Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review.

ABSTRACT:

Background

Diagnosing and managing follicular thyroid neoplasms (FTNs) remains a significant challenge, as the malignancy risk cannot be determined until after diagnostic surgery.

Objective

We aimed to use interpretable machine learning to predict the malignancy risk of FTNs preoperatively in a real-world setting.

Methods

We conducted a retrospective cohort study at the Peking University Third Hospital in Beijing, China. Patients with postoperative pathological diagnoses of follicular thyroid adenoma (FTA) or follicular thyroid carcinoma (FTC) were included, excluding those without preoperative thyroid ultrasonography. We used 22 predictors involving demographic characteristics, thyroid sonography, and hormones to train 5 machine learning models: logistic regression,

SUBMITTER: Shan R 

PROVIDER: S-EPMC11833187 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature