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TCIA General

  1. Li, H., & Mueller, K. (2017). Low-dose CT streak artifacts removal using deep residual neural network. Fully 3D Image Reconstruction in Radiology and Nuclear Medicine. Xi'an: Stony Brook University. (link)

  2. 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(link)

  3. 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

  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). 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. (10.1016/B978-0-12-411638-2.00001-X)
  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. (10.1007/s10278-013-9622-7)
  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. (10.1148/rg.324115080).
  48. Jaffe, C.C. (2012). Imaging and Genomics: Is There a Synergy?Radiology. 264(2); 329-31.(10.1148/radiol.12120871).

  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. (PMC3264712)

...

  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. DOI: 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, DOI: 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 NSCLC. European Radiology, 1-11.    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). DOI: 10.1148/radiol.2017151930

  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); 1227-1237. DOI: 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
  21. 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

  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

Radiomics

  1. Lojzim, J. M., & Fries, M. (2017, August). Brain tumor Segmentation using morphological processing and the discrete wavelet transform. Journal of Young Investigators, 33(2), 55-62. DOI: 10.22186/jyi.33.3.55-62

  2. Chaddad, A., Sabri, S., Niazi, T., & Abdulkarim, B. (2018). Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Medical & Biological Engineering & Computing, 1-14. doi:10.1007/s11517-018-1858-4

  3. Drukker, K., Li, H., Antropova, N., Edwards, A., Papaioannou, J., & Giger, M. L. (2018). Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival "early on" in neoadjuvant treatment of breast cancer. Cancer Imaging, 18(1). DOI:10.1186/s40644-018-0145-9

  4. Reeves, A. P., Xie, Y., & Liu, S. (2018). Automated image quality assessment for chest CT scans. Medical Physics, 45(2), 561-578. DOI: 10.1002/mp.12729

  5. AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2018). Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Medical Physics. DOI: 10.1002/mp.12752

  6. Larue, R. T. H. M., Van De Voorde, L., van Timmeren, J. E., Leijenaar, Ralph T. H., Berbee, M., Sosef, M. N., Schreurs, W. M. J., van Elmpt, W., Lambin, P. (2017). 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers. Radiotherapy and Oncology. DOI: 10.1016/j.radonc.2017.07.023

  7. Sutton, E. J., Huang, E. P., Drukker, K., Burnside, E. S., Li, H., Net, J. M., Rao, A., Whitman, G. J., Zuley, M., Ganott, M., Bonaccio, E., Giger, M. L., Morris, E. A. (2017). Breast MRI radiomics: Comparison of computer- and human-extracted imaging phenotypes. European Radiology Experimental. DOI: 10.1186/s41747-017-0025-2

  8. Vani, N., Swomya, A., & Jayamma, N. (2017). MRI Brain tumor classification using support vector machine. International Research Journal of Engineering and Technology, 1724-1729. DOI: 10.1109/SCEECS.2014.6804439

  9. Beichel, R.R., Smith, B.J., Bauer, C., Ulrich, E.J., Ahmadvand, P., Budzevich, M.M., Gillies, R.J., Goldgof, D., Grkovski, M., Hamarneh, G., Huang, Q., Kinahan, P.E., Laymon, C.M., Mountz, J.M., Muzi, J.P., Muzi, M., Nehmeh, S., Oborski, M.J., Tan, Y., Zhao, B., Sunderland, J.J., Buatti, J.M. (2017). Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med. Phys. 44(2); 479-496. DOI: 10.1002/mp.12041
  10. Vallières, M., Kay-Rivest, E., Perrin, L.J., Liem, X., Furstoss, C., Aerts, H.J.W.L., Khaouam, N., Nguyen-Tan, P.F., Want, C.-S., Sultanem, K., Seuntjens, J., Naqa, I.E. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Scientific Reports, (arXiv 1703.08516)
  11. Paredes, D., Saha, A., Mazurowski, M.A.(2017). Deep learning for segmentation of brain tumors: can we train with images from different institutions?SPIE Medical Imaging: Computer-Aided Diagnosis, DOI: 10.1117/12.2255696
  12. Kumar, S., Dharun. (2017). Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research, 28(5) 
  13. Nabizadeh N, Kubat M. Automatic Tumor Segmentation in Single-Spectral MRI Using A Texture-Based and Contour-Based Algorithm. ScienceDirect, 77: 1-10. DOI: 10.1016/j.eswa.2017.01.036

