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

Abstract

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 that does not capture the spatial tumor heterogeneity. In view of accumulative evidence of in vivo imaging signatures summarizing molecular features of cancer, this study seeks robust non-invasive radiographic markers of transcriptomic classification of glioblastoma, based solely on routine clinicallyacquired imaging sequences. A pre-operative retrospective cohort of 112 pathologyproven de novo 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 Rathore et al.Non-invasive Transcriptomic Classification of Glioblastoma 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 can be influential in surgical decision-making, treatment planning, and assessment of inoperable tumors.

Uses of artificial intelligence in glioma: A systematic review

“…AI can be used to differentiate between four molecular subtypes of glioblastoma: Neural, proneural, mesenchymal and classical by using the transcriptomic profiling tool (28). Research based on standard MRI sequences that used SVM obtained an accuracy of 71% in distinguishing the four subtypes (29).…”

Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)

“…One more limitation of our study is that we used retrospective multi-institutional data; a prospective dataset comparing our methods to standard histopathological review would lend further validity and confidence to our ML models. Future work would include the creation and validation of ML models through the neuro-CaPTk application for various other molecular characterizations, including transcriptomic subtypes, 50 as well as detection of other distinct molecular markers (eg, PTEN, TP53, and ATRX). Moreover, enthusiastically taking on the evolving field of integrated diagnostics, we aim to provide comprehensive diagnostic modules integrating radiology, pathology, and clinical markers in neuro-CaPTk.…”

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

“…It identifies the tumors into four molecular subtypes; classical, mesenchymal, proneural, and neural [ 27 ]. A study that applied SVM and based on conventional MRI sequences showed 71% accuracy in delineating the four subtypes [ 60 ].…”