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

Abstract

BackgroundEndoscopically 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.MethodsME-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.ResultsCNN1 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.ConclusionsThis 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.

The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review

“…The study by Hu et al [ 21 ] yielded poor diagnostic results for mixed lesions [ 18 ]. In addition, images of low quality, bleeding, and mucus were excluded from the learning, and in some studies, only images extracted from one type of endoscopic device were used [ 20 ]. In an actual clinical environment, there are variables, such as centers in various countries and environments, various types and versions of endoscopic devices, and factors that interfere with the endoscopic view according to the condition of the patient’s upper gastrointestinal tract.…”

“…The video also depicted the boundary in real time with good performance, but only in still frames. Liu et al [ 20 ] and Hu et al [ 21 ] and conducted research on the diagnosis of EGC and gastric neoplastic lesions using deep learning under ME-NBI.…”

“…Liu et al [ 20 ] classified the boundary using the segmentation method with data labeled by the endoscopist and showed values of 0.776 precision, a recall coefficient of 0.983, and a Dice coefficient of 0.867 at a critical value of 0.5. What these 2 teams shared was that the results of the deep learning model were better than those of the endoscopist.…”

Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy

“…Leheng Liu et al [11] developed a computer-aided diagnosis (CAD) system to assist in diagnosing and segmenting Gastric Neoplastic Lesions (GNLs). The study utilized two CNNs: CNN1 for diagnosing GNLs and CNN2 for segmenting them.…”

“…Via the segmentation process, which begins with the collecting and preprocessing of gastric images, followed by expert annotation to mark cancerous tissues, the AI model is trained on these annotated images to learn the distinguishing features of neoplastic tissue. Once trained, the model can segment neoplastic areas in new images, aiding in diagnosis and treatment planning [11].…”