  14. Kaur, T., Saini, B.S., Gupta, S. (2016). A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images. Neural Computing and Applications. 1-24. DOI: 10.1007/s00521-016-2751-4

  15. Song, J., Liu, Z., Zhong, W., Huang, Y., Ma, Z., Dong, D., Liang, C., Tian, J. (2016). Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis. Scientific Reports. 6:38282:1-9. DOI: 10.1038/srep38282

  16. Crawford, L., Monod, A., Chen, A.X., Mukherjee, S., Rabadán, R. (2016). Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme. arXiv preprint arXiv:161106818. 

  17. Korfiatis, P., Kline, T.L., Erickson, B.J. (2016). Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning. J Tomography, 2(4) 334-340 DOI: 10.18383/j.tom.2016.00166

  18. Zheng, C., Wang, X., Feng, D. (Eds.). (2016). Topology guided demons registration with local rigidity preservation. 2016 IEEE 38th Annual International Conference Engineering in Medicine and Biology Society (EMBC). IEEE. DOI: 10.1109/EMBC.2016.7590913

  19. Kotrotsou, A., Zinn, P.O., Colen, R.R. (2016). Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America. 24(4); 719-29. DOI: 10.1016/j.mric.2016.06.006

  20. Zhao, B., Tan, Y., Tsai, W.Y., Qi, J., Xie, C., Lu, L., Schwartz, L.H. (2016). Reproducibility of radiomics for deciphering tumor phenotype with imaging. Scientific Reports. 6:23428. DOI: 10.1038/srep23428
  21. Li, H., Zhu, Y., Burnside, E.S., Huang, E., Drukker, K., Hoadley, K.A., Fan, C., Conzen, S.D., Zuley, M., Net, J.M., Sutton, E., Whitman, G.J., Morris, E., Perou, C.M., Ji, Y., Giger, M.L. (2016). Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. npj Breast Cancer.DOI: 10.1038/npjbcancer.2016.12
  22. Grossmann, P., Gutman, D.A., Dunn Jr., W.D., Holder, C.A., Aerts, H.J.W.L. (2016). Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma. BMC Cancer. 16(611). DOI: 10.1186/s12885-016-2659-5
  23. Zhu, Y., Li, H., Guo, W., Drukker, K., Lian, L., Giger, M.L., Ji, Y. (2015). Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Scientific Reports. 5(17787). DOI: 10.1038/srep17787 
  24. Rajakumar, K., Muttan, S., Deepa, G., Revathy, S., Priya, B.S. (2015). Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF. Advances in Natural and Applied Sciences. 9(6 SE) 506-513.DOI: (link)

  25. Parmar, C., Leijenaar, R.T.H., Grossmann, P., Valazquez, E.R., Bussink, J., Rietveld, D., Rietbergen, M.M., Haibe-Kains, B., Lambin, P., Aerts, H.J.W.L. (2015). Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer.Scientific Reports. 5(11044) DOI: 10.1038/srep11044

  26.  Parmar, C., Grossmann, P., Bussink, J., Lambin, P., Aerts, H.J.W.L. (2015). Machine Learning methods for Quantitative Radiomic Biomarkers. Scientific Reports, 5(13087). DOI: 10.1038/srep13087 
  27. Chaddad, A., Tanougast, C. (2015), High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics, 15(728164). DOI: 10.1155/2015/728164
  28. Chaddad, A. (2015). Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.  International Journal of Biomedical Imaging, 2015(868031). DOI: 10.1155/2015/868031
  29. Dhara, A.K., Mukhopadhyay, S., Khandelwal, N. (2013). 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. Medical Imaging 2013: Computer-Aided Diagnosis, 8670. DOI: 10.1117/12.2007016

  30. Dhara, A.K., Mukhopadhyay, S., Alam, N., Khandelwal, N. (2013). Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Medical Imaging 2013: Computer-Aided Diagnosis, 8670. DOI: 10.1117/12.2006970

...

