Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning.

BACKGROUNDMohs micrographic surgery (MMS) is the gold standard for treating aggressive basal cell carcinoma, but its success depends on expertise in intraoperative interpretation of frozen sections.

OBJECTIVETo develop weakly supervised, multiple instance learning framework using a pathology foundation model for automated basal cell carcinoma detection in MMS frozen sections.

METHODSAn internal data set of 995 frozen MMS whole-slide images was slide-level labeled as tumor (512) or no tumor (483). Furthermore, tumor regions in the test set were annotated. Whole-slide images were tiled and encoded with Prov-Gigapath features for a weakly supervised multiple instance learning framework. The performance was evaluated as binary slide-level classification and in attention maps showing the localization of the tumor regions prior to validation on 2 external data sets.

RESULTSThe model showed near-perfect diagnostic performance, achieving 97.0% accuracy, and an area under the receiver operating characteristic curve of 0.998 on the internal data set. Furthermore, attention maps visualized diagnostically relevant regions, enhancing model interpretability (Intersection-over-Union = 0.41 ± 0.046 [Dice 0.58 ± 0.046]). External validation confirmed robust performance (90% to 92% accuracy, area under the receiver operating characteristic curve: 0.92-0.95).

LIMITATIONSThe absence of tumor region annotations on external data sets.

CONCLUSIONThese findings support the feasibility of artificial intelligence-assisted analysis of MMS frozen sections and justify prospective studies evaluating its integration into clinical workflows.

Abstract from PubMed