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  1. Kalpathy-Cramer J, Zhao B, et al. A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study. J Digit Imaging (2016). 29(4):476-487. (link)
  2. Huang BE, Mulyasasmita W, Rajagopal G. The Path from Big Data to Precision Medicine. Expert Review of Precision Medicine and Drug Development (2016). 1(2):129-143. (link)
  3. Chatellier G, Varlet V, Blachier-Poisson C. "Big data" and "open data": What kind of access should researchers enjoy? Therapie. 2016 Feb;71(1):97-105, 107-14.(link)
  4. Benedict SH, Hoffman K, et al. 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. Int J Radiat Oncol Biol Phys. 2016. 95(3):873-879 (link)
  5. Toga AW, Dinov ID. Sharing big biomedical data. Journal of Big Data. 2015;2(1):1-12.
  6. Moore SM, Maffitt DR, Smith KE, Kirby JS, Clark KW, Freymann JB, Vendt BA, Tarbox LR, Prior FW. De-identification of Medical Images with Retention of Scientific Research Value. RadioGraphics. 2015;35(3):727-35. doi: doi:10.1148/rg.2015140244.
  7. Mayo CS, Deasy JO, et al. How Can We Effect Culture Change Toward Data-Driven Medicine? Int J Radiat Oncol Biol Phys. 2016. 95(3):916-21. (link)
  8. Kirby, J., L. Tarbox, et al. (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
  9. GIllies RJ, Kinahan PE, et al. Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016. 278(2):563-77. (link)
  10. Fedorov A, Clunie D, et al. 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, 2016. (link)
  11. Commean PK, Rathmell JM, Clark KW, Maffitt DR, Prior FW. A Query Tool for Investigator Access to the Data and Images of the National Lung Screening Trial. Journal of Digital Imaging. 2015:1-9. (paper)
  12. Bourne PE. DOIs for DICOM Raw Images: Enabling Science Reproducibility. Radiology. 2015;275(1):3-4. link. PubMed PMID: 25799330.
  13. Armato SG, Hadjiiski L, Tourassi GD, Drukker K, Giger ML, Li F, Redmond G, Farahani K, Kirby JS, Clarke LP. Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned. Journal of Medical Imaging. 2015;2(2):020103-.
  14. Herskovits EH. Quantitative Radiology: Applications to Oncology. Emerging Applications of Molecular Imaging to Oncology. 2014;124:1-30.
  15. Gutman DA, Dunn Jr WD, Cobb J, Stoner RM, Kalpathy-Cramer J, Erickson B. Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer. Frontiers in Neuroinformatics. 2014;8.(paper)
  16. Erickson BJ, Fajnwaks P, Langer SG, and Perry J. Multisite Image Data Collection and Management Using the RSNA Image Sharing Network., Translational oncology, 2014. 7(1):36-39. (paper)
  17. Prior FW, Clark K, Commean P, Freymann J, Jaffe C, Kirby J, Moore S, Smith K, Tarbox L, Vendt B. TCIA: an information resource to enable open science. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE; 2013. (paper)
  18. Gutman DA, Cobb J, Somanna D, et al. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data., Journal of the American Medical Informatics Association, 2013. 20(6): p. 1091-1098. doi: 10.1136/amiajnl-2012-001469 (paper)
  19. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)
  20. Villani L and Prati RC. Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário. Congresso Brasileiro de Informática em Saúde (CBIS’2012). Citado na. 2012.(paper)
  21. Mongkolwat P, Channin DS, Kleper V, Rubin DL. Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder.Radiographics .2012. 32(4):1223-32. (paper).
  22. Jaffe, C Carl. Imaging and Genomics: Is There a Synergy?. Radiology. 2012. 264(2):329-31.(paper).
  23. Freymann JB, Kirby JS, Perry JH, Clunie DA, and Jaffe CC. Image data sharing for biomedical research—meeting HIPAA requirements for de-identification.Journal of Digital Imaging 25, no. 1 (2012): 14-24. (paper)

Radiogenomics

  1. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study. Acta Radiologica. 2016:0284185116673119.

