Integration of sentinel node mapping and molecular classification in endometrial cancer staging.

Abstract

Sentinel node mapping has gained popularity in surgical staging of endometrial cancer, providing a less invasive alternative to lymphadenectomy for staging purpose. Recent advances in molecular classification have deepened our understanding of endometrial cancer, leading to more personalized approaches in diagnosis and treatment. This review examined the interaction between sentinel node mapping and molecular classification in endometrial cancer, emphasizing the clinical implications. Surrogate molecular classification identified four distinct subtypes, each with different patterns of lymphatic spread and metastatic potential, overcoming the Bokhman's historic dualistic classification in type I (endometrioid) and type II (non-endometrioid) endometrial cancer. Accumulating evidence supported that integrating molecular subtypes with sentinel node mapping, would improve the accuracy of lymph node staging, allowing for more tailored therapeutic strategies. The potential for artificial intelligence and machine learning to analyze molecular signatures in real-time may further refine mapping accuracy and enable more individualized treatment plans. The development of novel molecular tracers and targeted therapies for sentinel node biopsy promises to enhance precision and minimize unnecessary lymphadenectomy. The aim of this review was to explore current methodologies, challenges, and future directions, highlighting the increasing role of molecular tools in sentinel node mapping and the personalized management of endometrial cancer.