Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System

ABSTRACT

Objective@#Imaging-based survival stratification of patients with gliomas is important for their management, and the 2021 WHO classification system must be clinically tested. The aim of this study was to compare integrative imaging- and pathology-based methods for survival stratification of patients with diffuse glioma. @*Materials and Methods@#This study included diffuse glioma cases from The Cancer Genome Atlas (training set 141 patients) and Asan Medical Center (validation set 131 patients). Two neuroradiologists analyzed presurgical CT and MRI to assign gliomas to five imaging-based risk subgroups (1 to 5) according to well-known imaging phenotypes (e.g., T2/FLAIR mismatch) and recategorized them into three imaging-based risk groups, according to the 2021 WHO classification group 1 (corresponding to risk subgroup 1, indicating oligodendroglioma, isocitrate dehydrogenase [IDH]-mutant, and 1p19q-codeleted), group 2 (risk subgroups 2 and 3, indicating astrocytoma, IDH-mutant), and group 3 (risk subgroups 4 and 5, indicating glioblastoma, IDHwt). The progression-free survival (PFS) and overall survival (OS) were estimated for each imaging risk group, subgroup, and pathological diagnosis. Time-dependent area-under-the receiver operating characteristic analysis (AUC) was used to compare the performance between imaging-based and pathology-based survival model. @*Results@#Both OS and PFS were stratified according to the five imaging-based risk subgroups (P < 0.001) and three imagingbased risk groups (P < 0.001). The three imaging-based groups showed high performance in predicting PFS at one-year (AUC, 0.787) and five-years (AUC, 0.823), which was similar to that of the pathology-based prediction of PFS (AUC of 0.785 and 0.837). Combined with clinical predictors, the performance of the imaging-based survival model for 1- and 3-year PFS (AUC 0.813 and 0.921) was similar to that of the pathology-based survival model (AUC 0.839 and 0.889). @*Conclusion@#Imaging-based survival stratification according to the 2021 WHO classification demonstrated a performance similar to that of pathology-based survival stratification, especially in predicting PFS.