Preoperative CT-based radiomics to predict the 5-year growth of residual nodules after resection of dominant lung tumors in patients with multiple lung subsolid nodules.

backgroundThe current prediction of postoperative growth in synchronous nodules remaining after surgical resection of dominant lung tumors in patients with multiple subsolid lung nodules is limited. This study aims to assess the efficacy of preoperative CT-based radiomics in predicting the 5-year growth of these residual nodules (RNs), versus models constructed using commonly utilized CT morphological and quantitative features.

methodsData from 1392 patients who underwent resection for lung subsolid nodules confirmed as adenocarcinoma or precursor glandular lesions between 2014 and 2018 were retrospectively reviewed. Among the participants, 208 surgical patients with 603 RNs were included, with a follow-up period exceeding five years. Each RN was classified as either grown or stable based on CT imaging. All enrolled RNs were randomly allocated to training and testing sets at an approximately 4:1 ratio. Four models (radiomics, morphological, quantitative, and combined) were built separately by using Random Forest. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses, and compared using DeLong test and net reclassification improvement (NRI).

resultsPatients harbored 1-26 RNs. 17.9% RNs grew in 5 years. Growth proportions varied by size: 4% for < 5 mm, 8.4% for 5-8 mm, and 48.5% for > 8 mm. Eighteen radiomics features, 5 morphological features, and 2 quantitative features were selected to build the respective models. The radiomics model showed a good ability to predict growth with an accuracy of 97.2% and 86.7% in the training and testing sets, respectively. The radiomics model showed a significantly higher area under the curve (AUC: 0.892) than the morphological model (AUC: 0.834, P < 0.05), an advantage over the quantitative model (AUC: 0.862, P = 0.251), and similarity to the combined model (AUC: 0.887) in the testing set. The radiomics model showed better reclassification than morphological (NRI = 7.4%; P = 0.017) and quantitative (NRI = 14%; P = 0.005) models in risk stratification. The calibration curves and decision curve analyses further confirmed the clinical value of radiomics.

conclusionsCT-based radiomics demonstrated superior predictive performance for the 5-year growth of RNs, and can be used independently as a promising tool for future clinical guidance.