  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. Golan, RJonathan Hugh Mason.(2018) Quantitative Cone-Beam Computed Tomography Reconstruction for Radiotherapy Planning . University of Edinburgh. (link to thesis)

  2. Golan, R. (2018). DeepCADe: . DeepCADe: A deep learning architecture for the detection of lung nodules in CT scans. (link to thesis)

  3. Großmann, P. B. H. J. (2018) Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers. Datawyse / Universitaire Pers Maastricht 2018. DOI: 10.26481/dis.20180308pg (link to thesis)

  4. Webb, G. (2018). A Gaussian mixture model based level set method for volume segmentation in medical images. Linköping, Sweden. (link to thesis)

  5. Androutsou, T. Clinical Decision Support System for Lung Cancer Diagnosis by analysis of thoracic CT images.  Carrier NTUA, Department of Electrical and Computer Engineering 2017. (link to thesis)

  6. Emirzade, Erkan.  A COMPUTER AIDED DIAGNOSIS SYSTEM FOR  LUNG CANCER DETECTION USING SVM. The Graduate School Of Applied Sciences Of Near East University, 2016. (link to thesis)
  7. Yu, Zexi. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. University of Saskatchewan 2016. (link to thesis)

  8. 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)

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

  10. Hunter L. Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction. Thesis, University of Texas; 2013.  (link to thesis)

  11. Karnayana PM. Radiogenomic correlation for prognosis in patients with glioblastoma multiformae. San Diego State University; 2013. (link to thesis)

  12. 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)

  13. 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.(link to thesis)

...

  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

Collection: LIDC-IDRI

  1. Mackie, T. R., Jackson, E. F., & Giger, M. (2018). Opportunities and challenges to utilization of quantitative imaging: Report of the AAPM practical big data workshop. Medical Physics. DOI: 10.1002/mp.13135
  2. Sumathipala, Y., Shafiq, M., Bongen, E., Brinton, C., & Paik, D. (2018). Machine learning to predict lung nodule biopsy method using CT image features: A pilot study. Computerized Medical Imaging and Graphics. doi: 10.1016/j.compmedimag.2018.10.006
  3. Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via

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

  5. 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/s00521-018-3877-3 

  6. 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

  7. 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 
  8. Liu, F., Feng, J., Su, W., Lv, Z., Xiao, F., & Qiu, S. (2017). Normalized euclidean super-pixels for medical image segmentation. International Conference on Intelligent Computing (pp. 586-597). Springer. 10.1007/978-3-319-63315-2_51

  9. 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

  10. MC Hancock, JF Magnan. Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset. Proc. SPIE Medical Imaging: Computer-Aided Diagnosis (2017). International Society for Optics and Photonics. DOI: 10.1117/12.2254446
  11. Wang, D; Fong, S; Wong, RK.; Mohammed, S; Fiaidhi, J; Wong, KKL. Robust High-dimensional Bioinformatics Data Streams Mining by ODR-ioVFDT. Scientific Reports 7, article number 43167 DOI: 10.1038/srep43167

  12. Mhetre RR, Sache RG. Detection of Lung Cancer Nodule on CT scan Images by using Region Growing Method. International Journal of Current Trends in Engineering & Research. 2016;2(7):215-9. (link)

  13. Setio AAA, Traverso A, de Bel T, Berens MS, Bogaard Cvd, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B. Validation, comparison, and combination of algorithms for automaticdetection of pulmonary nodules in computed tomography images: the LUNA16 challenge. arXiv preprint arXiv:161208012. 2016:1-16.

