Dataset Information

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

ABSTRACT:

Objective

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification.

Design

By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage.

Measurements

The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines.

Results

Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches.

Conclusion

A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

SUBMITTER: Nguyen AN

PROVIDER: S-EPMC2995652 | biostudies-literature | 2010 Jul-Aug

REPOSITORIES: biostudies-literature

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Publications

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

Nguyen Anthony N AN Lawley Michael J MJ Hansen David P DP Bowman Rayleen V RV Clarke Belinda E BE Duhig Edwina E EE Colquist Shoni S

Journal of the American Medical Informatics Association : JAMIA 20100701 4

<h4>Objective</h4>To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification.<h4>Design</h4>By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates we ...[more]

PMID: 20595312

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Dataset Information

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

Objective

Design

Measurements

Results

Conclusion

Publications

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

Similar Datasets

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