Nomogram Using Intratumoral and Peritumoral Radiomics For the Preoperative Prediction of Visceral Pleural Invasion in Clinical Stage IA Lung Adenocarcinoma

Abstract

Abstract Purpose To investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the presence of visceral pleural invasion (VPI) in patients diagnosed with clinical stage IA lung adenocarcinoma (LUAD) . Methods A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV), which included peritumoral regions at 5mm, 10mm, and 15 mm to construct a radiomics model. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. Results The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with AUC values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation sign, solid attachment sign, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-Radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. Conclusions Our nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in LUAD.