Minami Soichiro, Saso Kazuhiro, Miyoshi Norikatsu, Fujino Shiki, Kato Shinya, Sekido Yuki, Hata Tsuyoshi, Ogino Takayuki, Takahashi Hidekazu, Uemura Mamoru, Yamamoto Hirofumi, Doki Yuichiro, Eguchi Hidetoshi
Department of Gastroenterological Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Department of Innovative Oncology Research and Regenerative Medicine, Osaka International Cancer Institute, Osaka 541-8567, Japan.
Cancers (Basel). 2022 Oct 31;14(21):5361. doi: 10.3390/cancers14215361.
The submucosal invasion depth predicts prognosis in early colorectal cancer. Although colorectal cancer with shallow submucosal invasion can be treated via endoscopic resection, colorectal cancer with deep submucosal invasion requires surgical colectomy. However, accurately diagnosing the depth of submucosal invasion via endoscopy is difficult. We developed a tool to diagnose the depth of submucosal invasion in early colorectal cancer using artificial intelligence. We reviewed data from 196 patients who had undergone a preoperative colonoscopy at the Osaka University Hospital and Osaka International Cancer Institute between 2011 and 2018 and were diagnosed pathologically as having shallow submucosal invasion or deep submucosal invasion colorectal cancer. A convolutional neural network for predicting invasion depth was constructed using 706 images from 91 patients between 2011 and 2015 as the training dataset. The diagnostic accuracy of the constructed convolutional neural network was evaluated using 394 images from 49 patients between 2016 and 2017 as the validation dataset. We also prospectively tested the tool from 56 patients in 2018 with suspected early-stage colorectal cancer. The sensitivity, specificity, accuracy, and area under the curve of the convolutional neural network for diagnosing deep submucosal invasion colorectal cancer were 87.2% (258/296), 35.7% (35/98), 74.4% (293/394), and 0.758, respectively. The positive predictive value was 84.4% (356/422) and the sensitivity was 75.7% (356/470) in the test set. The diagnostic accuracy of the constructed convolutional neural network seemed to be as high as that of a skilled endoscopist. Thus, endoscopic image recognition by deep learning may be able to predict the submucosal invasion depth in early-stage colorectal cancer in clinical practice.
黏膜下层浸润深度可预测早期结直肠癌的预后。尽管黏膜下层浸润较浅的结直肠癌可通过内镜切除进行治疗，但黏膜下层浸润较深的结直肠癌则需要手术切除结肠。然而，通过内镜准确诊断黏膜下层浸润深度较为困难。我们开发了一种利用人工智能诊断早期结直肠癌黏膜下层浸润深度的工具。我们回顾了2011年至2018年间在大阪大学医院和大阪国际癌症研究所接受术前结肠镜检查且经病理诊断为黏膜下层浸润较浅或较深的结直肠癌的196例患者的数据。使用2011年至2015年间91例患者的706张图像作为训练数据集构建了用于预测浸润深度的卷积神经网络。使用2016年至2017年间49例患者的394张图像作为验证数据集评估所构建卷积神经网络的诊断准确性。我们还在2018年对56例疑似早期结直肠癌患者进行了该工具的前瞻性测试。诊断黏膜下层浸润较深的结直肠癌的卷积神经网络的敏感性、特异性、准确性和曲线下面积分别为87.2%（258/296）、35.7%（35/98）、74.4%（293/394）和0.758。在测试集中，阳性预测值为84.4%（356/422），敏感性为75.7%（356/470）。所构建卷积神经网络的诊断准确性似乎与熟练的内镜医师相当。因此，深度学习的内镜图像识别在临床实践中可能能够预测早期结直肠癌的黏膜下层浸润深度。
