Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer †.

Simple Summary: Currently, pathologists use ultrastaging to detect whether cancer has spread to the lymph nodes. This process is time-consuming and expensive. Our pilot study explored the use of a deep learning algorithm to help detect cancer spread to lymph nodes of early-stage cervical cancer patients. Using this technology could make the detection process faster, more efficient, and less costly. We evaluated an algorithm that was originally designed to identify cancer spread to lymph nodes in breast and colon cancer in cervical cancer patients. The study included 21 women with different types of early-stage cervical cancer. The algorithm was used to analyze 47 lymph node samples and successfully identified all cases where cancer had spread, showing 100% accuracy. Although the algorithm was initially developed for other cancers, it proved highly effective in this new population. More prospective research in a larger group of patients is needed to confirm its cost-effectiveness. Background/objectives: Pathological ultrastaging, an essential part of sentinel lymph node (SLN) mapping, involves serial sectioning and immunohistochemical (IHC) staining in order to reliably detect clinically relevant metastases. However, ultrastaging is labor-intensive, time-consuming, and costly. Deep learning algorithms offer a potential solution by assisting pathologists in efficiently assessing serial sections for metastases, reducing workload and costs while enhancing accuracy. This proof-of-principle study evaluated the effectiveness of a deep learning algorithm for SLN metastasis detection in early-stage cervical cancer. Methods: We retrospectively analyzed whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained SLNs from early-stage cervical cancer patients diagnosed with an SLN metastasis with either H&E or IHC. A CE-IVD certified commercially available deep learning algorithm, initially developed for detection of breast and colon cancer lymph node metastases, was employed off-label to assess its sensitivity in cervical cancer. Results: This study included 21 patients with early-stage cervical cancer, comprising 15 with squamous cell carcinoma, five with adenocarcinoma, and one with clear cell carcinoma. Among these patients, 10 had macrometastases and 11 had micrometastases in at least one SLN. The algorithm was applied to evaluate H&E WSIs of 47 SLN specimens, including 22 that were negative for metastasis, 13 with macrometastases, and 12 with micrometastases in the H&E slides. The algorithm detected all H&E macro- and micrometastases with 100% sensitivity. Conclusions: This proof-of-principle study demonstrated high sensitivity of a deep learning algorithm for detection of clinically relevant SLN metastasis in early-stage cervical cancer, despite being originally developed for adenocarcinomas of the breast and colon. Our findings highlight the potential of leveraging an existing algorithm for use in cervical cancer, warranting further prospective validation in a larger population. [ABSTRACT FROM AUTHOR]