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Integrating NLP to Enhance Algorithmic Identification of Metastatic and Castration-Resistant Prostate Cancer in Large

Shannon R Stock1,2, Joshua A Parrish1, Michael T Burns1

  • 1Department of Surgery, Durham VA Health Care System, Durham, North Carolina, USA.

Cancer Medicine
|December 21, 2025
PubMed
Summary
This summary is machine-generated.

Automated algorithms accurately identify advanced prostate cancer (PC) states, including castration-resistant PC (CRPC) and metastatic PC. Integrating natural language processing (NLP) significantly improved metastatic classification accuracy in large-scale population studies.

Keywords:
castration‐resistant prostate cancer detectionclaims data algorithmsmetastatic disease detectionnatural language processing

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Area of Science:

  • Oncology
  • Medical Informatics
  • Health Services Research

Background:

  • Accurate classification of prostate cancer (PC) disease states, specifically metastasis and castration resistance (CRPC), is crucial for population-based research.
  • Current chart review methods are not feasible for large-scale studies, necessitating the development of accurate automated classification methods.

Purpose of the Study:

  • To evaluate the accuracy of automated algorithms for identifying CRPC and metastatic PC in a large patient cohort.
  • To determine the effectiveness of integrating natural language processing (NLP) and treatment patterns with ICD codes for improved classification.

Main Methods:

  • A retrospective study was conducted using Veterans Affairs Health Care System data.
  • Algorithms for CRPC and metastatic PC were evaluated against manual chart review as the gold standard.
  • Methods included ICD codes, NLP, and a novel algorithm combining these with treatment patterns.

Main Results:

  • The CRPC algorithm achieved 85.1% sensitivity and 96.1% specificity.
  • For metastatic disease, the combined ICD codes, treatment patterns, and NLP algorithm showed the highest sensitivity (94.4%) and specificity (93.0%).
  • NLP integration enhanced metastatic classification sensitivity with minimal impact on specificity.

Conclusions:

  • Developed CRPC and metastasis algorithms are effective automated tools for identifying advanced PC states in large populations.
  • Integrating NLP into classification algorithms significantly improves sensitivity for metastatic disease detection.
  • A multifaceted approach combining data sources like ICD codes, treatment patterns, and NLP is valuable for large-scale PC research.