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.
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 cancerpatients 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.
Authors: Iain A McCowan; Darren C Moore; Anthony N Nguyen; Rayleen V Bowman; Belinda E Clarke; Edwina E Duhig; Mary-Jane Fry Journal: J Am Med Inform Assoc Date: 2007-08-21 Impact factor: 4.497
Authors: Anni Coden; Guergana Savova; Igor Sominsky; Michael Tanenblatt; James Masanz; Karin Schuler; James Cooper; Wei Guan; Piet C de Groen Journal: J Biomed Inform Date: 2008-12-27 Impact factor: 6.317
Authors: Timothy Threlfall; Jana Wittorff; Padabphet Boutdara; Jane Heyworth; Paul Katris; Harry Sheiner; Lin Fritschi Journal: Popul Health Metr Date: 2005-08-17
Authors: Anthony N Nguyen; Donna Truran; Madonna Kemp; Bevan Koopman; David Conlan; John O'Dwyer; Ming Zhang; Sarvnaz Karimi; Hamed Hassanzadeh; Michael J Lawley; Damian Green Journal: AMIA Annu Symp Proc Date: 2018-12-05