Leveraging whole slide embeddings from pretrained machine learning models can improve diagnosis of nonmelanoma skin cancer<\/p>\n<\/h3>\n
By Elana Gotkine HealthDay Reporter<\/b>
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TUESDAY, April 29, 2025 (HealthDay News) — Leveraging whole slide embedding from pretrained foundation models (FMs) improves nonmelanoma skin cancer (NMSC) diagnosis, according to a study presented at the annual meeting of the American Association for Cancer Research, held from April 25 to 30 in Chicago.<\/p>\n
Spencer Ellis, from the University of Chicago, and colleagues examined the effectiveness of general purpose FMs for diagnosis of NMSC, specifically in resource-constrained environments. Three pathology FMs (UNI, PRISM, and Prov-GigaPath) were evaluated, as was a ResNet18 baseline model. Data were included for 2,130 whole-slide images from 553 suspected NMSC biopsy samples from 455 participants: 706 normal tissue; 638 Bowen disease; 575 basal cell carcinoma; and 211 invasive squamous cell carcinoma.<\/p>\n
The researchers found that all three FMs significantly outperformed ResNet18 (mean area under the receiver operating characteristic curve [AUROC], 0.805). The overall best model used the PRISM tile embeddings aggregated using PRISM’s intrinsic Perceiver network, which trained a multilayer perceptron (MLP) model to predict the subtype of NMSC (AUROC, 0.925). Using attention-based multi-instance learning to aggregate tile embeddings to train an MLP model was optimal for the UNI and Prov-GigaPath models (mean AUROCs, 0.913 and 0.908, respectively). The simplest method with logistic regression of global average pooling aggregated embeddings could attain reasonable results for PRISM, UNI, and Prov-GigaPath (mean AUROCs, 0.882, 0.865, and 0.855, respectively).<\/p>\n
“Our results demonstrate that pretrained machine learning models have the potential to aid diagnosis of NMSC, which might be particularly beneficial in resource-limited settings,” coauthor Steven Song, from the Pritzker School of Medicine at the University of Chicago, said in a statement.<\/p>\n