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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 https://www.caeaccess.org/archives/volume7/number15/vidya-2018-cae-652760.pdf

  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 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1217463&dswid=2429

  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 http://www.ijmrhs.com/medical-research/a-neurofuzzy-based-system-for-the-classification-of-cells-as-cancerous-or-noncancerous.pdf

  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
  9. Saad, M., & Choi, T.-S. (2018). Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Computerized Medical Imaging and Graphics, 67, 1-8. doi:10.1016/j.compmedimag.2018.04.003
  10. Nishio, M., Nishizawa, M., Sugiyama, O., Kojima, R., Yakami, M., Kuroda, T., Togashi, K. (2018). Computer aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. Plos One, 13(4). doi:10.1371/journal.pone.0195875
  11. Jenuwine, N. M., Mahesh, S. N., Furst, J. D., & Raicu, D. S. (2018). Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection. Medical Imaging 2018. 1057539. Houston: SPIE. doi:10.1117/12.2293918
  12. Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., Gurusamy, K., Davidson, B., Pereira, S. P., Clarkson, M. J., Barratt, D. C. (2018). Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transaction on Medical Imaging. doi:10.1109/TMI.2018.2806309
  13. Edwards, S., Brown, S., & Lee, M. (2018). Automated 3-D tissue segmentation via clustering. Journal of Biomedical Engineering and Medical Imaging, 5(2). doi:10.14738/jbemi.52.4204
  14. Chacko, L. J., Schmidbauer, D. T., Handschuh, S., Reka, A., Fritscher, K. D., Raudaschl, P., Saba, R., Handler, M., Schier, P. P., Baumgarten, D., Fischer, N., Pechriggl, E. J., Brenner, E., Hoermann, R., Glueckert, R., Schrott-Fischer, A. (2018). Analysis of vestibular labyrinthine geometry and variation in the human temporal bone. Frontiers in Neuroscience, 12. doi:10.3389/fnins.2018.00107
  15. Causey, J., Zhang, J., Ma, S., Jiang, B., Qualls, J., Politte, D. G., Prior, F., Zhang, S., Huang, X. (2018). Highly accurate model for prediciton of lung nodule malignancy with CT scans. Retrieved from https://arxiv.org/ftp/arxiv/papers/1802/1802.01756.pdf
  16. 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
  17. 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
  18. 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
  19. 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.
  20. 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

  21. 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

  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 Archive. Nature 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 dataset. Journal 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
  26. 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)
  27. 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

  28. 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

  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)
  31. 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)
  32. Toga, A.W., Dinov, I.D. (2015). Sharing big biomedical data. Journal of Big Data. 2(1); 1-12. (link)
  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, Physics. 95(3); 916-21. (link)
  35. 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
  36. GIllies, R.J., Kinahan, P.E., Hricak, H., (2016). Radiomics: Images Are More than Pictures, They Are Data.Radiology, 278(2); 563-77. (link)
  37. 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)
  38. 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)
  39. Bourne, P.E. (2015). DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 275(1); 3-4. link. 
  40. 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); doi:10.1117/1.JMI.2.2.020103
  41. Herskovits, E.H. (2014). Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 124; 1-30. (link)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. 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)
  47. 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).
  48. Jaffe, C.C. (2012). Imaging and Genomics: Is There a Synergy?Radiology. 264(2); 329-31.(paper).

  49. 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)

...

  1. 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. doi:10.18632/oncotarget.24893

  2. 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, doi: 10.1007/s00261-018-1624-y)
  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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.
  20. 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

  21. 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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