Foundation artificial intelligence models enable high-accuracy diagnostic differentiation of hybrid neurofibroma/schwannoma using whole-slide images

Peripheral nerve sheath tumors encompass a heterogeneous group including schwannoma, neurofibroma, and hybrid neurofibroma/schwannoma (HNS). Accurate differentiation is crucial due to distinct biological behavior, different management, and a possible assignment to a genetic disease. We investigated whether deep learning (DL)-based artificial intelligence (AI) reliably distinguished HNS from schwannomas and neurofibromas using whole-slide images (WSIs). Utilizing a dataset of H&E-stained WSIs from 115 tumors, we applied state-of-the-art foundation models (UNI, CONCH, ResNet50) for feature extraction, combined with multiple instance learning algorithms CLAM and mMIL, and a patch-based classifier. Our models achieved high validation performance, with macro-area under the receiver operating characteristic curve (AUC-ROC) values up to 1.0 for UNI-based MIL models. Attention maps …