Development and Validation of a Risk Stratification Model of Pulmonary Ground-Glass Nodules Based on Complementary Lung-RADS 1.1 and Deep Learning Scores - Meng, Qingcheng; Li, Bing; Gao, Pengrui; Liu, Wentao; Zhou, Peijin; Ding, Jia; Zhang, Jiaqi; Ge, Hong (Frontiers in Public Health, 2022)
Purpose: To assess the value of novel deep learning (DL) scores combined with complementary lung imaging reporting and data system 1.1 (cLung-RADS 1.1) in managing the risk stratification of ground-glass nodules (GGNs) for improving the diagnosis rate of lung neoplasms and reducing the follow-up period. Materials and methods: Overall, 506 patients with 561 GGNs on routine computed tomography images, obtained between January 2017 and March 2021, were enrolled in this single-center, retrospective Chinese study. cLung-RADS 1.1 was previously validated, and DL algorithms were based on a multi-stage, three-dimensional DL-based convolutional neural network. Therefore, the DL-based cLung-RADS 1.1 model was created using a combination of the risk scores of DL and category of cLung-RADS 1.1. The recall rate, precision, accuracy, per-class F1 score, weighted average F1 score (F1weighted), Matthews correlation coefficient (MCC), and area under the curve (AUC) were used to evaluate the performance of DL-based cLung-RADS 1.1. Results: The percentage of neoplastic lesions in our study was 95.72% (537/561).The DL-based cLung-RADS 1.1 model achieved F1 scores of 95.96% and 95.58%, F1weighted values of 97.49% and 96.62%, accuracies of 92.38% and 91.77%, and MCCs of 32.43% and 37.15% in the training and validation tests, respectively. The model achieved the best AUCs of 0.753 (0.526–0.980) and 0.734 (0.585–0.884) for the training and validation tests, respectively. Conclusion: The DL-based cLung-RADS 1.1 model showed the best performance in risk stratification management of GGNs when compared with other methods, demonstrating substantial promise for developing a more effective personalized lung neoplasm management paradigm.