Multivariate Analysis of Preoperative Magnetic Resonance Imaging Reveals Transcriptomic Classification of de novo Glioblastoma Patients

• Published in: Frontiers in computational neuroscience 2019-12, Vol.13, p.81-81
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Angiogenesis; Biomarkers; Brain cancer; brain tumors classification; Classification; Datasets; Decision making; Edema; Genetic screening; Glioblastoma; Hospitals; Image processing; Magnetic resonance imaging; Medical prognosis; Medicine; Mesenchyme; Multivariate analysis; Mutation; Neoplasia; Neuroimaging; Neuroscience; Neurosurgery; NMR; Nuclear magnetic resonance; Patients; Segmentation; Spatial heterogeneity; Statistical analysis; transcriptomic classification; Tumors
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2019.00081

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 analysis of tissue that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinically-acquired imaging sequences. A pre-operative retrospective cohort of 112 pathology-proven glioblastoma patients, having multi-parametric MRI (T1, T1-Gd, T2, T2-FLAIR), collected from the Hospital of the University of Pennsylvania were included. Following tumor segmentation into distinct radiographic sub-regions, diverse imaging features were extracted and support vector machines were employed to multivariately integrate these features and 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 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 quantitative multivariate analysis of features extracted from clinically-acquired MRI may provide a radiographic biomarker of the transcriptomic profile of glioblastoma. Importantly our findings ca