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Related Concept Videos

Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm.

Justin A Strauss1, Chun R Chao, Marilyn L Kwan

  • 1Kaiser Permanente Southern California, Research and Evaluation, Pasadena, California, USA.

Journal of the American Medical Informatics Association : JAMIA
|July 24, 2012
PubMed
Summary
This summary is machine-generated.

A new tool, SCENT (SAS-based coding, extraction, and nomenclature tool), accurately identifies primary and recurrent cancers from pathology reports. This SAS-based natural language processing application aids clinical and epidemiologic research.

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Area of Science:

  • Medical Informatics
  • Computational Pathology
  • Natural Language Processing

Background:

  • Timely and complete identification of primary and recurrent cancers is crucial for clinical and epidemiologic research but faces significant limitations.
  • Existing methods for cancer identification from pathology reports can be time-consuming and incomplete.

Purpose of the Study:

  • To develop and validate a SAS-based tool named SCENT (SAS-based coding, extraction, and nomenclature tool) for automated identification and extraction of cancer information from electronic pathology reports.
  • To address the limitations in timely and complete cancer identification for research purposes.

Main Methods:

  • SCENT utilizes hierarchical classification rules and a dictionary of clinical concepts with SNOMED codes to analyze and code pathology report text.
  • Validation involved manual review of pathology reports from 800 cancer patients (400 breast, 400 prostate) by trained abstractors.
  • Accuracy was assessed by comparing SCENT classifications with manual abstractor classifications.

Main Results:

  • SCENT demonstrated high concordance with manual abstractor classifications, achieving kappa values of 0.96 for breast cancer and 0.95 for prostate cancer.
  • The tool achieved high accuracy in identifying new primary and recurrent cancer cases, with sensitivity, specificity, and predictive values exceeding 94% across both cancer groups.
  • SCENT showed a low false positive rate, correctly classifying 792 benign cases with only three false positives.

Conclusions:

  • The validation results support SCENT's capability to accurately identify, extract, and code information from pathology report text.
  • SCENT has broad applicability in supporting clinical and epidemiologic research and can be shared between institutions.
  • Further validation is recommended for other clinical text sources with greater linguistic variability.