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. 2013 Mar-Apr;20(2):349-55.
doi: 10.1136/amiajnl-2012-000928. Epub 2012 Jul 21.

Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

Justin A Strauss  1 , Chun R Chao, Marilyn L Kwan, Syed A Ahmed, Joanne E Schottinger, Virginia P Quinn
Affiliations

Identifying primary and recurrent cancers using a SAS-based natural language processing algorithm

Justin A Strauss et al. J Am Med Inform Assoc. 2013 Mar-Apr.

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.

Materials and 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.

Discussion: 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.

Conclusion: 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.

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Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Process diagram for a SAS-based coding, extraction, and nomenclature tool (SCENT).
Figure 2
Figure 2
Sample pathology report text following preprocessing by a SAS-based coding, extraction, and nomenclature tool (SCENT).
Figure 3
Figure 3
Sample pathology report text following preprocessing and code assignment by a SAS-based coding, extraction, and nomenclature tool (SCENT).
Figure 4
Figure 4
Sample chart review form used by abstractors to classify the pathology reports of breast and prostate cancer patients.
See this image and copyright information in PMC

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