![SCENT Process Diagram
Clinical Concepts (Excel) Pathology Text (Research Database) [moderately-differentiated ductal adenocarcinoma
with papillary]
Type : Morphology, topology, or procedural Text : Raw text segment from report [features.]
Code : SNOMED 3.X Line : Sequential text segment identifier [the tumor involves 0.6 cm of one core.]
Class : Malignant , basaloid , benign, or N/A
Description : Concept description
[moderately-differentiated ductal adenocarcinoma
Regular Preprocessed with papillary features.]
Code : M-85033 Expressions Text [the tumor involves 0.6 cm of one core.]
Description : intraductal papillary
adenocarcinoma with invasion
moderately differentiated <nlp snm=m85033
[intraductal] type=m class=3>ductal adenocarcinomawith
papillary</nlp snm=m85033> features
Tokenize [papillary] Examine
[adenocarcinoma] Extract Data
Words [with] Segments Disease
[invasion] Extent
Code
Tumor Gleason
[intraductal] Staging Score
Matches Diagnostic
[papillary] Certainty
Clean [adenocarcinoma]
[with] Tokenize [moderately] [differentiated]
[invasion] [ductal] [adenocarcinoma]
Words with [papillary] [features]
[adenocarcinoma[ls]?]
[((intra)?duct(al)?)] [papillar (y|ies)]
Check
Enhance [papillar (y|ies)]
[((intra)?duct(al)?)] Negation
[adenocarcinoma[ls]?]
Loop Match free (of|from)
Concept Dictionary (SAS) Concepts Tokens not? (support[a-z]*|identified)
non(?!small|hodgkins)](https://image.slidesharecdn.com/use-of-sas-based-natural-language-processing-to-identify-incident-and-recurrent-malignancies-strauss4480-120502115255-phpapp01/85/Use-of-SAS-Based-Natural-Language-Processing-to-Identify-Incident-and-Recurrent-Malignancies-STRAUSS-8-320.jpg)
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Use of SAS Based Natural Language Processing to Identify Incident and Recurrent Malignancies STRAUSS
- 1. May 1, 2012 • 2012 HMORN Conference • Seattle, Washington Use of SAS-Based Natural Language Processing to Identify Incident and Recurrent Malignancies Justin A. Strauss, MA Research Associate III Kaiser Permanente Southern California
- 2. Co-Authors & Funding • Chun R. Chao, PhD • Marilyn L. Kwan, PhD • Syed A. Ahmed, MD • Joanne E. Schottinger, MD • Virginia P. Quinn, PhD
- 3. Acknowledgements & Funding • Mayra Martinez, Michelle McGuire, Melissa Preciado, Nirupa Ghai, and Jeff Slezak (KPSC); Lawrence Kushi (KPNC); Debra Ritzwoller (KPCO); Joan Warren (NCI); Jianyu Rao and Jiaoti Huang (UCLA) • Funding was provided by KPSC Community Benefit and the Cancer Research Network
- 4. Malignancy Identification • Malignancy identification is important for clinical and epidemiologic cancer research. • Limited quality and availability of incident and recurrent malignancy data within health plans. • Delayed availability of incident malignancy data from cancer registries. • Few registries track cancer recurrences. • Manual chart abstraction slow and expensive. • Previous research has shown electronic diagnosis codes (e.g., ICD-9) to be unreliable.
- 5. Natural Language Processing • Natural language processing (NLP) can be used to identify and extract information from electronic clinical text, including incident and recurrent malignancy data. • Increasing opportunity for NLP with adoption of electronic clinical systems in patient care delivery. • Despite its potential value in clinical and research settings, NLP usage has been relatively sparse. Contributing factors may include: • Technical complexity • Systems integration requirements • Habitual use of existing methods
- 6. SCENT Overview • A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to identify incident and recurrent malignancies using text from pathology reports. • SCENT is currently being implemented in two research studies at Kaiser Permanente Southern California (KPSC): • Intervention to improve medication adherence among breast cancer patients. • Differences in the prognosis of prostate cancer patients according to their genetic factors • Use of SAS programming minimizes implementation barriers and increases availability for multisite research.
- 7. Description of Methods • SCENT identifies non-negated clinical concepts within pathology report text. • Built using SAS Base (does not require Text Miner add-on). • Makes extensive use of SAS hash objects and regular expressions. • Includes components for preprocessing, matching, negation and uncertainty detection, extracting diagnostic information (e.g., staging and Gleason score), and classifying report malignancy status. • Flexibility to assign codes using variety of coding systems. • Validation used subset of SNOMED 3.x (~1000 concepts).
