Abstract
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 (<0.5 cm) as the only independent clinical predictors of diagnostic success from among the candidate factors evaluated. The radiomics signature comprised 10 robust features derived from first-order statistics, neighboring gray tone difference matrix (NGTDM), gray level run length matrix (GLRLM), gray level size zone matrix (GLSZM), and wavelet transforms. The combined clinical-radiomics model demonstrated superior discriminative performance, achieving AUCs of 0.942 and 0.926 in the training and validation cohorts, respectively, significantly outperforming both the clinical model (AUCs: 0.703 and 0.696) and the radiomics model alone (AUCs: 0.883 and 0.867). ROC analysis established an optimal radiomics score (Rad score) cutoff of 0.42 (corresponding to a nomogram score of ≈165), yielding sensitivities of 89.6%-88.9% and specificities of 86.3%-84.7%, providing a clinically applicable threshold for biopsy target prioritization. The ROC curves visually confirm the performance of all three models. CONCLUSIONS: The proposed biopsy-slot ROI-based clinical-radiomics model accurately predicts tumor-rich targets in CT-guided PTNB for lung cancer. By synergistically integrating quantitative imaging biomarkers with key clinical variables, this model facilitates personalized biopsy planning and promotes precision-guided sampling strategies, potentially reducing nondiagnostic procedures. However, because this retrospective single-center study only included patients who subsequently underwent surgical resection, the findings may not be directly generalizable to inoperable patients or the broader population undergoing CT-guided PTNB.