Machine learning directed sentinel lymph node biopsy in cutaneous head and neck melanoma

Abstract

Background The specificity of sentinel lymph node biopsy (SLNB) for detecting lymph node metastasis in head and neck melanoma (HNM) is low under current National Comprehensive Cancer Network (NCCN) treatment guidelines. Methods Multiple machine learning (ML) algorithms were developed to identify HNM patients at very low risk of occult nodal metastasis using National Cancer Database (NCDB) data from 8466 clinically node negative HNM patients who underwent SLNB. SLNB performance under NCCN guidelines and ML algorithm recommendations was compared on independent test data from the NCDB (n = 2117) and an academic medical center (n = 96). Results The top‐performing ML algorithm (AUC = 0.734) recommendations obtained significantly higher specificity compared to the NCCN guidelines in both internal (25.8% vs. 11.3%, p < 0.001) and external test populations (30.1% vs. 7.1%, p < 0.001), while achieving sensitivity >97%. Conclusion Machine learning can identify clinically node negative HNM patients at very low risk of nodal metastasis, who may not benefit from SLNB.

Evolution of the treatment of esophageal cancer: artificial intelligence and the role of sentinel lymph node assessment in esophageal cancer

“…Liu et al investigated a deep learning-based AI algorithm called LYmph Node Assistant for the analysis of tissues and the recognition of metastases on whole-slide images. The authors observed an AUC of 99.3% and a sensitivity of 91.9% [26] . Oliver et al developed and tested multiple machine learning algorithms for the identification of patients with head and neck melanoma with a very low risk of occult lymph node metastasis, with the goal of avoiding the unnecessary and expensive use of SLN biopsy based on NCCN treatment guidelines.…”

The emerging role of sentinel lymph node biopsy in oral cavity and oropharyngeal carcinomas

“…Real-time quantitative PCR on SNs is an area of exploration and shows potential for more accurate intraoperative identification of occult metastases [91,92] . The application of machine learning is also being explored to help identify pathological features that predict the risk of metastasis prior to surgical treatment [93,94] . The hope is to identify patients who are at higher risk and may benefit from SLNB or END versus observation.…”

Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery

“…In otolaryngology, for instance, AI and machine learning have been used for screening, diagnosis, and treatment decision in rhinology, 5 otology, 6 laryngology, 7 and head and neck oncology 8 . We have also previously demonstrated the utility of AI for predicting head and neck melanoma patients with a low risk of nodal metastasis 9 and identifying patients with oral cavity squamous cell carcinoma at risk for occult nodal disease 10 …”