Quantitative analysis of chest MRI images for benign malignant diagnosis of pulmonary solid nodules - Yang, Bin; Gao, Yeqi; Lu, Jie; Wang, Yefu; Wu, Ren; Shen, Jie; Ren, Jialiang; Wu, Feiyun; Xu, Hai (Frontiers in Oncology, 2023)
Background: In this study, we developed and validated machine learning (ML) models by combining radiomic features extracted from magnetic resonance imaging (MRI) images with clinicopathological factors to assess pulmonary nodule classification for benign-malignant diagnosis.Methods: A total of 333 consecutive patients with pulmonary nodules (233 in the training cohort and 100 in the validation cohort) were enrolled in this study. A total of 2824 radiomic features were extracted from the MRI images (CE T1w and T2w). Logistic regression (LR), Naï ve Bayes (NB), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) classifiers were applied to build the predictive models, and a radiomics score (Rad-score) was obtained for each patient after applying the best prediction model. Clinical factors and Rad -score were used jointly to build a nomogram model based on multivariate logistic regression analysis, and the diagnostic performance of the five prediction models was evaluated using the area under the receiver operating characteristic curve (AUC). Results: A total of 161 women (48.35%) and 172 men (51.65%) with pulmonary nodules were enrolled in this study. Six important features were selected from the 2145 radiomic features extracted from CE T1w and T2w. The XGBoost classifier model achieved the highest discrimination performance, with AUCs of 0.901, 0.906, and 0.851 in the training, validation, and test cohorts, respectively. The nomogram model improved the performance to AUC values of 0.918, 0.912, and 0.877 in the training, validation, and test cohorts, respectively. Conclusion: MRI radiomic ML models demonstrated good nodule classification performance with XGBoost, which was superior to the other four models. The nomogram model achieved higher performance with the addition of clinical information.