Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival

Sunkyu Kim, Choong Kun Lee, Yonghwa Choi, Eun Sil Baek, Jeong Eun Choi, Joon Seok Lim, Jaewoo Kang, Sang Joon Shin

Research output: Contribution to journalArticlepeer-review

Abstract

Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based natural language processing model that analyzes serial magnetic resonance imaging reports of rectal cancer patients and predicts their overall survival. To evaluate the model, a retrospective cohort study of 4,338 rectal cancer patients was conducted. The experimental results revealed that the proposed model utilizing pre-trained clinical linguistic knowledge could predict the overall survival of patients without any structured information and was superior to the carcinoembryonic antigen in predicting survival. The deep-transfer-learning model using free-text radiological reports can predict the survival of patients with rectal cancer, thereby increasing the utility of unstructured medical big data.

Original languageEnglish
Article number747250
JournalFrontiers in Oncology
Volume11
DOIs
Publication statusPublished - 2021 Nov 17

Keywords

  • deep learning
  • MRI
  • natural language processing (NLP)
  • rectal cancer
  • survival prediction

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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