Nomogram combining spectral dual-layer detector CT radiomics and deep learning features predicts alveolar tumor spread

Objective: Tumor spread through air spaces (STAS) is associated with increased lung adenocarcinoma recurrence, but it can only be identified postoperatively. Here, a predictive nomogram for detecting preoperative STAS was devised, by combining clinical characteristics with spectral dual-layer detector CT (SDCT)-extracted radiomics (Rad) and deep learning (DL) features. Methods: A total of 197 surgically resected lung adenocarcinoma patients were divided randomly into training (137) and testing (60) cohorts; clinical data, SDCT images, and tumor tissue samples for histopathological STAS identification were obtained. Rad features were extracted by PyRadiomics, and DL by the ResNet50 convolutional neural network, from manually delineated tumor regions of interest in SDCT, and then incorporated into seven machine learning algorithms; receiver operating characteristic (ROC) analysis identified the best-performing one for the Rad, DL, and DLR (Rad+DL) models. The predictive nomogram was formed by combining DLR with statistically significant clinical characteristics identified by uni- and multivariate logistic regression analyses, and its performance was evaluated by ROC and calibration curve analyses. Results: Logistic regression was the best-performing machine learning algorithm, and DLR showed relatively better predictive performance than Rad and DL, with areas under the curve (AUCs) of 0.904 for the training and 0.862 for the testing cohort. The nomogram, comprising DLR with the clinical characteristic of pleural indentation, had the highest accuracy, with AUCs of 0.918 for the training and 0.896 for the testing cohort; its predictions strongly corresponded with actual STAS positivity under calibration curve analysis. Conclusion: The predictive nomogram facilitates reliable preoperative prediction of STAS in lung adenocarcinoma, serving as a valuable tool for devising personalized surgical treatments.