Development and validation of prediction models for papillary thyroid cancer structural recurrence using machine learning approaches.

ABSTRACT:

Background

Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients.

Methods

A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) w

SUBMITTER: Wang H 

PROVIDER: S-EPMC11000372 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature