MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:23392431", "kind": "science-record", "revision": 3, "content_hash": "sha256:29508cd5c8e8015612d582b7a490c5c44c084f07e75eadba098276a5b18e08e8", "created_at": "2026-11-01T15:40:02Z", "updated_at": "2026-11-01T21:19:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.", "doi": "10.1148/radiol.13120118", "pmid": "23392431", "authors": ["David A Gutman", "Lee A D Cooper", "Scott N Hwang", "Chad A Holder", "Jingjing Gao", "Tarun D Aurora", "William D Dunn", "Lisa Scarpace", "Tom Mikkelsen", "Rajan Jain", "Max Wintermark", "Manal Jilwan", "Prashant Raghavan", "Erich Huang", "Robert J Clifford", "Pattanasak Mongkolwat", "Vladimir Kleper", "John Freymann", "Justin Kirby", "Pascal O Zinn", "Carlos S Moreno", "Carl Jaffe", "Rivka Colen", "Daniel L Rubin", "Joel Saltz", "Adam Flanders", "Daniel J Brat"], "journal": "Radiology", "year": 2013, "abstract": "PURPOSE: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.\nMATERIALS AND METHODS: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff α statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.\nRESULTS: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).\nCONCLUSION: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.", "claims": [{"id": "claim-target-result", "statement": "The study found that noninvasive imaging-based features can serve as prognostic biomarkers, using multivariate Cox regression and Fisher exact tests for the associations and publicly available Cancer Genome Atlas (TCGA) data as the source.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:23392431", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "While data are still being generated from additional cases, the initial findings connecting radiology, outcome, and genomics clearly demonstrate the power of this approach; we demonstrated that noninvasive, imaging-based features can be used as prognostic biomarkers."}, {"asset": "source:pmid:23392431", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required."}, {"asset": "source:pmid:23392431", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set."}, {"asset": "source:pmid:23392431", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study found that noninvasive imaging-based features can serve as prognostic biomarkers, using multivariate Cox regression and Fisher exact tests for the associations and publicly available Cancer Genome Atlas (TCGA) data as the source.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:23392431"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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