Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.

ABSTRACT:

Objective

This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography (LDCT) scans, thereby addressing overdiagnosis in lung cancer screening.

Methods

In this retrospective study, 628 patients with pulmonary nodules who underwent three LDCT scans followed by surgical resection were categorized into invasive carcinoma (n=155) and non-invasive nodule (n=473) groups on the basis of pathological diagnosis. This derivation aimed to identify risk factors and construct a multivariate logistic model. The predictive performance was externally validated in two independent cohorts (retrospectively designed, n=252; prospectively designed, n=269). The discrimination and calibration of the mod

SUBMITTER: Lu J 

PROVIDER: S-EPMC12603625 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature