Abstract Objective The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored sur
Abstract Objective The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment of GGN infiltration status using computed tomography (CT) imaging integrated with a neural network to enhance the precision of clinical decision-making in surgical planning and therapeutic interventions. Methods This multicenter retrospective study analyzed clinical data to quantify mismatch rates in surgical approaches across varying infiltration statuses. Regions of interest (ROIs) within the CT lung window level were manually delineated using ITK-SNAP software, enabling the extraction of relevant CT imaging features, including morphological descriptors, first-order statistical parameters, texture attributes, and high-order characteristics. Feature selection was performed using the Lasso algorithm to identify the most predictive variables, which were subsequently incorporated into the radiomics-based neural network model. The neural network architecture combined a 3D convolutional neural network (CNN) with random rotations for data augmentation and employed pre-trained parameters to optimize model weights. Results The radiomics-integrated neural network exhibited high predictive performance, achieving an area under the subject operating characteristic curve (AUC) of 0.85, with validation set AUCs of 0.66 and 0.71. Additionally, the predicted mismatch rate between lobectomy and sublobectomy was 21.48%, representing a 35.57% reduction, while the mismatch rate within sublobectomy decreased by 13.66%, reaching 10.73% Conclusion The neural network-enhanced imaging model provides a robust predictive tool for assessing the preoperative infiltration status of pulmonary GGNs. Its application significantly reduces mismatch rates in surgical decision-making, contributing to more precise and individualized treatment strategies.
