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  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

Radiomics

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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
  9. 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)
  10. 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
  11. Kumar, S., Dharun. (2017). Combination of fuzzy c-means clustering and texture pattern matrix for brain MRI segmentation. Biomedical Research, 28(5) 
  12. 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

  13. 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

  14. 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

  15. 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. 

  16. 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

  17. 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

  18. 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

  19. 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
  20. 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
  21. 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
  22. 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 
  23. 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)

  24. 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

  25.  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 
  26. Chaddad, A., Tanougast, C. (2015), High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Advances in Bioinformatics, 15(728164). DOI: 10.1155/2015/728164
  27. 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
  28. 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

  29. 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

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