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Both radiographic (Rad) imaging, such as multi-parametric magnetic resonanceimaging, and digital pathology (Path) images captured from tissue samples arecurrently acquired as standard clinical practice for glioblastoma tumors. Boththese data streams have been separately used for diagnosis and treatmentplanning, despite the fact that they provide complementary information. In thisresearch work, we aimed to assess the potential of both Rad and Path images incombination and comparison. An extensive set of engineered features wasextracted from delineated tumor regions in Rad images, comprising T1, T1-Gd,T2, T2-FLAIR, and 100 random patches extracted from Path images. Specifically,the features comprised descriptors of intensity, histogram, and texture, mainlyquantified via gray-level-co-occurrence matrix and gray-level-run-lengthmatrices. Features extracted from images of 107 glioblastoma patients,downloaded from The Cancer Imaging Archive, were run through support vectormachine for classification using leave-one-out cross-validation mechanism, andthrough support vector regression for prediction of continuous survivaloutcome. The Pearson correlation coefficient was estimated to be 0.75, 0.74,and 0.78 for Rad, Path and RadPath data. The area-under the receiver operatingcharacteristic curve was estimated to be 0.74, 0.76 and 0.80 for Rad, Path andRadPath data, when patients were discretized into long- and short-survivalgroups based on average survival cutoff. Our results support the notion thatsynergistically using Rad and Path images may lead to better prognosis at theinitial presentation of the disease, thereby facilitating the targetedenrollment of patients into clinical trials.

Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma