Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan; Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan.
Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan.
Gastrointest Endosc. 2019 Jan;89(1):25-32. doi: 10.1016/j.gie.2018.07.037. Epub 2018 Aug 16.
The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma.
We retrospectively collected 8428 training images of esophageal cancer from 384 patients at the Cancer Institute Hospital, Japan. Using these, we developed deep learning through convolutional neural networks (CNNs). We also prepared 1118 test images for 47 patients with 49 esophageal cancers and 50 patients without esophageal cancer to evaluate the diagnostic accuracy.
The CNN took 27 seconds to analyze 1118 test images and correctly detected esophageal cancer cases with a sensitivity of 98%. CNN could detect all 7 small cancer lesions less than 10 mm in size. Although the positive predictive value for each image was 40%, misdiagnosing shadows and normal structures led to a negative predictive value of 95%. The CNN could distinguish superficial esophageal cancer from advanced cancer with an accuracy of 98%.
The constructed CNN system for detecting esophageal cancer can analyze stored endoscopic images in a short time with high sensitivity. However, more training would lead to higher diagnostic accuracy. This system can facilitate early detection in practice, leading to a better prognosis in the near future.
食管癌的预后相对较差。患者通常在晚期被诊断出，此时往往已经来不及进行有效的治疗。最近，人工智能（AI）使用深度学习在医学领域取得了显著的进展。然而，目前还没有关于其应用于诊断食管癌的报告。在这里，我们展示了 AI 诊断食管癌（包括鳞状细胞癌和腺癌）的能力。
我们回顾性地收集了日本癌症研究所医院 384 名患者的 8428 张食管癌训练图像。我们使用这些图像通过卷积神经网络（CNNs）开发了深度学习。我们还准备了 1118 张来自 47 名患者的测试图像，其中包括 49 例食管癌和 50 例无食管癌患者，以评估诊断准确性。
CNN 分析 1118 张测试图像需要 27 秒，其对食管癌的检出率为 98%，敏感度为 98%。CNN 可以检测到所有 7 个小于 10mm 的小癌症病变。虽然每张图像的阳性预测值为 40%，但误诊的阴影和正常结构导致阴性预测值为 95%。CNN 可以区分早期食管癌和晚期食管癌，准确率为 98%。
我们构建的用于检测食管癌的 CNN 系统可以在短时间内分析存储的内镜图像，具有较高的敏感性。然而，增加更多的训练数据将提高诊断的准确性。该系统有助于在实践中实现早期检测，在不久的将来带来更好的预后。
