Recent studies showed that genomic analysis of lower grade gliomas can be very effective for stratification of patients into groups with different prognosis and proposed specific genomic classifications. In this study, we explore the association of one of those genomic classifications with imaging parameters to determine whether imaging could serve a similar role to genomics in cancer patient treatment. Specifically, we analyzed imaging and genomics data for 110 patients from 5 institutions from The Cancer Genome Atlas and The Cancer Imaging Archive datasets. The analyzed imaging data contained preoperative FLAIR sequence for each patient. The images were analyzed using the in-house algorithms which quantify 2D and 3D aspects of the tumor shape. Genomic data consisted of a cluster of clusters classification proposed in a very recent and leading publication in the field of lower grade glioma genomics. Our statistical analysis showed that there is a strong association between the tumor cluster-of-clusters subtype and two imaging features: bounding ellipsoid volume ratio and angular standard deviation. This result shows high promise for the potential use of imaging as a surrogate measure for genomics in the decision process regarding treatment of lower grade glioma patients.
Maciej A. Mazurowski, Kal Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, Katherine B. Peters, and Ashirbani Saha, "Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341T (Presented at SPIE Medical Imaging: February 16, 2017; Published: 21 March 2017); https://doi.org/10.1117/12.2255579.
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