Prediction of benign and malignant pulmonary nodules using preoperative CT features: using PNI-GARS as a predictor.

ABSTRACT:

Purpose

The aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging.

Materials and methods

A data set of Centers 1 (training set: 2633; internal testing set: 1129); Center 2 and Center 3 (external testing set: 218) of patients with pulmonary nodule cases was retrospectively collected. Handcrafted features were extracted from noncontrast chest CT scans by three senior radiologists. A total of 22 clinically handcrafted parameters (age, gender, L-RADS, and PNI-GARS et al.) were used to construct machine learning models (random forest, gradient boosting, and explainable boosting) for the classification of preoperative pulmonary nodules, and the parameters of the model were adjusted to achieve optim

SUBMITTER: Zhan Y 

PROVIDER: S-EPMC11614820 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature