Prediction of malignancy risk in Bethesda III nodules: development and validation of multiple machine learning models.

ABSTRACT:

Objective

To develop and validate a machine learning (ML)-based prediction model of Bethesda III nodules and create a nomogram based on the best model.

Methods

We collected data on patients with Bethesda III nodules who were admitted between January 2020 and June 2024, including 276 Bethesda III nodules from 7371 patients who underwent ultrasound-guided fine needle aspiration (US-FNA). Clinical, ultrasonographic, cytological, laboratory, and molecular data were collected and randomly split into training and validation cohorts at a ratio of 7: 3. Six feature selection methods and ML algorithms-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)-were evaluated. A nomogram

SUBMITTER: Li W 

PROVIDER: S-EPMC12425777 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature