Wearable device for axillary lymph node screening in breast cancer based on infrared thermography and artificial intelligence.

backgroundBreast cancer (BC) is the most prevalent cancer among women worldwide, and patients with metastasis to axillary lymph nodes (ALN) experience significantly lower survival rates. Current imaging-based screening methods often suffer from low sensitivity and limited accessibility for detecting ALN metastasis in breast cancer patients. In this study, we present an AI-based infrared thermography system for ALN metastasis detection to improve diagnostic accessibility and reduce intervention-related morbidity.

methodsIn this study, we curated an internal and external cohort for developing and accessing the deep learning model-based infrared thermography system. The internal cohort included 460 inpatient participants from Peking Union Medical College Hospital, randomly divided into a training set (70%) for model development and a hold-out internal validation set (30%) for initially model evaluation. The external cohort, consisting of 80 patients from both outpatient and inpatient departments recruited from Longfu Hospital, served for independent validation of the developed screening tool.

resultsThe developed model AI-IRT for axillary lymph node (ALN) metastasis detection exhibited high diagnostic performance, achieving an Area Under the Curve (AUC) of 0.9424 and an accuracy of 0.8478 in the internal validation set, with a sensitivity of 0.8958 and specificity of 0.8222. In a tertiary classification scenario, the model produced an AUC of 0.8936, with corresponding accuracy, sensitivity, and specificity values of 0.7246, 0.7246, and 0.7852, respectively. In the external validation set, the AI-IRT system achieved an AUC of 0.881 and an accuracy of 0.875, with a sensitivity of 0.892 and specificity of 0.861. For the tertiary classification, the model attained an AUC of 0.771 and an accuracy of 0.613, with both sensitivity and specificity at 0.613 and 0.695, respectively.

conclusionEvaluated on both curated internal and external cohorts, the proposed AI-IRT demonstrated strong performance across multiple centers, highlighting its potential to enhance pre-operative and intra-operative decision-making in the treatment of breast cancer patients.