Early identification of melanomas with high metastatic potential is crucial for treatment planning and survival prediction. We investigated whether weakly supervised WSI-based and multimodal learning approaches can identify metastatic risk in primary cutaneous melanoma beyond current clinicopathological predictors. A total of 426 routinely stained whole-slide images (WSIs), from primary melanomas (249 metastatic and 177 non-metastatic) together with corresponding clinicopathological features, were collected and divided to training and validation, and a hold-out test set. WSIs were divided into patches and encoded using Prov-GigaPath, while clinicopathological features were converted to text embeddings using BioMedBERT. We evaluated a weakly supervised transformer-based approach for slide-level metastatic risk prediction from WSIs, both alone and in combination with clinicopathological features, and compared performance to a model based on clinicopathological features alone. Both WSI-based models achieved strong performance (AUC = 0.887 and 0.883) and higher accuracy (0.847 for both) than the clinicopathological-based model (AUC = 0.849, accuracy = 0.753). This benefit was most pronounced in T2 tumors, where early risk stratification is clinically most relevant. These findings demonstrate that weakly supervised WSI-based models capture prognostically relevant information and highlight their potential for early metastatic risk stratification in primary melanoma.
Dahlén et al. (Wed,) studied this question.