Deep learning-based algorithm improves radiologists’ performance in lung cancer bone metastases detection on computed tomography

Frontiers in Oncology

Oncology

Ying Fang¹  Honglin Wang¹  Mingdi Xue¹  Jiayao Zhang¹  Pengran Liu¹  Yi Xie¹  Songxiang Liu¹  Zhewei Ye¹  Tongtong Huo²  Yuyu Duan³  Ziyi Wang⁴ 

[1] Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Research Institute of Imaging, National Key Laboratory of Multi-Spectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan, China;Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China;Research Institute of Imaging, National Key Laboratory of Multi-Spectral Information Processing Technology, Huazhong University of Science and Technology, Wuhan, China;

关键词: artificial intelligence;    deep learning;    deep convolutional neural network;    lung cancer bone metastases;    computer-aided diagnosis;   

DOI  :  10.3389/fonc.2023.1125637

 received in 2022-12-16, accepted in 2023-01-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

PurposeTo develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT)MethodsIn this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38). We trained and developed a DCNN model based on positive scans with bone metastases and negative scans without bone metastases to detect and segment the bone metastases of lung cancer on CT. We evaluated the clinical efficacy of the DCNN model in an observer study with five board-certified radiologists and three junior radiologists. The receiver operator characteristic curve was used to assess the sensitivity and false positives of the detection performance; the intersection-over-union and dice coefficient were used to evaluate the segmentation performance of predicted lung cancer bone metastases.ResultsThe DCNN model achieved a detection sensitivity of 0.894, with 5.24 average false positives per case, and a segmentation dice coefficient of 0.856 in the testing cohort. Through the radiologists-DCNN model collaboration, the detection accuracy of the three junior radiologists improved from 0.617 to 0.879 and the sensitivity from 0.680 to 0.902. Furthermore, the mean interpretation time per case of the junior radiologists was reduced by 228 s (p = 0.045).ConclusionsThe proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.

【 授权许可】

Unknown   
Copyright © 2023 Huo, Xie, Fang, Wang, Liu, Duan, Zhang, Wang, Xue, Liu and Ye