DataSheet_1_Diagnosis and segmentation effect of the ME-NBI-based deep learning model on gastric neoplasms in patients with suspected superficial lesions - a multicenter study.pdf

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.

背景 内镜下可见的胃肿瘤性病变（gastric neoplastic lesions, GNLs）涵盖早期胃癌与上皮内瘤变，需予以精准诊断及及时治疗。然而，胃肿瘤性病变的漏诊率居高不下，会增加胃癌进展的潜在风险。本研究旨在开发一款基于深度学习的计算机辅助诊断（computer-aided diagnosis, CAD）系统，用于对疑似浅表病变患者实施窄带成像放大内镜（magnifying endoscopy with narrow-band imaging, ME-NBI）下的胃肿瘤性病变诊断与分割任务。
方法 本研究回顾性分析了两家中心收录的胃肿瘤性病变患者的窄带成像放大内镜图像。我们开发了两个卷积神经网络（convolutional neural network, CNN）模块并基于上述图像进行训练：CNN1用于胃肿瘤性病变的诊断任务，CNN2用于病变分割任务。此外，我们使用内部测试集与来自另一家中心的外部测试集，对该系统的诊断与分割性能进行评估。
结果 在内部测试集中，CNN1的诊断性能如下：准确率、灵敏度、特异度、阳性预测值（PPV）与阴性预测值（NPV）分别为90.8%、92.5%、89.0%、89.4%与92.2%，曲线下面积（AUC）为0.928。在CNN1的辅助下，所有内镜医师的诊断准确率均高于其独立诊断时的准确率。CNN2与标注真值（ground truth）的平均交并比（IOU）为0.5837，其精确率、召回率与戴斯系数分别为0.776、0.983与0.867。
结论 本研究所开发的计算机辅助诊断系统可作为辅助工具用于胃肿瘤性病变的诊断与分割，帮助内镜医师更精准地诊断胃肿瘤性病变并划定病变范围，从而提升病变活检的阳性率，并保障内镜下切除术的完整性。