Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy

Abstract

Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity. These segmentations were combined for visualization and quantification by assigning a 4-digit numerical code to each voxel to indicate the segmented RP. Each RP volume was then compared with overall survival. A combined classifier was then generated on the basis of significant RPs and optimized volume thresholds. Five RPs were predictive of overall survival before therapy. Combining the RP classifiers into a single prognostic score predicted patient survival better than each alone (P < .005). Voxels coded with 1 RP associated with poor prognosis were pathologically confirmed to contain hypercellular tumor. This study applies radiomic profiling to de novo patients with GBM to determine imaging signatures associated with poor prognosis at tumor diagnosis. This tool may be useful for planning surgical resection or radiation treatment margins.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status

“…Texture analysis, as a contextual quantification method, has already embarked in the medical imaging literature as a method that can detect tissue heterogeneity and complexity [13,14]. Radiomics, applied in the clinical context of glioblastoma, have generally been performed on anatomical sequences and applied to distinguish between GBM subtypes [15], for prediction of survival rates [16] and prognosis [17], prediction of response to treatment [18], as well as risk stratification [19]. Deep-learning based techniques have recently received a sustained attention due to their high performance in such classification tasks [20,21].…”

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

“…The utility of MRI texture features in glioblastoma demonstrated good performance (area under ROC curve > 0.7) in distinguishing different molecular subtypes and predicting 12-month overall survival status (area under ROC curve = 0.69) [ 266 ]. Similarly, based on a series of MRI imaging features of 81 patients, a prognostic model was established that has a potential role in guiding personalized treatment selection [ 267 ]. In Pca, Haralick texture analysis of prostate MRI has the ability to detect the tumor lesions and differentiating Pca with different Gleson scores [ 268 ].…”

Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer

“…To correct for intersubject intensity variation, T2-weighted images were intensity normalized using previously published techniques ( 12 ). Apparent diffusion coefficient (ADC) maps were calculated from different combinations of b values.…”

“…Radiomics describes the field of study in which images are treated as mineable databases to solve classification problems ( 9 , 10 ). Image features, which can be a statistical expression of pixel neighborhood or tumor morphometry, as well as clinical variables ( 9 , 11 , 12 ), are used as inputs to classification algorithms. Models can then be used to detect and characterize a clinically relevant outcome ( 12 – 15 ).…”