Background and Aims

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.

Methods

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.

Results

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%.

Conclusions

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.

中文翻译：

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卷积神经网络通过人工智能对食管癌的诊断结果

背景和目标

食道癌的预后相对较差。通常情况下，对患者进行晚期诊断以至于无法进行有效治疗时，通常被诊断为晚期。最近，使用深度学习的人工智能（AI）在医学上取得了显着进步。但是，尚无关于其在食道癌诊断中的应用的报道。在这里，我们展示了AI检测食管癌（包括鳞状细胞癌和腺癌）的诊断能力。

方法

我们回顾性收集了日本癌症研究所医院的384例食管癌的8428张训练图像。使用这些，我们通过卷积神经网络（CNN）开发了深度学习。我们还为47例食管癌患者和50例无食管癌患者准备了1118张测试图像，以评估其诊断准确性。

结果

CNN用了27秒的时间分析了1118张测试图像，并以98％的敏感性正确检测出食道癌病例。CNN可以检测到所有7个尺寸小于10毫米的小癌灶。尽管每张图像的阳性预测值为40％，但误诊阴影和正常结构会导致阴性预测值为95％。CNN可以将浅表食道癌与晚期癌症区分开，准确率达98％。

结论

所构建的用于检测食道癌的CNN系统可以在短时间内以高灵敏度分析存储的内窥镜图像。但是，更多的培训将导致更高的诊断准确性。该系统可以在实践中促进早期发现，从而在不久的将来带来更好的预后。