MRI-based radiomics analysis in differentiating solid non-small-cell from small-cell lung carcinoma: a pilot study. Issue 10 (October 2022) - Dang, S.; Guo, Y.; Han, D.; Ma, G.; Yu, N.; Yang, Q.; Duan, X.; Duan, H.; Ren, J.
Abstract : Aim: To investigate the ability of a T2-weighted (W) magnetic resonance imaging (MRI)-based radiomics signature to differentiate solid non-small-cell lung carcinoma (NSCLC) from small-cell lung carcinoma (SCLC). Materials and methods: The present retrospective study enrolled 152 eligible patients (NSCLC = 125, SCLC = 27). All patients underwent MRI using a 3 T scanner and radiomics features were extracted from T2W MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to identify the optimal radiomics features for the construction of a radiomics model to differentiate solid NSCLC from SCLC. Threefold cross validation repeated 10 times was used for model training and evaluation. The conventional MRI morphology features of the lesions were also evaluated. The performance of the conventional MRI morphological features, and the radiomics signature model and nomogram model (combining radiomics signature with conventional MRI morphological features) was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Five optimal features were chosen to build a radiomics signature. There was no significant difference in age, gender, and the largest diameter. The radiomics signature and conventional MRI morphological features (only pleural indentation and lymph node enlargement) were independent predictive factors for differentiating solid NSCLC from SCLC. The area under the ROC curves (AUCs) for MRI morphological features, and the radiomics model, and nomogram model was 0.69, 0.85, and 0.90 (ROC), respectively. Conclusions: The T2W MRI-based radiomics signature is a potential non-invasive approach for distinguishing solid NSCLC from SCLC. Highlights: Five optimal features were selected to develop a radiomics signature. MR T2WI based radiomics for differentiating SCLC from NSCLC. Nomogram model improves the diagnostic efficiency of radiomics.