Incorporating adipose tissue into a CT-based deep learning nomogram to differentiate granulomas from lung adenocarcinomas

<h2>Summary</h2> We aimed to build and validate a computed tomography (CT)-based deep learning nomogram for discriminating granulomas from lung adenocarcinomas. A retrospective study of 1,159 patients with solitary lung nodules from three institutions in China who underwent pre-operative lung CT scans was performed. The patients were divided into one training, one validation, one test, and two external validation cohorts. Deep learning features were extracted from CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for dimension reduction and feature selection. Logistic regression analysis showed that age, gender, intranodular and perinodular (IPN) features, and adipose features were the significant predictors of malignancy presence (all <i>p</i> < 0.05). The nomogram was built by incorporating these four factors and achieved better diagnostic accuracy than the single-factor model. The nomogram demonstrates satisfactory discrimination and calibration. In addition, decision curve analysis revealed the considerable clinical usefulness of the nomogram.