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

## TL;DR

This study develops a model combining clinical data and radiomics to improve the accuracy of lung cancer biopsies by predicting optimal biopsy targets.

## Contribution

A novel clinical–radiomics model using biopsy-slot ROIs to predict tumor-rich targets in CT-guided lung cancer biopsies.

## Key findings

- The combined clinical–radiomics model achieved AUCs of 0.942 and 0.926 in training and validation sets.
- Radiomics features from biopsy-slot ROIs significantly improved diagnostic yield compared to clinical factors alone.

## Abstract

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.

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).

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.

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.