Prediction of subsolid pulmonary nodule growth rate using radiomics.

ABSTRACT:

Background

Pulmonary nodule growth rate assessment is critical in the management of subsolid pulmonary nodules (SSNs) during clinical follow-up. The present study aimed to develop a model to predict the growth rate of SSNs.

Methods

A total of 273 growing SSNs with clinical information and 857 computed tomography (CT) scans were retrospectively analyzed. The images were randomly divided into training and validation sets. All images were categorized into fast-growth (volume doubling time (VDT) ≤ 400 days) and slow-growth (VDT > 400 days) groups. Models for predicting the growth rate of SSNs were developed using radiomics and clinical features. The models' performance was evaluated using the area under the curve (AUC) values for the receiver operating characteristic curve.

R

SUBMITTER: Ma ZJ 

PROVIDER: S-EPMC10629176 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature