Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via AI-Based Pathology.

Abstract

Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent. Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole slide images and is composed of two deep learning models to locate the lymph nodes and detect cancers. It achieved a area under the receiver operating characteristic (ROC) curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 whole-slide images (WSIs) from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical center, with a AUC of 0.925. Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.