Radiomics-based optimization of target selection in CT-guided percutaneous lung cancer biopsy: a retrospective study

Background: CT-guided percutaneous transthoracic needle biopsy (PTNB) is a cornerstone diagnostic procedure for lung cancer. However, its diagnostic accuracy is frequently compromised by sampling errors arising from tumor heterogeneity and operator-dependent target selection, leading to false-negative outcomes. This study aimed to develop and validate a clinical-radiomics model based on biopsy-slot regions of interest (ROIs) to preoperatively predict tumor-rich targets and improve the diagnostic yield of CT-guided PTNB. Methods: In this retrospective study, a cohort of 350 patients with surgically confirmed lung cancer who underwent CT-guided PTNB was analyzed. Patients were classified into true-positive group (TPG) and false-negative group (FNG) based on pathological results and randomly allocated into training and validation sets. Radiomic features were extracted from standardized biopsy-slot ROIs, and feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Independent clinical predictors were identified from a comprehensive set of candidate variables, including patient demographics, lesion characteristics, procedural factors, and classical lung cancer risk factors, using multivariate logistic regression and integrated with radiomic features to develop a combined prediction model. Model performance and clinical utility were assessed using receiver operating characteristic (ROC) and decision curve analysis (DCA). Results: Multivariate analysis identified age and vascular proximity (