Value of Artificial Intelligence in Evaluating Lymph Node Metastases

SIMPLE SUMMARY: In surgical pathology, the assessment of the presence of lymph node metastases is a key aspect in terms of the staging and prognosis of cancer patients. This type of work is time-consuming and prone to error. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic cells, so this task can be automated and standardized, increasing diagnostic quality. This manuscript aims to systematically review the published literature regarding the application of various artificial intelligence systems for the assessment of metastases in lymph nodes in whole slide images. ABSTRACT: One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.