Performance of a deep learning-based identification system for esophageal cancer from CT images

Masashi Takeuchi, Takumi Seto, Masahiro Hashimoto, Nao Ichihara, Yosuke Morimoto, Hirofumi Kawakubo, Tatsuya Suzuki, Masahiro Jinzaki, Yuko Kitagawa, Hiroaki Miyata, Yasubumi Sakakibara

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Because cancers of hollow organs such as the esophagus are hard to detect even by the expert physician, it is important to establish diagnostic systems to support physicians and increase the accuracy of diagnosis. In recent years, deep learning-based artificial intelligence (AI) technology has been employed for medical image recognition. However, no optimal CT diagnostic system employing deep learning technology has been attempted and established for esophageal cancer so far. Purpose: To establish an AI-based diagnostic system for esophageal cancer from CT images. Materials and methods: In this single-center, retrospective cohort study, 457 patients with primary esophageal cancer referred to our division between 2005 and 2018 were enrolled. We fine-tuned VGG16, an image recognition model of deep learning convolutional neural network (CNN), for the detection of esophageal cancer. We evaluated the diagnostic accuracy of the CNN using a test data set including 46 cancerous CT images and 100 non-cancerous images and compared it to that of two radiologists. Results: Pre-treatment esophageal cancer stages of the patients included in the test data set were clinical T1 (12 patients), clinical T2 (9 patients), clinical T3 (20 patients), and clinical T4 (5 patients). The CNN-based system showed a diagnostic accuracy of 84.2%, F value of 0.742, sensitivity of 71.7%, and specificity of 90.0%. Conclusions: Our AI-based diagnostic system succeeded in detecting esophageal cancer with high accuracy. More training with vast datasets collected from multiples centers would lead to even higher diagnostic accuracy and aid better decision making.

Original languageEnglish
JournalEsophagus
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Computed tomography
  • Convolutional neural network
  • Esophageal cancer

ASJC Scopus subject areas

  • Gastroenterology

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