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  1. Gillmann, C., Arbelaez, P., Penaloza, J. T., Hagen, H., & Wischgoll, T. (2017). Intuitive error space exploration of medical image data in clinical daily routine. Eurographics Conference on Visualization (EuroVis) 2017. doi: 10.2312/eurovisshort.20171148
  2. Jinu, J., Rajesh, K. R., Pournami, S. C., & Vidya, P. (2017). Interactive 3D Virtual Colonoscopic Navigation For Polyp Detection From CT Images. Procedia Computer Science, 115, 407-414. doi: 10.1016/j.procs.2017.09.099
  3. Ghosh, D., & Bandyopadhyay, S. K. (2017). Brain tumor detection from MRI image: An approach. International Journal of Applied Research, 3(6), 1152-1159.  Retrieved from https://pdfs.semanticscholar.org/1916/f00997b627213b46c874a9a133ee8b6fa92e.pdf
  4. Vallières, M., Laberge, S., Diamant, A., & El Naqa, I. (2017). Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Physics in Medicine and Biology, 62(22), 8536-8565.
  5. Mitra, S., Banerjee, S., & Hayashi, Y. (2017). Volumetric brain tumour detection from MRI using visual saliency. (J. Najbauer, Ed.) PLOS One, 12(11). http://dx.doi.org/10.1371/journal.pone.0187209

  6. Gueziri, H.-E. (2017). User-centered design and evaluation of interactive segmentation methods for medical images. Montreal: École de technologie supérieure du Quebec.  Retrieved from http://espace.etsmtl.ca/1959/2/GUEZIRI_Houssem-Eddine-web.pdf

  7. 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

  8. 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 Archive. Nature Scientific Data, 4; 1-7. doi:10.1038/sdata.2017.124

  9. 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 dataset. Journal of Digital Imaging, 1-4. doi:10.1007/s10278-017-0003-5

  10. 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
  11. Nida, N; Khan, M. (2016). Efficient Colorization of Medical Imaging based on Colour Transfer Method. U.G. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences, 53(4); 253-261. (link)
  12. Kalpathy-Cramer, J., Zhao, B., Goldgof, D., Gu, Y., Wang, X., Yang, H., Tan, Y., Gillies, R., Napel, S. (2016). A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging29(4):476-487. DOI: 10.1007/s10278-016-9859-z

  13. Parks, C.L., Monson, K.L. (2016). Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications. Journal of Digital Imaging. 1-11. DOI: 10.1007/s10278-016-9932-7

  14. 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)

  15. 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)
  16. Benedict, S.H., Hoffman K., Martel, M.K., Abernethy, A.P., Asher, A.L., Capala, J., Chen, R.C., Chera, B., Couch, J., Deye, J., Efstathiou, J.A., Ford, E., Fraass, B.A., Gabriel, P.E., Huser, V., Kavanagh, B.D., Khuntia, D., Marks, L.B., Mayo, C., McNutt, T., Miller, R.S., Moore, K.L., Prior, F., Roelofs, E., Rosenstein, B.S., Sloan, J., Theriault, A., Vikram, B. (2016). Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data.International Journal of Radiation Oncology: Biology, Physics. 95(3):873-879 (link)
  17. Toga, A.W., Dinov, I.D. (2015). Sharing big biomedical data. Journal of Big Data. 2(1); 1-12. (link)
  18. 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.
  19. 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, Physics. 95(3); 916-21. (link)
  20. Kirby, J., Tarbox, L., Freymann, J., Jaffe, C., Prior, F. (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
  21. GIllies, R.J., Kinahan, P.E., Hricak, H., (2016). Radiomics: Images Are More than Pictures, They Are Data.Radiology, 278(2); 563-77. (link)
  22. Fedorov, A., Clunie, D., Ulrich, E., Bauer, C., Wahle, A., Brown, B., Onken, M., Riesmeier, J., Pieper, S., Kikinis, R., Buatti, J., Beichel, R.R. (2016). DICOM for quantitative imaging biomarker development: A standards based approach to sharing of clinical data and structured PET/CT analysis results in head and neck cancer research.PeerJ, 4(e2057). (link)
  23. Commean, P.K., Rathmell, J.M., Clark, K.W., Maffitt, D.R., Prior, F.W. (2015). A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial. Journal of Digital Imaging. 1-9. (paper)
  24. Bourne, P.E. (2015). DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 275(1); 3-4. link. 
  25. Armato, S.G., Hadjiiski, L., Tourassi, G.D., Drukker, K., Giger, M.L., Li, F., Redmond, G., Farahani, K., Kirby, J.S., Clarke, L.P. (2015). Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Journal of Medical Imaging. 2(2); 10.1117/1.JMI.2.2.020103
  26. Herskovits, E.H. (2014). Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 124; 1-30. (link)
  27. Gutman, D.A., Dunn Jr., W.D., Cobb, J., Stoner, R.M., Kalpathy-Cramer, J., Erickson, B. (2014) Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer. Frontiers in Neuroinformatics. 8. (paper)
  28. Erickson, B.J., Fajnwaks, P., Langer, S.G., and Perry, J. (2014) Multisite Image Data Collection and Management Using the RSNA Image Sharing Network., Translational oncology, 7(1); 36-39. (paper)
  29. Prior, F.W., Clark, K., Commean, P., Freymann, J., Jaffe, C., Kirby, J., Moore, S., Smith, K., Tarbox, L., Vendt, B. (2013) TCIA: an information resource to enable open science. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE.(paper)
  30. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, 26(6), 1045-1057. (paper)
  31. Villani, L., and Prati, R.C. (2012). Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário.Congresso Brasileiro de Informática em Saúde, Citado na. (paper)
  32. Mongkolwat, P., Channin, D.S., Kleper, V., Rubin, D.L. (2012). Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder. Radiographics, 32(4); 1223-32. (paper).
  33. Jaffe, C.C. (2012). Imaging and Genomics: Is There a Synergy?Radiology. 264(2); 329-31.(paper).

