DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules - Wang, Chengdi; Shao, Jun; Xu, Xiuyuan; Yi, Le; Wang, Gang; Bai, Congchen; Guo, Jixiang; He, Yanqi; Zhang, Lei; Yi, Zhang; Li, Weimin (Frontiers in Oncology, 2022)
Objectives: Distinction of malignant pulmonary nodules from the benign ones based on computed tomography (CT) images can be time-consuming but significant in routine clinical management. The advent of artificial intelligence (AI) has provided an opportunity to improve the accuracy of cancer risk prediction. Methods: A total of 8950 detected pulmonary nodules with complete pathological results were retrospectively enrolled. The different radiological manifestations were identified mainly as various tumor density and morphological features. Then, these nodules were classified into benign and malignant groups, both of which were subdivided into finer specific types. Here, we proposed a deep convolutional neural network named DeepLN to identify the radiological features and predict the pathologic subtypes of lung nodules. Results: In terms of density, the area under the receiver operating characteristic curves (AUCs) of DeepLN were 0.9707 (95% confidence interval, CI: 0.9645-0.9765), 0.7789 (95%CI: 0.7569-0.7995), and 0.8950 (95%CI: 0.8822-0.9088) for the pure-ground glass opacity (pGGO), mixed ground glass opacity (mGGO) and solid nodules. As for the morphological features, the AUCs were 0.8347 (95%CI: 0.8193-0.8499) and 0.9074 (95%CI: 0.8834-0.9314) for spiculation and lung cavity respectively. In identification of malignant nodules, our DeepLN algorithm achieved an AUC of 0.8530 (95%CI: 0.8319-0.8681) in the test set. Regarding predicting the pathological subtypes in the test set, the multi-task AUCs in the benign group were 0.8841 (95%CI: 0.8567-0.9083) for benign tumor, 0.8265 (95%CI: 0.8004-0.8499) for inflammation, and 0.8022 (95%CI: 0.7616-0.8445) for other benign ones, while AUCs were 0.8675 (95%CI: 0.8525-0.8813) for adenocarcinoma (LUAD), 0.8792 (95%CI: 0.8640-0.8950) for squamous cell carcinoma (LUSC), 0.7404 (95%CI: 0.7031-0.7782) for other malignant ones respectively in the malignant group. Conclusions: The DeepLN based on deep learning algorithm represented a competitive performance to predict the pathologic subtypes on the basis of non-invasive CT images, and thus has great possibility to be utilized in later routine clinical workflow.