  14. Firmino M, Angelo G, et al. Computer-aided Detection (CADe) and Diagnosis (CADx) System for Lung Cancer with Likelihood of Malignancy Biomed Eng Online (2016) 15(1):2 (link)
  15. Deep G, Kaur L, et al. Directional Local Ternary Quantized Extrema Pattern: A new descriptor for Biomedical Image Indexing and Retrieval Eng Sci and Tech, an International Journal (2016) (link)
  16. Wang W, Luo J, Yang X, Lin H. Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Academic Radiology. 2015.

  17. 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).

  18. 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.
  19. 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)

  20. Magdy, E., N. Zayed, et al. Automatic Classification of Normal and Cancer Lung CT Images using Multi-scale AM-FM Features. Intl Journal of Biomedical Imaging, 2015. (link)

  21. Lassen BC, Jacobs C, et al. Robust Semi-automatic Segmentation of Pulmonary Subsolid Nodules in Chest Computed Tomography Scans. Phys Med Biol (2015) 60(3):1307-1323. (link)

  22. Kumar, D., M. J. Shafiee, et al. Discovery Radiomics for Computed Tomography Cancer Detection. arXiv e-print, 2015. (arXiv link)

  23. 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.

  24. 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  IEEE J Biomed Health Inform (2014) 19(5):1734:1746 (pubmed link)

  25. 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)
  26. 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)
  27. 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)
  28. 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)
  29. Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
  30. 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)

  31. Raicu DS, Varutbangkul E, Furst JD, Armato SG III: Modeling semantics from image data: Opportunities from LIDC. International Journal of Biomedical Engineering and Technology 3: 83–113, 2010.

  32. Zinovev D, Duo Y, Raicu DS, Furst JD, Armato SG III: Consensus versus disagreement in imaging research: A case study using the LIDC Database. Journal of Digital Imaging 25: 423–436, 2012.

...

Collection: Soft-tissue-Sarcoma

  1. Lee, I., Im, H.-J., Solaiyappan, M., & Cho, S. Y. (2017). Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma. EJNMMI Physics, 4(22), 1-10. DOI 10.1186/s40658-017-0189-0
  2. Hermessi, H., Mourali, O., & Zagrouba, E. (2019). Deep feature learning for soft tissue sarcoma classification in MR images via transfer learning. Expert Systems with Applications, 120, 116-127. DOI: 10.1016/j.eswa.2018.11.025 

...

  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)

Collection: SPIE-AAPM-NCI PROSTATEx Challenges

  1. A Chaddad, T Niazi, S Probst, F Bladou, M Anidjar, B Bahoric. (2018) Predicting Gleason Score of Prostate Cancer Patients using Radiomic Analysis. Frontiers in Oncology. DOI: 10.3389/fonc.2018.00630

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

     

...

  1. Han, L., & Kamdar, M. R. (2018). MRI to MGMT: Predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pacific Symposium on Biocomputing, 23, 331-342. (link)

  2. ParthaSarathi, M., & Ansari, M. A. (2017). Multimodal retrieval framework for brain volumes in 3D MR volumes. Journal of Medical and Biological Engineering, 1-12. DOI:10.1007/s40846-017-0287-4

  3. Liu, Y., Xu, X., Yin, L., Zhang, X., Li, L., & Lu, H. (2017). Relationship between glioblastoma heterogeneity and survival time: An MR imaging texture analysis. American Journal of Neuroradiology, 1-7. DOI:10.3174/ajnr.A5279.

