Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients - Rathore, Saima; Akbari, Hamed; Bakas, Spyridon; Pisapia, Jared M.; Shukla, Gaurav; Rudie, Jeffrey D.; Da, Xiao; Davuluri, Ramana V.; Dahmane, Nadia; O'Rourke, Donald M.; Davatzikos, Christos (Frontiers in Computational Neuroscience, 2019)
Glioblastoma, the most frequent primary malignant brain neoplasm, is genetically diverse and classified into four transcriptomic subtypes, i.e., classical, mesenchymal, proneural, and neural. Currently, detection of transcriptomic subtype is based on ex vivo analysis of tissue, and does not capture the spatial heterogeneity of the tumor. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive imaging markers of transcriptomic classification of glioblastoma, based solely on routine imaging sequences. A preoperative retrospective cohort of 112 pathology-proven de novo glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from Hospital of the University of Pennsylvania were included. Following segmentation of tumors into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machine was employed to multi-variately integrate these features and to derive an imaging signature of transcriptomic subtype. Extracted features included intensity distributions, volume, morphology, statistics, tumors’ anatomical location, and texture descriptors for each tumor sub-region. The derived signature was evaluated against the transcriptomic subtype of the surgically resected tissue specimens, using a 5-fold cross-validation method and a receiver-operating-characteristics analysis. The proposed model was 71% accurate in distinguishing among the four transcriptomic subtypes. The accuracy (sensitivity/specificity) for distinguishing each subtype (classical, mesenchymal, proneural, neural) from the rest was equal to 88.4% (71.4/92.3), 75.9% (83.9/72.8), 82.1% (73.1/84.9), and 75.9% (79.4/74.4), respectively. The findings were also replicated in The Cancer Genomic Atlas glioblastoma dataset. The obtained imaging signature for the classical subtype was dominated by associations with features related to edge sharpness, whereas for the mesenchymal subtype had more pronounced presence of higher T2 and T2-FLAIR signal in edema, and higher volume of enhancing tumor and edema. The proneural and neural subtypes were characterized by the lower T1-Gd signal in enhancing tumor and higher T2-FLAIR signal in edema, respectively. Our results indicate that multivariate analysis of features extracted from routine MRI, yields prompt non-invasive biomarkers of the transcriptomic profile of glioblastoma, without the need for advanced genetic testing. Importantly our findings can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.