioblastoma. Cancer Medicine. DOI: 10.1002/cam4.1863 

  2. Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesseling, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. H. E., Moll, A. C., Dorsman, J., Castelijns, J., de Graff, P., de Jong, M. C. (2018). Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget, 9(28), 20134-20155. doiDOI: 10.18632/oncotarget.24893 

  3. Alessandrino, F., Shinagare, A. B., Bosse, D., Choueiri, T. K., Krajewski, K. M. (2018). Radiogenomics in renal cell carcinoma. Radiology, 270(2), 464-471. doi:10.1148/radiol.13130663 (also published in Abdominal Radiology, doiDOI: 10 10.1007/s00261-018-1624-y) 
  4. 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 NSCLCEuropean Radiology, 1-11.  Retrieved from  DOI:10.1007/s00330-017-4996-4
  5. Smits, M., & van den Bent, M. J. (2017). Imaging correlates of adult glioma genotypes. Radiology, 284(2).

  6. Lehrer, M., Bhadra, A., Ravikumar, V., Chen, J. Y., Wintermark, M., Hwang, S. N., Holder, C. A., Huang, E. P., Fevrier-Sullivan, B., Freymann, J. B., Rao, A., & TCGA Glioma Phenotype Research Group. (2017). Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma. Oncoscience, 4, 57-66. doi:10.18632/oncoscience.353

  7. Demerath, T., Simon-Gabriel, C.P., Kellner, E., Schwarzwald, R., Lange, T., Heiland, D.H., Reinacher, P., Staszewski, O., Mast, H., Kiselev, V.G., Egger, K., Urbach, H., Weyerbrock, A., Mader, I. (2017). Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiology Journal, 30(1); 36-47. doi:10.1177/1971400916678225
  8. Liu, T.T., Achrol, A.S., Mitchell, L.A., Rodriguez, S.A., Feroze, A., Iv, M., Kim, C., Chaudhary, N., Gevaert, O., Stuart, J.M., Harsh, G.R., Chang, S.D., Rubin, D.L. (2016). Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment. Neuro-Oncology, 1-11. doi:10.1093/neuonc/now270

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

  10. Song, S.E., Bae, M.S., Chang, J.M., Cho, N., Ryu, H.S., Moon, W.K. (2016). MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiologica. 58(7), 792-799.  doi:10.1177/0284185116673119

  11. McCann, S.M., Jiang, Y., Fan, X., Wang, J. Antic, T., Prior, F., VanderWeele, D., Oto, A. Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. American Journal of Roentgenology 206(3); 559-565 doi:10.2214/AJR.15.14967

  12. Katrib, A., Hsu, W., Bui, A., Xing, Y. (2016). “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment.Quantitative Biology. 1-12. doi:10.1007/s40484-016-0061-6

  13. Bai, H.X., Lee, A.M., Yang, L., Zhang, P., Davatzikos, C., Maris, J.M., Diskin, S.J. (2016). Imaging genomics in cancer research: Limitations and promises.The British Journal of Radiology, 89(1061); doi:10.1259/bjr.20151030
  14. 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. doi: 10.1118/1.4925591

  15. Tomczak, K., Czerwińska, P., Wiznerowicz, M. (2015). The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.Contemp Oncol (Pozn). 19(1A); A68-A77. doi:10.5114/wo.2014.47136

  16. Shinegare, A.B., Vikram, R., Jaffe, C., Akin, O., Kirby, J., Huang, E., Freymann, J., Sainani, N.I., Sadow, C.A., Bathala, T.K., Rubin, D.L., Oto, A., Heller, M.T., Surabhi, V.R., Katabathina, V., Silverman, S.G. (2015). Radiogenomics of clear renal cell carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.Abdominal Imaging, 40(6). 1684-1692. doi:10.1007/s00261-015-0386-z
  17. Pope, W.B. (2015). Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 25(1); 105-19. doi: 10.1016/j.nic.2014.09.006

  18. Gutman, D.A., Dunn Jr., W.D., Grossmann, P., Cooper, L.A., Holder, C.A., Ligon, K.L., Alexander, B.M., Aerts, H.J. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma.Neuroradiology, 57(12); 1227-1237doi: 10.1007/s00234-015-1576-7
  19. Feldman, M., Piazza, M.G., Edwards, N.A., Ray, Chaudhury, A., Maric, D., Merrill, M.J., Zhuang, Z., Chittiboina, P. (2015). 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target.Neurosurgery 62. (CN_suppl_1); 209-210. doi: 10.1227/01.neu.0000467099.84064.25

  20. 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. (2014). NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology. 7(5); 556-69. doi: 10.1016/j.tranon.2014.07.007.
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  22. Gevaert, O., Xu, J., Hoang, C.D., Leung, A.N., Xu, Y., Quon, A., Rubin, D.L., Napel, S., Plevritis, S.K. (2012) Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 264(2); 387-96. doi: 10.1148/radiol.12111607


  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:  REMBRANDT

  1. Sereika, M., Urbanaviciute, R., Tamasauskas, A., Skiriute, D., & Vaitkiene, P. (2018). GFAP expression is influenced by astrocytoma grade and rs2070935 polymorphism. Journal of Cancer, 9(23), 4496-4502. DOI: 10.7150/jca.26769 

  2. Schrock, M. S. (2017). Wwox deficiency in human cancers: Role in treatment resistance. Columbus, OH: The Ohio State University.  Retrieved from!etd.send_file?accession=osu1492793625915816&disposition=inline

  3. Babu, B. S., & Varadarajan, S. (2017). Detection of brain tumour in MRI scan images using Tetrolet Transform and SVM classifier. Indian Journal of Science and Technology, 10. doi:10.17485/ijst/2017/v10i19/113721

Collection: RIDER Collections

  1. Arimura, H., Soufi, M., Ninomiya, K., Kamezawa, H., & Yamada, M. (2018). Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis. Radiological Physics and Technology, 48, 27-36. DOI: 10.1007/s12194-018-0486-x

  2. Buch, K., Kuno, H., Qureshi, M. M., Li, B., & Sakai, O. (2018). Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.  Journal of Applied Clinical Medical Physics. DOI: 10.1002/acm2.12482

  3. 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
  4. 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)
  5. 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

  6. 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.
  7. 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)
  8. 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.
  9. 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  


  1. Nguyen, G. K., Mellnick, V. M., Yim, A. K.-Y., Salter, A., & Ippolito, J. E. (2018). Synergy of sex differences in visceral fat measured with CT and tumor metabolism helps predict overall survival in patients with Renal Cell Carcinoma. Radiology, 287(3), 884-892. doi:10.1148/radiol.2018171504

  2. Liu X, Swen JJ, Diekstra MHM, Boven E, Castellano D, Gelderblom H, Mathijssen RHJ, Vermeulen SH, Oosterwijk E, Junker K, Roessler M, Alexiusdottir K, Sverrisdottir A, Radu MT, Ambert V, Eisen T, Warren A, Rodriguez-Antona C, Garcia-Donas J, Bohringer S, Koudijs KKM, Kiemeney L, Rini BI, Guchelaar HJ. (2018) A genetic polymorphism in CTLA-4 is associated with overall survival in sunitinib-treated patients with clear cell metastatic renal cell carcinoma. Clin Cancer Res 2018. DOI: 10.1158/1078-0432.CCR-17-2815

    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)
  3. 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)
  4. 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: TCGA-LGG

  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-LUSC

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