Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics
Junhan Zhao, Shih‐Yen Lin, Raphaël Attias, Liza Mathews, Christian Engel, G Larghero, Dmytro Vremenko, Ting‐Wan Kao, Tsung-Hua Lee, Yu-Hsuan Wang, Cheng Che Tsai, Eliana Marostica, Ying‐Chun Lo, David M. Meredith, Keith L. Ligon, Omar Arnaout, Thomas Roetzer-Pejrimovsky, Shih-Chieh Lin, Natalie Shih, Nipon Chaisuriya, David Cook, Jung-Hsien Chiang, Chia‐Jen Liu, Adelheid Wöehrer, Jeffrey A. Golden, MacLean P. Nasrallah, Kun‐Hsing Yu
Abstract
Accurate pathological diagnosis is crucial in guiding personalized treatments for patients with central nervous system cancers. Distinguishing glioblastoma and primary central nervous system lymphoma is particularly challenging due to their overlapping pathology features, despite the distinct treatments required. To address this challenge, we establish the Pathology Image Characterization Tool with Uncertainty-aware Rapid Evaluations (PICTURE) system using 2141 pathology slides collected worldwide. PICTURE employs Bayesian inference, deep ensemble, and normalizing flow to account for the uncertainties in its predictions and training set labels. PICTURE accurately diagnoses glioblastoma and primary central nervous system lymphoma with an area under the receiver operating characteristic curve (AUROC) of 0.989, with the results validated in five independent cohorts (AUROC = 0.924-0.996). In addition, PICTURE identifies samples belonging to 67 types of rare central nervous system cancers that are neither gliomas nor lymphomas. Our approaches provide a generalizable framework for differentiating pathological mimics and enable rapid diagnoses for central nervous system cancer patients. Distinguishing glioblastoma and primary central nervous system lymphoma (PCNSL) remains challenging due to their overlapping pathology features. Here, the authors develop a computational tool, PICTURE, for differentiating similar pathological features enabling improved diagnosis of CNS tumours.