Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists
Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists
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DOI：
10.1016/j.gie.2019.04.245
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发表时间：
2019-09-01
影响因子：
7.7
通讯作者：
Tada, Tomohiro
中科院分区：
文献类型：
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作者：
Nakagawa, Kentaro;Ishihara, Ryu;Tada, Tomohiro
背景和目标：肿瘤浸润深度是影响浅表鳞状细胞癌（SCC）患者治疗选择的关键因素。然而，入侵深度的诊断目前是主观的，容易interobserver variability.Methods：我们开发了一个基于深度学习的人工智能（AI）系统的基础上单次拍摄多盒检测器架构的浅表食管SCC的评估。我们从2005年12月至2016年12月期间在我们的机构获得了浅表食管SCC患者的内窥镜图像。在排除质量差的图像后，来自804个具有癌症浸润深度的病理证据的浅表食管SCC的8660个非放大内窥镜（非ME）和5678个ME图像被用作训练数据集，从155例患者中选择405个非ME图像和509个ME图像作为验证集。我们的系统显示，敏感性为90.1%，特异性为95.8%，阳性预测值为99.2%，阴性预测值为63.9%，准确性为91.0%，用于区分病理性粘膜和粘膜下微浸润性（SM 1）癌和粘膜下深浸润性（SM 2/3）癌。由16名经验丰富的内镜医师使用相同的验证集诊断癌症浸润深度，总体灵敏度为89.8%，特异性为88.3%，阳性预测值为97.9%，阴性预测值为65.5%，准确性为89.6%。这种新开发的AI系统在诊断浅表食管SCC患者的浸润深度方面表现出良好的性能，与经验丰富的内窥镜医生的性能相当。
Background and Aims: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma (SCC). However, the diagnosis of invasion depth is currently subjective and liable to interobserver variability.Methods: We developed a deep learning-based artificial intelligence (AI) system based on Single Shot MultiBox Detector architecture for the assessment of superficial esophageal SCC. We obtained endoscopic images from patients with superficial esophageal SCC at our facility between December 2005 and December 2016.Results: After excluding poor-quality images, 8660 non-magnified endoscopic (non-ME) and 5678 ME images from 804 superficial esophageal SCCs with pathologic proof of cancer invasion depth were used as the training dataset, and 405 non-ME images and 509 ME images from 155 patients were selected for the validation set. Our system showed a sensitivity of 90.1%, specificity of 95.8%, positive predictive value of 99.2%, negative predictive value of 63.9%, and an accuracy of 91.0% for differentiating pathologic mucosal and submucosal microinvasive (SM1) cancers from submucosal deep invasive (SM2/3) cancers. Cancer invasion depth was diagnosed by 16 experienced endoscopists using the same validation set, with an overall sensitivity of 89.8%, specificity of 88.3%, positive predictive value of 97.9%, negative predictive value of 65.5%, and an accuracy of 89.6%.Conclusions: This newly developed AI system showed favorable performance for diagnosing invasion depth in patients with superficial esophageal SCC, with comparable performance to experienced endoscopists.