Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning Chuxi Huang 1 & Wenhui Lv 2 & Changsheng Zhou 1 & Li Mao 3 & Qinmei Xu 1 & Xinyu Li 2 & Li Qi 1 & Fei Xia 1 & Xiuli Li 3 & Qirui Zhang 2 & Longjiang Zhang 1,2 & Guangming Lu 1,2 Received: 23 April 2020 / Revised: 17 May 2020 / Accepted: 3 July 2020 # European Society of Radiology 2020
Abstract Objectives To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT. Methods A cohort of 1414 SSNs, consisting of 319 transient SSNs in 168 individuals and 1095 persistent SSNs in 816 individuals, were identified on chest CT. The cohort was assigned by examination date into a development set of 996 SSNs, a tuning set of 212 SSNs, and a validation set of 206 SSNs. Our model was built by transfer learning, which was transferred from a well-performed deep learning model for pulmonary nodule classification. The performance of the model was compared with that of two experienced radiologists. Each nodule was categorized by Lung CT Screening Reporting and Data System (Lung-RADS) to further evaluate the performance and the potential clinical benefit of the model. Two methods were employed to visualize the learned features. Results Our model achieved an AUC of 0.926 on the validation set with an accuracy of 0.859, a sensitivity of 0.863, and a specificity of 0.858, and outperformed the radiologists. The model performed the best among Lung-RADS 2 nodules and maintained well performance among Lung-RADS 4 nodules. Feature visualization demonstrated the model’s effectiveness in extracting features from images. Conclusions The transfer learning model presented good performance on the discrimination between transient and persistent SSNs. A reliable diagnosis on nodule persistence can be achieved at baseline CT; thus, an early diagnosis as well as better patient care is available. Key Points • Deep learning can be used for the discrimination between transient and persistent subsolid nodules. • A transfer learning model can achieve good performance when it is transferred from a model with a similar task. • With the assistance of deep learning model, a reliable diagnosis on nodule persistence can be achieved at baseline CT, which can bring a better patient care strategy. Keywords Lung . Tomography, X-ray computed . Deep learning . Diagnosis, computer-assisted Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07071-6) contains supplementary material, which is available to authorized users. * Guangming Lu [email protected] 1
Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, No.305, Zhongshan East Road, Nanjing 210002, China
2
Department of Medical Imaging, Jinling Hospital, Southern Medical University, No.305, Zhongshan East Road, Nanjing 210002, China
3
Deepwise AI Lab, Deepwise Inc, No.8, Haidian Avenue
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