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

• Published in: Frontiers in oncology Vol. 13; p. 1125637
• Main Authors: Huo, Tongtong, Xie, Yi, Fang, Ying, Wang, Ziyi, Liu, Pengran, Duan, Yuyu, Zhang, Jiayao, Wang, Honglin, Xue, Mingdi, Liu, Songxiang, Ye, Zhewei
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 08.02.2023
• Subjects: artificial intelligence; computer-aided diagnosis; deep convolutional neural network; deep learning; lung cancer bone metastases; Oncology; deep learning; deep convolutional neural network; computer-aided diagnosis; lung cancer bone metastases; artificial intelligence
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1125637

To develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT). In 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. The 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). The proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.