Direct image to subtype prediction for brain tumors using deep learning
Katherine Hewitt, Chiara Maria Lavinia Löffler, Hannah Sophie Muti, Anna S. Berghoff, Christian Eisenlöffel, Marko van Treeck, Zunamys I. Carrero, Omar S. M. El Nahhas, Gregory Patrick Veldhuizen, Sophie Weil, Oliver Lester Saldanha, Laura Bejan, Thomas O Millner, Sebastian Brandner, Sascha Brückmann, Jakob Nikolas Kather
Abstract
Abstract Background Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. Results We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. Conclusions In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.