Prognostic Imaging Biomarkers in XXXX : Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

@entity1 @entity463 @entity5
UNASSIGNED: Purpose To develop and independently validate prognostic imaging biomarkers for predicting survival in @entity1 with @entity463 on the basis of multiregion quantitative image analysis. Materials and Methods This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 @entity1 from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 @entity1 with @entity463 from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each @entity1 , we semiautomatically delineated the @entity5 and performed automated intratumor segmentation, dividing the @entity5 into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire @entity5 , we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. Results The optimal prognostic model identified five imaging biomarkers that quantified @entity5 surface area and intensity distributions of the @entity5 and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and @entity5 volume (concordance index, 0.59; P = .409). Conclusion The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in @entity463 . (  ) RSNA, 2015 Online supplemental material is available for this article.