  2. McCann SM, Jiang Y, Fan X, Wang J, et al. Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. AJR Am J Roentgenol (2016). 206(3):559-565 (link)

  3. Katrib A, Hsu W, Bui A, Xing Y. “Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment. Quantitative Biology. 2016:1-12. (link)  

  4. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, Diskin SJ. Imaging genomics in cancer research: limitations and promises. The British Journal of Radiology. 2016:20151030. doi:10.1259/bjr.20151030

  5. 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.

  6. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015;19(1A):A68-A77.

  7. Shinegare AB, Vikram R, Jaffe C, et al. Radiogenomics of clear renal cell carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group. Abdominal Imaging (2015). 40(6)1684-1692. (link)

  8. Pope WB. Genomics of Brain Tumor Imaging. Neuroimaging Clinics of North America. 2015;25(1):105-19.

  9. Gutman, D. A., W. D. Dunn Jr, et al. (2015). Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology: 1-11.

  10. Feldman, M., M. G. Piazza, et al. (2015). 137 Somatostatin Receptor Expression on VHL-Associated Hemangioblastomas Offers Novel Therapeutic Target. Neurosurgery 62: 209-210.

  11. 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. NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures. Translational Oncology. 2014;7(5):556-69. doi: 10.1016/j.tranon.2014.07.007.
  12. Rao A. 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), 2013 IEEE.
  13. Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A, Rubin DL, Napel S, Plevritis SK. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 2012;264(2):387-96. Epub 2012/06/23.  (link)

Radiomics

  1. Zheng C, Wang X, Feng D, editors. Topology guided demons registration with local rigidity preservation. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016: IEEE.

  2. Kotrotsou A, Zinn PO, Colen RR. Radiomics in Brain Tumors: An Emerging Technique for Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America. 2016;24(4):719-29.

  3. Zhao B, Tan Y, Tsai WY, Qi J et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016 Mar 24;6:23428. (link)
  4. Li H, Zhu Y, Burnside ES, Huang E, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. npj Breast Cancer (2016). (link)
  5. Grossmann P, Gutman DA, et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma. BMC Cancer (2016). (link)
  6. Zhu Y, Li H, Guo W, Drukker K, et al. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep (2015). (link) 
  7. Rajakumar K, Muttan S, Deepa G, Revathy S, Priya BS. Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF. Advances in Natural and Applied Sciences. 2015 Jun 1;9(6 SE):506-13. (link)

  8. Parmar, C., R. T. Leijenaar, et al. (2015). "Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer." Sci Rep 5: 11044.

  9.  Parmar, C., P. Grossmann, et al. (2015). "Machine Learning methods for Quantitative Radiomic Biomarkers." Sci Rep 5: 13087.
  10. Tanougast C, Chaddad A. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Adv Bioinformatics (2015). (link)
  11. Chaddad A. Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models.  International Journal of Biomedical Imaging, 2015. (link)
  12. Dhara AK, Mukhopadhyay S, Khandelwal N. 3d texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. SPIE 2013. (link)
  13. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N. Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700K. (link)

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Info
iconfalse
titleThe following refer to the LIDC Collection data, created before submission to TCIA
  1. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Medical Physics, 38: 915–931, 2011. (link)
  2. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, The Lung Image Database Consortium Research Group: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232: 739–748, 2004.
  3. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, Mullan BF, Yankelevitz DF, van Beek EJR, Armato SG III, McNitt-Gray MF, Reeves AP, Gur D, Henschke CI, Hoffman EA, Bland PH, Laderach G, Pais R, Qing D, Piker C, Guo J, Starkey A, Max D, Croft BY, Clarke LP: Evaluation of lung MDCT nodule annotation across radiologists and methods. Academic Radiology 13: 1254–1265, 2006.
  4. Armato SG III, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology 14: 1409–1421, 2007.
  5. Armato SG III, Roberts RY, McNitt-Gray MF, Meyer CR, Reeves AP, McLennan G, Engelmann RM, Bland PH, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth.” Academic Radiology 14: 1455–1463, 2007.
  6. McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais R, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology 14: 1464–1474, 2007.
  7. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJR, Kazerooni EA, Yankelevitz DF, McNitt-Gray MF, McLennan G, Armato SG III, Henschke CI, Aberle DR, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 14: 1475–1485, 2007.
  8. Armato SG III, Roberts RY, Kocherginsky M, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz DF, McLennan G, McNitt-Gray MF, Meyer CR, Reeves AP, Caligiuri P, Quint LE, Sundaram B, Croft BY, Clarke LP: Assessment of radiologist performance in the detection of lung nodules: Dependence on the definition of “truth”. Academic Radiology 16: 28–38, 2009.

