Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study

Leheng Liu; Zhixia Dong; Jinnian Cheng; Xiongzhu Bu; Kaili Qiu; Chuan Yang; Jing Wang; Wenlu Niu; Xiaowan Wu; Jingxian Xu; Tiancheng Mao; Lungen Lu; Xinjian Wan; Hui Zhou

doi:10.3389/fonc.2022.1075578

Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study

Front Oncol. 2023 Jan 16:12:1075578. doi: 10.3389/fonc.2022.1075578. eCollection 2022.

Authors

Leheng Liu^{1

2}, Zhixia Dong³, Jinnian Cheng⁴, Xiongzhu Bu⁵, Kaili Qiu⁵, Chuan Yang⁵, Jing Wang⁶, Wenlu Niu¹, Xiaowan Wu¹, Jingxian Xu^{1

2}, Tiancheng Mao^{1

2}, Lungen Lu^{1

2}, Xinjian Wan³, Hui Zhou^{1

2}

Affiliations

¹ Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Shanghai Key Laboratory of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
³ Department of Gastroenterology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
⁴ Department of Gastroenterology, Shanghai Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁵ School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China.
⁶ Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

Background: Endoscopically visible gastric neoplastic lesions (GNLs), including early gastric cancer and intraepithelial neoplasia, should be accurately diagnosed and promptly treated. However, a high rate of missed diagnosis of GNLs contributes to the potential risk of the progression of gastric cancer. The aim of this study was to develop a deep learning-based computer-aided diagnosis (CAD) system for the diagnosis and segmentation of GNLs under magnifying endoscopy with narrow-band imaging (ME-NBI) in patients with suspected superficial lesions.

Methods: ME-NBI images of patients with GNLs in two centers were retrospectively analysed. Two convolutional neural network (CNN) modules were developed and trained on these images. CNN1 was trained to diagnose GNLs, and CNN2 was trained for segmentation. An additional internal test set and an external test set from another center were used to evaluate the diagnosis and segmentation performance.

Results: CNN1 showed a diagnostic performance with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 90.8%, 92.5%, 89.0%, 89.4% and 92.2%, respectively, and an area under the curve (AUC) of 0.928 in the internal test set. With CNN1 assistance, all endoscopists had a higher accuracy than for an independent diagnosis. The average intersection over union (IOU) between CNN2 and the ground truth was 0.5837, with a precision, recall and the Dice coefficient of 0.776, 0.983 and 0.867, respectively.

Conclusions: This CAD system can be used as an auxiliary tool to diagnose and segment GNLs, assisting endoscopists in more accurately diagnosing GNLs and delineating their extent to improve the positive rate of lesion biopsy and ensure the integrity of endoscopic resection.

Keywords: convolutional neural network (CNN); deep learning; gastric neoplastic lesions; magnifying endoscopy with narrow band imaging (ME-NBI); suspected superficial lesions.

Grants and funding

This research was funded by Shanghai Science and Technology Program (grant number 18411952900 and 19411951500) and Clinical Research Innovation Plan of Shanghai General Hospital (grant number CTCCR-2021B01).