No 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. Materials 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 a 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. Results: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). Conclusion: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.q RSNA, 2013

No abstract

Purpose:To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Materials andMethods:An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. Results:Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBV NER ), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBV NER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBV NER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBV NER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBV NER , age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Conclusion:Patients with high rCBV NER and NER crossing the midline and those with high rCBV NER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBV NER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.q RSNA, 2014

RECIST (Response Evaluation Criteria in Solid Tumors) is a widely employed method introduced in 2000 to assess change in tumor size in response to therapy. The simplicity of the technique, however, contrasts sharply with the increasing sophistication of imaging instrumentation. Anatomically based imaging measurement, although supportive of drug development and key to some accelerated drug approvals, is being pressed to improve its methodologic robustness, particularly in the light of more functionally-based imaging that is sensitive to tissue molecular response such as fluorodeoxyglucose positron emission tomography. Nevertheless ready availability of computed tomography and magnetic resonance imaging machines largely assures anatomically based imaging a continuing role in clinical trials for the foreseeable future. Recent advances in image processing enabled by the computational power of modern clinical scanners open a considerable opportunity to characterize tumor response to therapy as a complement to image acquisition. Various alternative quantitative volumetric approaches have been proposed but have yet to gain wide acceptance by clinical and regulatory communities, nor have these more complex techniques shown incontrovertible evidence of greater reproducibility or predictive value of clinical events and outcome. Unless plans are created for clinical trials that incorporate the design needed to prove the added value and unique clinical utility of these novel approaches, any theoretical benefit of these more elaborate methods could remain unfulfilled.