Separability of histopathology foundation model embeddings of stimulated Raman histology images for lung carcinoma subtyping.

Abstract

Histopathology foundation models trained on hematoxylin and eosin (H&E) whole-slide images have recently emerged as versatile encoders for downstream diagnostic tasks, but their transferability to out-of-domain imaging modalities such as stimulated Raman histology (SRH) is unknown. Here, we retrospectively evaluated whether embeddings from histopathology foundation models enable intraoperative SRH-based classification of lung cancer patients into (i) non-small cell lung cancer (NSCLC) versus pulmonary metastasis (MET) and (ii) adenocarcinoma (AC) versus squamous cell carcinoma (SCC). For NSCLC versus MET, embeddings from histopathology foundation models showed separability in a range comparable to ResNet50. In contrast, for AC versus SCC, histopathology foundation models showed a tendency to outperform ResNet50, although without statistical significance. Overall performance remained too low to support clinical deployment. Nonetheless, our results indicate that histopathology foundation models could transfer discriminative representations to SRH images for specific intraoperative subtyping tasks.