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Collection:  RIDER Collections

  1. Aerts, H. J. W. L. et al. Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC. Sci. Rep.(2016) 6, 33860 (link)
  2. Oliveira B, O'Halloran M, Conceicao R, Glavin M, Jones E. Development of Clinically-Informed 3D Tumor Models for Microwave Imaging Applications. IEEE Antennas and Wireless Propagation Letters 2016;15:520-3. doi: 10.1109/LAWP.2015.2456051

  3. Melouah A. Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique.  Computer Science and Its Applications: Springer; 2015. p. 119-28.
  4. Aerts HJ, Velazquez ER, Leijenaar RTH, Parmar C, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014. 5(4006). doi:10.1038/ncomms5006(link)
  5. Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B. Test–Retest Reproducibility Analysis of Lung CT Image Features. Journal of digital imaging. 2014:1-19.
  6. Meyer CR, Armato SG III, Fenimore CP, McLennan G, Bidaut LM, Barboriak DP, Gavrielides MA, Jackson EF, McNitt-Gray MF, Kinahan PE, Petrick N, Zhao B. Quantitative imaging to assess tumor response to therapy: Common themes of measurement, truth data and error sources. Translational Oncology 2: 198–210, 2009. (link)
  7. McNitt-Gray MF, Bidaut LM, Armato SG III, Meyer CR, Gavrielides MA, Fenimore CP, McLennan G, Petrick N, Zhao B, Reeves AP, Beichel R, Kim H-J, Kinnard L. CT assessment of response to therapy: Tumor volume change measurement, truth data and error. Translational Oncology2009. 2:216–222. (link)
  8. Kinahan PE, Doot RK, Wanner-Roybal M, Bidaut LM, Armato SG III, Meyer CR, McLennan G.PET/CT assessment of response to therapy: Tumor change measurement, truth data and error. Translational Oncology 2:223–230, 2009. (link)
  9. Jackson EF, Barboriak DP, Bidaut LM, Meyer CR. Magnetic resonance assessment of response to therapy: tumor change measurement, truth data and error sources.Translational Oncology 2009 Dec;2(4):211-5. PubMed PMID: 19956380; PubMed Central PMCID: PMC2781079. (link)
  10. Armato SG 3rd, Meyer CR, Mcnitt-Gray MF, McLennan G, Reeves AP, Croft BY, Clarke LP;RIDER Research Group. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.Clin Pharmacol Ther. 2008 Oct;84(4):448-56. PubMed PMID: 18754000. (link)
  11. Melouah, A. (2015). Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications, Springer: 119-128.

  12. Desseroit M-C, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Le Rest CC, Hatt M. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III. European journal of nuclear medicine and molecular imaging. 2016:1-9. http://dx.doi.org/10.1007/s00259-016-3325-5

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  1. 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)
  2. 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.

  3. 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.

  4. 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.

Collection:  TCGA-GBM

  1. Taman Upadhaya ; Yannick Morvan ; Eric Stindel ; Pierre-Jean Le Reste and Mathieu Hatt. 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); doi:10.1117/12.2217151
  2. Upadhaya, Taman, Yannick Morvan, Eric Stindel, Le Reste, and Mathieu HattMulvey 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)
  3. 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)
  4. 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.
  5. 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)
  6. 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.

  7. Upadhaya T, Morvan Y, Stindel E, Le Reste PJ, Hatt Met 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.

  8. 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.

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

  10. 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)

  11. 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.

  12. 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.

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

  14. 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)
  15. 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)
  16. 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 Aug 24 [Epub ahead of print] doi: 10.1007/s11060-014-1580-5 (link)
  17. 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)
  18. Nicolasjilwan M, Hu Y, Yan C, Meerzaman D, Holder CA, Gutman D, et al. Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients. Journal of Neuroradiology, July 2014. doi: 10.1016/j.neurad.2014.02.006
  19. Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE FOR EGFR MUTATION IDENTIFICATION IN GLIOBLASTOMA. Neuro-Oncology. 2014;16(suppl 5):v156-v7.
  20. Wassal E, Zinn P, Colen R. DIFFUSION AND CONVENTIONAL MR IMAGING GENOMIC BIOMARKER SIGNATURE PREDICTS IDH-1 MUTATION IN GLIOBLASTOMA PATIENTS. Neuro-Oncology. 2014;16(suppl 5):v157-v.

  21. 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.

  22. 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.
  23. Amer A, Zinn P, Colen R. IMMEDIATE POST-RESECTION PERICAVITARIAN DWI HYPERINTENSITY IN GLIOBLASTOMA PATIENTS IS PREDICTIVE OF PATIENT OUTCOME. Neuro-Oncology. 2014;16(suppl 5):v138-v9.
  24. 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)
  25. 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)
  26. Mazurowski MA, Desjardins A, Malof JM. Imaging descriptors improve the predictive power of survival models for glioblastoma patients. Neuro-oncology, 2013. 15(10):1389-1394 (link)
  27. Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
  28. Jain R, Poisson L, Narang J, Scarpace L, Rosenblum ML, Rempel S, Mikkelson T. Correlation of Perfusion Parameters with Genes Related to Angiogenesis Regulation in Glioblastoma: A Feasibility Study. American Journal of Neuroradiology, 2012. 33(7):1343-1348 [Epub ahead of print] (link)
  29. Zinn PO, Sathyan P, Mahajan B, Bruyere J, Hegi M, et al. A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 2012 7(8): e41522. doi:10.1371/journal.pone.0041522 (link)
  30. Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS ONE, 2011 6(10): e25451. doi:10.1371/journal.pone.0025451 (link)
  31. Wangaryattawanich, P., M. Hatami, et al.  "Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival." Neuro-oncology, (2015): nov117 .

  32. Kuo, J. S., K. B. Pointer, et al. (2015). "139 Human Ether-a-Go-Go-Related-1 Gene (hERG) K+ Channel as a Prognostic Marker and Therapeutic Target for Glioblastoma." Neurosurgery 62: 210-211.

  33. Zinn, P. O., M. Hatami, et al. (2015). "138 Diffusion MRI ADC Mapping of Glioblastoma Edema/Tumor Invasion and Associated Gene Signatures." Neurosurgery 62: 210.

  34. Steed, T., J. Treiber, et al. (2015). "Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images." American Journal of Neuroradiology 36(4): 678-685.

  35. Lee, J., S. Narang, et al. (2015). "Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation." Journal of Medical Imaging 2(4): 041006-041006.

  36. Itakura, H., A. S. Achrol, et al. (2015). "Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities." Science Translational Medicine 7(303): 303ra138-303ra138.

  37. Cui, Y., K. K. Tha, et al. (2015). "Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images." Radiology: 150358.

  38. Lee, J., S. Narang, et al. (2015). "Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme." PloS one 10(9): e0136557.

  39. Rios Velazquez E, Meier R, Dunn WD Jr, Alexander B, Wiest R, Bauer S, Gutman DA, Reyes M, Aerts HJ. "Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features." Sci Rep. 2015 Nov 18;5:16822. doi: 10.1038/srep16822.

Collection: TCGA-KIRC 

  1. dataset: Prognosis and correlation with VASARI features." Sci Rep. 2015 Nov 18;5:16822. doi: 10.1038/srep16822.

Collection: TCGA-KIRC 

  1. 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)
  2. 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)
  3. 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.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. http://dx.doi.org/10.18632/oncotarget.7129