  34. Freymann, J.B., Kirby, J.S., Perry, J.H., Clunie, D.A., Jaffe, C.C. (2012). Image data sharing for biomedical research—meeting HIPAA requirements for de-identification. Journal of Digital Imaging, 25(1). 14-24. (paper)

Radiogenomics

  1. 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
  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.  Retrieved from https://link.springer.com/article/10.1007/s00330-017-4996-4.  DOI: 10.1007/s00330-017-4996-4

  3. Smits, M., & van den Bent, M. J. (2017). Imaging correlates of adult glioma genotypes. Radiology, 284(2). http://dx.doi.org/10.1148/radiol.2017151930

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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); 1227-1237. doi: 10.1007/s00234-015-1576-7

  17. 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

  18. 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.
  19. Rao A. (2013).  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), doi: 10.1109/GlobalSIP.2013.6736815

  20. 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

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Collection: QIN GBM DCE-MRI

  1. Beers, A., Chang, K., Brown, J., Zhu, X., Sengupta, D., Willke, T. L., Gerstner, E., Rosen, B., Kalpathy-Cramer, J. (2018). Anatomical DCE-MRI phantoms generated from glioma patient data. SPIE Medical Imaging. 105732(V). Houston: SPIE. doi:10.1117/12.2294961
  2. Gerstner ER, Zhang Z, Fink JR, Muzi M, Hanna L, Greco E, Mintz A, Kostakoglu L, Eikman EA, Prah MA, Ellingson BM, Ratai EM, Schmainda KM, Sorensen G, Barboriak DP,  Mankoff DA. ACRIN 6684: Assessment of tumor hypoxia in newly diagnosed GBM using 18F-FMISO PET and MRI. Clin Cancer Res. 2016 Oct 15;22(20):5079-5086. DOI:10.1158/1078-0432.CCR-15-2529
  3. Gerstner ER, Zhang Z, Fink JR, Muzi M, Hanna L, Greco E, Mintz A, Kostakoglu L, Eikman EA, Prah M, Schmainda KM, Sorensen GA, Barboriak D,  Mankoff DA. ACRIN 6684: Assessment of tumor hypoxia in newly diagnosed GBM using 18F-FMISO PET and MRI. J Clin Oncol 33(Suppl):2024. 2015.
  4. Fink JR, Zhang Z, Gerstner ER, Muzi M, Kostakoglu L, Mintz A, Eikman EA, Barboriak D,  Mankoff DA. ACRIN 6684: Multicenter phase II assessment of tumor hypoxia in glioblastoma using 18F-Fluoromisonidazole (FMISO) PET and MRI. J Nucl Med 56(Suppl3):325. 2015.
  5. Fink JR, Muzi M, Peck M,  Krohn KA. Multimodality Brain Tumor Imaging: MR Imaging, PET, and PET/MR Imaging. J Nucl Med 56(10):1554-1561. 2015.
  6. Muzi M, Fink JR, Richards TL, Marro KI, Wong T, Muzi JP, Eary JF, Rockhill JK,  Krohn KA. Evaluation of PET and MR measurements to examine progression in glioma patients. J Nucl Med 55(Suppl1):1512-. 2014.

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