A Multicenter Study on Intraoperative Glioma Grading via Deep Learning on Cryosection Pathology

Intraoperative glioma grading remains a significant challenge primarily due to the diminished diagnostic attributable to the suboptimal quality of cryosectioned slides. Precise intraoperative diagnosis is instrumental in guiding the surgical strategy to balance resection extent and neurologic function preservation, thereby optimizing patient prognoses. This study developed a model for intraoperative glioma grading via deep learning on cryosectioned images, termed intraoperative glioma grading on cryosection (IGGC). The model was trained and validated on The Cancer Genome Atlas data sets and 1 cohort (n_train = 1603 and n_validate = 628), and tested on 5 cohorts (n_test = 213). The IGGC model achieved an area under the receiver operating characteristic curve value of 0.99 in differentiating between high-grade glioma and low-grade glioma, and an area under the receiver operating characteristic curve value of 0.96 in identifying grade 4 glioma. Integrated into the clinical workflow, the IGGC model-assisted pathologists of varying experience levels in reducing interobserver variability and enhancing diagnostic consistency. This integrated diagnostic model possesses the potential for clinical implementation, offering a time-efficient and highly accurate method for the 3-grade classification of adult-type diffuse gliomas based on intraoperative cryosectioned slides.