- 8. SCENT Process Diagram Clinical Concepts (Excel) Pathology Text (Research Database) [moderately-differentiated ductal adenocarcinoma with papillary] Type : Morphology, topology, or procedural Text : Raw text segment from report [features.] Code : SNOMED 3.X Line : Sequential text segment identifier [the tumor involves 0.6 cm of one core.] Class : Malignant , basaloid , benign, or N/A Description : Concept description [moderately-differentiated ductal adenocarcinoma Regular Preprocessed with papillary features.] Code : M-85033 Expressions Text [the tumor involves 0.6 cm of one core.] Description : intraductal papillary adenocarcinoma with invasion moderately differentiated <nlp snm=m85033 [intraductal] type=m class=3>ductal adenocarcinomawith papillary</nlp snm=m85033> features Tokenize [papillary] Examine [adenocarcinoma] Extract Data Words [with] Segments Disease [invasion] Extent Code Tumor Gleason [intraductal] Staging Score Matches Diagnostic [papillary] Certainty Clean [adenocarcinoma] [with] Tokenize [moderately] [differentiated] [invasion] [ductal] [adenocarcinoma] Words with [papillary] [features] [adenocarcinoma[ls]?] [((intra)?duct(al)?)] [papillar (y|ies)] Check Enhance [papillar (y|ies)] [((intra)?duct(al)?)] Negation [adenocarcinoma[ls]?] Loop Match free (of|from) Concept Dictionary (SAS) Concepts Tokens not? (support[a-z]*|identified) non(?!small|hodgkins)
- 9. Sample Report Coding Preprocessed Text LEFT BREAST CORE BIOPSY TWO O CLOCK. <BR> INVASIVE DUCTAL CARCINOMA NOTTINGHAM GRADE 2. <BR> NO CALCIFICATION IS IDENTIFIED. <BR> NO VASCULAR INVASION IS IDENTIFIED. <BR> HORMONE RECEPTOR AND HER 2 NEU STATUS PENDING AN ADDENDUM WILL FOLLOW. Coded Text <NLP SNM=T04030 TYPE=T>LEFT BREAST</NLP SNM=T04030> CORE <NLP SNM=P1140 TYPE=P>BIOPSY</NLP SNM=P1140> TWO O CLOCK. <BR> INVASIVE <NLP SNM=M85003 TYPE=M CLASS=3>DUCTAL CARCINOMA</NLP SNM=M85003> NOTTINGHAM GRADE 2. <BR> NO CALCIFICATION IS IDENTIFIED. <BR> NO VASCULAR INVASION IS IDENTIFIED. <BR> HORMONE RECEPTOR AND HER 2 NEU STATUS PENDING AN ADDENDUM WILL FOLLOW.
- 10. Validation Study • To validate SCENT, trained chart abstractors reviewed electronic pathology reports. • Random samples of breast (n=400) and prostate (n=400) cancer patients. • Patients diagnosed at KPSC between 2000-2007. • Reports included from six months post-diagnosis through end of 2008. • In total, 206 breast and 186 prostate cancer patients contributed 490 and 425 eligible reports, respectively. • SCENT classifications were compared with those of abstractors.
- 11. Classification Concordance Abstractor Classifications Cancer Other Benign Suspicious Recurrence Primary Cancer SCENT Classifications % N % N % N % N Kappa Breast Cancer (Total) (436) (32) (18) (4) Benign 99.8 435 - - - - 25.0 1 0.96 Cancer Recurrence - - 100.0 32 - - - - Other Primary Cancer 0.2 1 - - 100.0 18 50.0 2 Suspicious - - - - - - 25.0 1 Prostate Cancer (Total) (356) (29) (36) (4) Benign 99.4 354 - - 5.6 2 - - 0.95 Cancer Recurrence - - 96.6 28 2.8 1 - - Other Primary Cancer 0.6 2 3.4 1 91.7 33 - - Suspicious - - - - - - 100.0 4 Note: incident contralateral breast malignancies were considered to be recurrences.
- 12. SCENT Performance Metrics Sensitivity* Specificity* PPV* NPV* Breast Cancer 1.00 (0.93-1.00) 0.99 (0.98-1.00) 0.94 (0.85-0.98) 1.00 (0.99-1.00) Prostate Cancer 0.97 (0.89-0.99) 0.99 (0.98-1.00) 0.97 (0.89-0.99) 0.99 (0.98-1.00) * Shown with Wilson's 95% confidence interval.
- 13. Conclusions • Favorable results suggest SCENT can identify and extract information about primary and recurrent malignancies from pathology reports. • Rapid cancer case identification. • Improved measurement accuracy of common study endpoint. • SCENT has the potential to expedite chart reviews by narrowing the search and highlighting relevant concepts. • Generalized utility for extracting standardized disease scores and other clinical information. • SCENT is proof of concept for SAS-based NLP that can be easily shared between institutions to support research.
- 14. Limitations & Next Steps • SCENT has a number of limitations, including: • Unable to disambiguate and contextualize identified clinical concepts without part-of-speech (POS) tagging. • More susceptible to changes in text structure and increased linguistic variability than statistical NLP approaches. • General purpose NLP (e.g., cTAKES) likely to perform better outside of pathology. • Next steps include: • Release SCENT source code and requisite support files. • Optimize current functionality and assess feasibility of adding methods (e.g., POS tagging, n-grams, statistical classifiers). • Attempt to identify non-pathologically diagnosed malignancies using radiology reports and clinical progress notes. • Quantify cost savings associated with SCENT-assisted chart reviews.
- 15. Questions?