Lymphovascular Invasion Detection in Breast Cancer Using Deep Learning

Lymphovascular invasion (LVI) is a critical pathological feature in breast cancer, strongly associated with an increased risk of metastasis and poorer prognosis. However, manual detection of LVI is labor-intensive and prone to inter-observer variability. To address these challenges, this study explores the potential of Swin-Transformer, a state-of-the-art deep learning model, and GigaPath, a cutting-edge foundation model, for automating the detection of LVI in whole-slide images (WSIs) of breast cancer tissue. We trained the models on a dataset of 90 annotated Hematoxylin and Eosin (H&E)-stained breast cancer slides, achieving strong performance with a slide-level Area Under the Receiver Operating Characteristic (AUC) of 97%, a sensitivity of 79% with an average of 8 false positives (FPs) per slide using the best-performing model. The results underscore the potential of Swin-Transformer and GigaPath to enhance diagnostic accuracy and consistency in LVI detection.