Development and validation of an explainable machine learning model to predict Delphian lymph node metastasis in papillary thyroid cancer: a large cohort study.

ABSTRACT: Background: The occurrence of papillary thyroid cancer (PTC) has risen substantially and tends to exhibit early-stage lymph node metastasis (LNM), increasing the risk of postoperative recurrence and decreasing survival. There is a lack of a machine learning (ML) model to predict delphian LNM (DLNM) in PTC. This investigation seeks to comprehensively assess the significance of standard clinical indicators for DLNM prediction, while constructing a dependable and widely applicable ensemble ML framework to support surgical planning and therapeutic decision-making. Methods: This investigation incorporated 1993 sequential PTC patients who underwent curative surgical procedures from 2020 to 2023. Based on the time to surgery, we divided the cohort into the training cohort (n=1395) a

SUBMITTER: Cui J 

PROVIDER: S-EPMC11905415 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature