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A new natural language processing (NLP) algorithm accurately identifies myxofibrosarcoma patients in VA pathology reports, improving upon traditional ICD codes and word searches for better sarcoma research databases.

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Area of Science:

  • Medical Informatics
  • Oncology
  • Natural Language Processing

Background:

  • International Classification of Diseases (ICD) codes underrepresent soft tissue sarcoma diagnoses.
  • The National VA Database offers a unique resource for sarcoma research due to comprehensive clinical data.
  • Natural Language Processing (NLP) can analyze clinical documents to identify soft tissue sarcoma independently of ICD codes.

Purpose of the Study:

  • Determine the proportion of myxofibrosarcoma patients missed by ICD codes in the VA database.
  • Evaluate the accuracy of a de novo NLP algorithm in identifying myxofibrosarcoma from pathology reports.

Main Methods:

  • Reviewed 10.7 million pathology reports from the VA corporate data warehouse (2003-2022).
  • Developed a gold-standard cohort through manual review of reports containing 'myxofibrosarcoma'.
  • Iteratively refined an NLP algorithm, assessing its sensitivity, specificity, PPV, NPV, and accuracy against the gold standard.

Main Results:

  • 27% of myxofibrosarcoma patients were missed by ICD codes.
  • The NLP algorithm achieved 92% accuracy in identifying myxofibrosarcoma, outperforming ICD codes (73%) and word searches (74%).
  • Final NLP models demonstrated accuracies ranging from 92% to 100%.

Conclusions:

  • A de novo NLP algorithm accurately identifies myxofibrosarcoma from pathology reports.
  • This NLP approach significantly improves upon ICD-based and simple word search methods for cohort creation.
  • The algorithm is publicly available on GitHub to encourage external validation and further development.