Collection: Mouse-Mammary

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Info
titleThese refer to the Mouse-Mammary Collection data, created before submission to TCIA
  1. Jansen SA et al, NMR Biomed. 2011 Aug;24(7):880-7. 
  2. Jansen SA et al, Breast Cancer Res. 2009;11(5):R65. 
  3. Jansen SA et al, Radiology. 2009 Nov;253(2):399-406.
  4. Jansen SA et al, Phys Med Biol. 2008 Oct 7;53(19):5481-93.
  5. Jansen SA., Ductile carcinoma in situ: magnetic resonance and ultrasound imaging in mouse models of breast cancer (Mouse.Mammary.MRI.Ultrasound.Summary.pdf).
  6. Jansen S., Investigating genetic events in the progression of ductal carcinoma in situ (Mouse.Mammary.Genetics.DCIS.pdf).

 

...

 

Collection: NLST

Please see List of NLST Publications at NIH to browse publications from this Data Collection.

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Info
titleThese refer to the QIN-Breast Collection data, created before submission to TCIA
  1. Li X, Dawant BM, Welch EB, Chakravarthy AB, Freehardt D, Mayer I, Kelley M, Meszoely I, Gore JC, Yankeelov TE. Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms. Medical Physics, 2010; 37:2541-52. PMCID: PMC2881925
  2. Atuegwa NC, Gore JC, Yankeelov TE. Using Quantitative Imaging Data to Drive Mathematical Models of Tumor Growth and Treatment Response. Physics in Medicine and Biology, 2010; 55:2429-49. PMCID: PMC2897238
  3. Yankeelov TE, Arlinghaus L, Li X, Gore JC. The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer. Seminars in Oncology, 2011; 38:16-25. PMCID: PMC3073543
  4. Arlinghaus L, Li X, Levy M, Smith D, Welch WB, Gore JC, Yankeelov TE. Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy. Journal of Oncology, 2010. pii: 919620. Epub 2010 Sep 29. PMCID: PMC2952974
  5. Arlinghaus LR, Welch EB, Chakravarthy AB, Farley JS, Gore JC, Yankeelov TE. Motion and distortion correction in diffusion-weighted MRI of the breast at 3T. Journal of Magnetic Resonance Imaging, 2011; 33:1063-70. PMCID: PMC3081111
  6. Gore JC, Manning HC, Quarles CC, Waddell KW, Yankeelov TE. Magnetic Resonance in the Era of Molecular Imaging of Cancer. Magnetic Resonance Imaging, 2011; 29:587-600. PMCID: PMC3285504
  7. Arlinghaus LR, Li X, Rahman AR, Welch EB, Xu L, Gore JC, Yankeelov TE. On the Relationship Between the Apparent Diffusion Coefficient and Extravascular Extracellular Volume Fraction in Human Breast Cancer. Magnetic Resonance Imaging, 2011; 29:630-8. PMCID: PMC3100356
  8. Smith DS, Welch EB, Li X, Arlinghaus LD, Loveless ME, Koyama T, Gore JC, Yankeelov TE. Quantitative effects of accelerated dynamic contrast enhanced MRI data using compressed sensing. Physics in Medicine and Biology, 2011; 56:4933-46. PMCID: PMC3192434
  9. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 2011; 56:5753-69. PMCID: PMC3176673

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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. 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.

  2. 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

  3. 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)

  4. 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)
  5. 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.
  6. 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)

  7. 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.

  8. 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.

  9. 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.

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

  11. 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)

  12. 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.

  13. 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.

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

  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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
  20. 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.

  21. 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.

  22. 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.

  23. 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.
  24. 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.
  25. 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)
  26. 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)
  27. 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)
  28. Zinn PO, Colen RR. Imaging Genomic Mapping in Glioblastoma. Neurosurgery 60:126-130. Aug 2013 (link)
  29. 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)
  30. 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)
  31. 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)

  32. 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 .

  33. 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.

  34. 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.

  35. 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.

  36. 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.

  37. 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.

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