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Related Concept Videos

Metastasis02:30

Metastasis

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Metastasis is the spread of cancer cells from the original site to distant locations in the body. Cancer cells can spread via blood vessels (hematogenous) as well as lymph vessels in the body.
Epithelial-to-Mesenchymal Transition
The epithelial-to-mesenchymal transition or EMT is a developmental process commonly observed in wound healing, embryogenesis, and cancer metastasis. EMT is induced by transforming growth factor-beta (TGF-β) or receptor tyrosine kinase (RTK) ligands, which further...
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Using Electronic Health Records to Classify Cancer Site and Metastasis.

Kurt Kroenke1,2, Kathryn J Ruddy3, Deirdre R Pachman4

  • 1Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, United States.

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|June 18, 2025
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Summary
This summary is machine-generated.

Identifying cancer site and metastasis in electronic health records (EHR) is crucial for cancer symptom management. This study compared EHR data methods, finding pragmatic approaches feasible but needing refinement for precise variable use.

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Area of Science:

  • Oncology
  • Health Informatics
  • Clinical Research

Background:

  • The Enhanced EHR-facilitated Cancer Symptom Control (E2C2) Trial aims to improve cancer symptom management through collaborative care.
  • Accurate identification of cancer site and metastatic status is essential but challenging using electronic health records (EHR).

Purpose of the Study:

  • To compare the effectiveness of three distinct approaches for determining cancer site using EHR and cancer registry data.
  • To evaluate six different strategies for identifying metastasis, including International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes and natural language processing (NLP).

Main Methods:

  • The study analyzed data from 50,559 patients within a large health system's medical oncology clinics.
  • Cancer site was determined using single, two, or all prevalent ICD-10 codes.
  • Metastasis identification involved ICD-10 codes, NLP, cancer registry data, medication data, treatment plans, and Phase 1 trial evaluations.

Main Results:

  • Using the two most prevalent ICD-10 cancer site codes identified 92% of cases compared to using all codes, while the single most prevalent code identified 65%.
  • Agreement among cancer site determination methods was high (kappa > 0.80).
  • ICD-10 codes and NLP showed the highest agreement (kappa = 0.53) for metastasis identification, applicable to the entire cohort; cancer registry data was less accessible.

Conclusions:

  • Electronic health record data can be pragmatically used to identify cancer site and metastatic disease in a large, diverse patient population.
  • While feasible for covariates, these EHR-based methods may require further refinement for use as key dependent or independent variables in clinical research.