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Developing and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish.

Fabián Villena1,2,3, Pablo Báez4,5, Sergio Peñafiel6

  • 1Department of Computer Sciences, Faculty of Physical and Mathematical Sciences, Universidad de Chile, Región Metropolitana, Chile.

JCO Clinical Cancer Informatics
|February 24, 2025
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Summary

This study introduces an automated system for coding cancer pathology reports, improving efficiency and accuracy in cancer registry data collection. The system uses natural language processing to extract tumor information and suggest International Classification of Diseases for Oncology (ICD-O) codes.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • Pathology reports are crucial for cancer registries but manual coding is time-consuming.
  • Automating the extraction of information from unstructured pathology reports is a significant challenge.

Purpose of the Study:

  • To develop and validate a two-step automatic coding system for pathology reports.
  • To recognize tumor morphology and topography mentions and suggest International Classification of Diseases for Oncology (ICD-O) codes in Spanish.

Main Methods:

  • Created an annotated corpus of 1,101 documents for tumor morphology and topography mentions.
  • Implemented a named entity recognition (NER) model using bidirectional LSTM-CRF with transfer learning from pretrained language models.
  • Utilized a search engine tailored to ICD-O codes for subsequent coding.

Main Results:

  • NER models achieved F1 scores of 0.86 for morphology and 0.90 for topography.
  • The automatic coding system achieved an accuracy of 0.72 (morphology) and 0.65 (topography) at five suggestions.

Conclusions:

  • Natural language processing tools can be feasibly implemented in cancer centers for routine pathology report coding.
  • The developed recommender system offers reliable and transparent coding at the point of consultation.
  • The study provides an annotated corpus, guidelines, and source code for reproducibility.