MRI-LOCALIZED BIOPSIES REVEAL HISTOPATHOLOGIC HETEROGENEITY IN POST-TREATMENT RECURRENT HIGH-GRADE GLIOMA

Abstract Evaluation of recurrence in post-treatment glioma is challenging because contrast-enhancing (CE) lesions are a mixture of tumor and treatment effect. This study characterizes intratumoral heterogeneity using quantitative digital pathology to correlate intraoperative MRI-localized biopsies with histopathology in the post-treatment setting. Findings will inform multiparametric radiographic models of intratumoral heterogeneity. A retrospective review was performed on adult patients with MRI-localized biopsies obtained during resection for post-treatment recurrent high-grade glioma. 68 patients and 170 MRI-localized samples were analyzed (median 2 samples/patient). Immunohistochemistry (IHC) for markers of glioma cells (SOX2), macrophages (CD68), and proliferating cells (KI67) was used to characterize biopsies. Slides were digitized and quantified using an automated cell-counting algorithm. Histopathological criteria based on IHC data was developed to classify biopsies. IHC quantification was compared across histological groups using ANOVA and paired t-tests. Most patients (52/68) yielded multiple biopsies. 75% (39/52) demonstrated heterogeneity in histological classification of all specimens obtained from their lesion. 47/170 (28%) biopsies were predominantly treatment effect, and most were CE (31/47 or 66%). Only 75/170 (44%) biopsies contained recurrent glioma, and 21/75 (28%) were NE. SOX2 labeling index was higher in biopsies containing recurrent tumor (p=5.13E-25). CD68 labeling index was higher in biopsies with predominant treatment effect (p=1.35E-12). IHC data from MRI-localized biopsies informed a multiple linear regression model which demonstrated significant predictive value for determining the distribution of recurrent tumor in the post-treatment setting. Contrast enhancement is not a reliable predictor of tumor in recurrent high-grade glioma. Most patients demonstrated marked intratumoral heterogeneity, highlighting the difficulty of accurate tumor sampling post-treatment glioma. Our histopathological classification significantly distinguished recurrent tumor from treatment effect and informed a multiparametric radiomic model which can guide surgical sampling and assess response to therapy.