  4. Beig N, Patel J, Prasanna P, et al. Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in Glioblastoma. SPIE Medical Imaging; 2017; 10134:1-10. International Society for Optics and Photonics. DOI:10.1117/12.2255694

  5. Lee, J.K., Wang, J., Sa, J.K., et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nature Genetics.(2017) DOI: 10.1038/ng.3806

  6. Cui Y, Ren S, Tha KK, Wu J, Shirato H, Li R. Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. European Radiology. 2017:1-10. (link)

  7. Kanas VG, Zacharaki EI, Thomas GA, Zinn PO, Megalooikonomou V, Colen RR. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Computer Methods and Programs in Biomedicine. 2017;140:249-57.(link)

  8. Czarnek N, Clark K, Peters KB, Mazurowski MA. Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study. Journal of Neuro-Oncology. 2017:1-8. (link)

  9. Chaddad A, Desrosiers C, Toews M, editors. Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016.

  10. Prasanna, P., Patel, J., Partovi, S. et al. Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.  Eur Radiol (2016) pp 1–10. DOI:10.1007/s00330-016-4637-3

  11. Mulvey M, Muhyadeen S,  Sinha U. Classification of Glioblastoma Multiforme Molecular Subtypes Using Three-Dimensional Multi-Modal MR Imaging Features. Med. Phys. 43, 3373 (2016); (link)

  12. Ren X, Cui Y, Gao H,  Li, R. Identifying High-Risk Tumor Volume Based On Multi-Region and Integrated Analysis of Multi-Parametric MR Images for Prognostication of Glioblastoma. Med. Phys. 43, 3751 (2016); (link)
  13. Dunn WD Jr,  Aerts HJWL, et al.  Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma.   J   Neuroimaging Psychiatry Neurol 2016. 1(2): 64-72.
  14. Upadhaya T, Morvan Y, et al. 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); (link)

  15. Upadhaya T, Morvan Y, et al. 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.

  16. Upadhaya T, Morvan Y, et al. 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.

  17. 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.

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

  19. 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)

  20. 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.

  21. 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.

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

  23. 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)
  24. 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)
  25. 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. 120(3):483–488 DOI: 10.1007/s11060-014-1580-5 (link)
  26. 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)
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  29. Wassal E, Zinn P, Colen R. Diffusion and conventional and MR imaging genomic biomarker signature predicts IDH-1 mutation in glioblastoma patients. Neuro-Oncology. 2014;16(suppl 5):v157-v.

  30. 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.

  31. 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.
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  33. 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)
  34. Jain R, Poisson L, Narang J, Gutman D, Scarpace L, Hwang SN, Holder C, Wintermark M, Colen RR, Kirby J, Freymann J, Brat DJ, Jaffe C, Mikkelsen T. Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology, 2013 Apr:267(1):212 –220, DOI:10.1148/radiol.12120846 (link)
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Collection: TCGA-KIRC 

  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. TA Juratli, SS Tummala, A Riedl, D Daubner, S Hennig, T Penson, A Zolal, C Thiede, G Schackert, D Krex, JJ Miller, DP Cahill. (2018) Radiographic assessment of contrast enhancement and T2/FLAIR mismatch sign in lower grade gliomas: correlation with molecular groups.Journal of Neuro-Oncology, 2018. DOI:  10.1007/s11060-018-03034-6

  2. 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 

  3. 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 

Collection: TCGA-LUSC

  1. Walter, R., Rozynek, P., Casjens, S., Werner, R., Mairinger, F., Speel, E., Zur Hausen, A., Meier, S., Wohlschlaeger, J., Theegarten, D., Behrens, T., Schmid, K. W., Bruning, T., Johnen, G. (2018). Methylation of L1RE1, RARB, and RASSF1 function as possible biomarkers for the differential diagnosis of lung cancer. PLoS One, 13(5), e0195716. DOI:10.1371/journal.pone.0195716

Collection: 4D-Lung 

  1. Woodruff, H. C., Shieh, C.-C., Hegi-Johnson, F., Keall, P. J. and Kipritidis, J. (2017), Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT. Med. Phys. DOI: 10.1002/mp.12199
  2. Hugo GD, Weiss E,  Sleeman WC, Balik S, Keall PJ, Lu J, Williamson JF. A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med. Phys. (2017) DOI: 10.1002/mp.12059

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10.1016/j.eswa.2018.11.025



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