Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodule

Abstract Objective: To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB). Method： A total of 124 and 53 patients with PNMA and PTB, respectively，were retrospectively analyzed from January 2017 to November 2022 in The Forth Affiliated Hospital of Hebei Medical University. A total of 1037 radiomic features were extracted from the contrast enhanced computed tomography (CT). Patients were randomly divided into training group and test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of developed models. Results： Combined clinical and radiomics models established by Logistic Regression method had the best performance. The ROC-AUC (also decision curve analysis) of combined model were 0.940 and 0.990 in the training group and test group, respectively, which showed a good predictive performance for differentiation of PNMA from PTB. Briser Score of the combined model were 0.132 and 0.068 in the training group and test group, respectively. Conclusion: The combined model incorporating radiomics features and clinical parameters may have potential value for preoperative differentiation of PNMA from PTB.