title: Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm.
abstract: OBJECTIVE
  Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem.
  
  
  METHODS
  SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are analyzed and coded using a dictionary of clinical concepts and associated SNOMED codes. To assess the accuracy of SCENT, validation was conducted using manual review of pathology reports from a random sample of 400 breast and 400 prostate cancer patients diagnosed at Kaiser Permanente Southern California. Trained abstractors classified the malignancy status of each report.
  
  
  RESULTS
  Classifications of SCENT were highly concordant with those of abstractors, achieving κ of 0.96 and 0.95 in the breast and prostate cancer groups, respectively. SCENT identified 51 of 54 new primary and 60 of 61 recurrent cancer cases across both groups, with only three false positives in 792 true benign cases. Measures of sensitivity, specificity, positive predictive value, and negative predictive value exceeded 94% in both cancer groups.
  
  
  CONCLUSIONS
  Favorable validation results suggest that SCENT can be used to identify, extract, and code information from pathology report text. Consequently, SCENT has wide applicability in research and clinical care. Further assessment will be needed to validate performance with other clinical text sources, particularly those with greater linguistic variability.
  
  
  CONCLUSIONS
  SCENT is proof of concept for SAS-based natural language processing applications that can be easily shared between institutions and used to support clinical and epidemiologic research.
authors: Kaiser Permanente Southern California, Research and Evaluation, Pasadena, California, USA.
authors: Justin A
authors: JA
authors: Strauss
authors: Chun R
authors: CR
authors: Chao
authors: Marilyn L
authors: ML
authors: Kwan
authors: Syed A
authors: SA
authors: Ahmed
authors: Joanne E
authors: JE
authors: Schottinger
authors: Virginia P
authors: VP
authors: Quinn
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D001943:Breast Neoplasms / Q000453:epidemiology / Q000473:pathology; D002140:California / Q000453:epidemiology; D057225:Data Mining; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D033181:Information Dissemination; D008297:Male; D009323:Natural Language Processing; D009369:Neoplasms / Q000453:epidemiology* / Q000473:pathology; D011471:Prostatic Neoplasms / Q000453:epidemiology / Q000473:pathology; D012008:Recurrence; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D039061:Systematized Nomenclature of Medicine