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116 results

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Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis.
Beer L, Sahin H, Bateman NW, Blazic I, Vargas HA, Veeraraghavan H, Kirby J, Fevrier-Sullivan B, Freymann JB, Jaffe CC, Brenton J, Miccó M, Nougaret S, Darcy KM, Maxwell GL, Conrads TP, Huang E, Sala E. Beer L, et al. Among authors: jaffe cc. Eur Radiol. 2020 Aug;30(8):4306-4316. doi: 10.1007/s00330-020-06755-3. Epub 2020 Apr 6. Eur Radiol. 2020. PMID: 32253542 Free PMC article.
Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group.
Vargas HA, Huang EP, Lakhman Y, Ippolito JE, Bhosale P, Mellnick V, Shinagare AB, Anello M, Kirby J, Fevrier-Sullivan B, Freymann J, Jaffe CC, Sala E. Vargas HA, et al. Among authors: jaffe cc. Radiology. 2017 Nov;285(2):482-492. doi: 10.1148/radiol.2017161870. Epub 2017 Jun 22. Radiology. 2017. PMID: 28641043 Free PMC article.
Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor.
Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J, Freymann J, Jaffe CC, Mikkelsen T, Flanders A. Jain R, et al. Among authors: jaffe cc. Radiology. 2014 Aug;272(2):484-93. doi: 10.1148/radiol.14131691. Epub 2014 Mar 19. Radiology. 2014. PMID: 24646147 Free PMC article.
Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
Burnside ES, Drukker K, Li H, Bonaccio E, Zuley M, Ganott M, Net JM, Sutton EJ, Brandt KR, Whitman GJ, Conzen SD, Lan L, Ji Y, Zhu Y, Jaffe CC, Huang EP, Freymann JB, Kirby JS, Morris EA, Giger ML. Burnside ES, et al. Among authors: jaffe cc. Cancer. 2016 Mar 1;122(5):748-57. doi: 10.1002/cncr.29791. Epub 2015 Nov 30. Cancer. 2016. PMID: 26619259 Free PMC article.
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.
Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. Gutman DA, et al. Radiology. 2013 May;267(2):560-9. doi: 10.1148/radiol.13120118. Epub 2013 Feb 7. Radiology. 2013. PMID: 23392431 Free PMC article.
Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Imaging Research Group.
Shinagare AB, Vikram R, Jaffe C, Akin O, Kirby J, Huang E, Freymann J, Sainani NI, Sadow CA, Bathala TK, Rubin DL, Oto A, Heller MT, Surabhi VR, Katabathina V, Silverman SG. Shinagare AB, et al. Abdom Imaging. 2015 Aug;40(6):1684-92. doi: 10.1007/s00261-015-0386-z. Abdom Imaging. 2015. PMID: 25753955 Free PMC article.
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma.
Rao A, Rao G, Gutman DA, Flanders AE, Hwang SN, Rubin DL, Colen RR, Zinn PO, Jain R, Wintermark M, Kirby JS, Jaffe CC, Freymann J; TCGA Glioma Phenotype Research Group. Rao A, et al. Among authors: jaffe cc. J Neurosurg. 2016 Apr;124(4):1008-17. doi: 10.3171/2015.4.JNS142732. Epub 2015 Oct 16. J Neurosurg. 2016. PMID: 26473782 Free PMC article.
Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project.
Colen RR, Vangel M, Wang J, Gutman DA, Hwang SN, Wintermark M, Jain R, Jilwan-Nicolas M, Chen JY, Raghavan P, Holder CA, Rubin D, Huang E, Kirby J, Freymann J, Jaffe CC, Flanders A; TCGA Glioma Phenotype Research Group; Zinn PO. Colen RR, et al. Among authors: jaffe cc. BMC Med Genomics. 2014 Jun 2;7:30. doi: 10.1186/1755-8794-7-30. BMC Med Genomics. 2014. PMID: 24889866 Free PMC article.
116 results