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Extraction of Treatments and Responses From Non-Small Cell Lung Cancer Clinical Notes Using Natural Language

Sonish Sivarajkumar1, Subhash Edupuganti2, David Lazris2,3

  • 1Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA.

JCO Clinical Cancer Informatics
|January 8, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a natural language processing (NLP) system to automatically extract cancer treatments and patient responses from clinical notes, improving real-world evidence (RWE) generation for non-small cell lung cancer (NSCLC). The system achieved high accuracy in linking treatments with outcomes.

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Area of Science:

  • Computational oncology
  • Biomedical informatics
  • Natural Language Processing (NLP)

Background:

  • Manual extraction of cancer treatment outcomes from clinical notes is time-consuming and challenging for real-world evidence (RWE) generation.
  • Automating this process is crucial for efficient analysis of oncology data.

Purpose of the Study:

  • To develop and validate a robust NLP system for automatically extracting cancer treatments and RECIST-based response categories from non-small cell lung cancer (NSCLC) clinical notes.
  • To assess the system's performance in linking treatments with patient responses.

Main Methods:

  • A retrospective study using 250 annotated NSCLC oncology notes.
  • An end-to-end NLP pipeline combining rule-based entity extraction and a machine learning model (biomedical clinical BERT) for relation classification.
  • Performance evaluation on a held-out test set and partial external validation.

Main Results:

  • The NLP system demonstrated high accuracy, with an F1 score of 0.92 for relation classification and a macro-averaged F1 score of 0.87 for entity extraction on the UPMC test set.
  • High precision was observed for chemotherapy and most response types.
  • External validation showed moderate relation extraction performance (F1: 0.51-0.64).

Conclusions:

  • The developed NLP system reliably extracts structured treatment and response information from unstructured NSCLC oncology notes with high accuracy.
  • This automated approach facilitates the abstraction of critical cancer treatment outcomes, streamlining RWE generation in oncology.