  Both radiographic (Rad) imaging, such as multi-parametric magnetic resonance
imaging, and digital pathology (Path) images captured from tissue samples are
currently acquired as standard clinical practice for glioblastoma tumors. Both
these data streams have been separately used for diagnosis and treatment
planning, despite the fact that they provide complementary information. In this
research work, we aimed to assess the potential of both Rad and Path images in
combination and comparison. An extensive set of engineered features was
extracted 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, mainly
quantified via gray-level-co-occurrence matrix and gray-level-run-length
matrices. Features extracted from images of 107 glioblastoma patients,
downloaded from The Cancer Imaging Archive, were run through support vector
machine for classification using leave-one-out cross-validation mechanism, and
through support vector regression for prediction of continuous survival
outcome. 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 operating
characteristic curve was estimated to be 0.74, 0.76 and 0.80 for Rad, Path and
RadPath data, when patients were discretized into long- and short-survival
groups based on average survival cutoff. Our results support the notion that
synergistically using Rad and Path images may lead to better prognosis at the
initial presentation of the disease, thereby facilitating the targeted
enrollment of patients into clinical trials.

Image and Video Processing;Computer Vision and Pattern Recognition