Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses

Abstract

Background

Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges due to its manifestation variations and requires surgical excision upon histological confirmation of its characteristic cellular features. Recent artificial intelligence (AI) advancements in pathology promise enhanced diagnostic accuracy and treatment approaches for this neoplasm.

Methods

We employed a multiscale transfer learning model, initiating the training process at high resolutions and adapting to broader scales. For evaluation purposes, we applied metrics such as accuracy, precision, recall, the F1 score, and the area under the receiver operating characteristic curve (AUROC) to measure the performance of the model, with the statistical significance of the results assessed via two-sided P tests. Our novel approach also included a retrosynthetic saliency mapping technique to achieve enhanced lesion visualization in whole-slide images (WSIs), supporting pathologists' diagnostic processes.

Results

Our model effectively navigated the challenges of global-scale classification, achieving a high validation accuracy of up to 0.973 despite some initial fluctuations. This method displayed excellent accuracy in terms of identifying basaloid and ghost cells, especially at lower scales, with slight variability in its ghost cell accuracy and more noticeable changes in the ‘Other’ category at higher scales. The consistent performance attained for basaloid cells was clear across all scales, whereas areas for improvement were identified in the ‘Other’ category. The model also excelled at generating detailed and interpretable saliency maps for lesion visualization purposes, thereby enhancing its value in digital pathology diagnostics.

Conclusion

Our pilomatricoma study demonstrates the efficacy of a deep learning-based histopathological diagnosis model, as validated by its high performance across various scales, and it is enhanced by an innovative retrosynthetic approach for saliency mapping.