title: Multi-class classification of cancer stages from free-text histology reports using support vector machines.
abstract: Multi-class machine learning techniques using support vector machines (SVM) are proposed to classify the TNM stage of lung cancer patients from analysis of their free-text histology reports. Stages obtained automatically can be used for retrospective population-level studies of lung cancer outcomes. While the system could in principle be applied to stage different cancer types, the paper focuses on staging lung cancer due to data availability. Experiments have quantified system performance on a corpus of reports from 710 lung cancer patients using four different SVM architectures for multi-class classification. Results show that a system based on standard binary SVM classifiers organised in a hierarchical architecture show the most promise with overall accuracy results of 0.64 and 0.82 across T and N stages, respectively.
authors.affiliation: CSIRO e-Health Research Centre, Brisbane, Australia. Anthony.Nguyen@csiro.au
authors.forename: Anthony
authors.initials: A
authors.lastname: Nguyen
authors.forename: Darren
authors.initials: D
authors.lastname: Moore
authors.forename: Iain
authors.initials: I
authors.lastname: McCowan
authors.forename: Mary-Jane
authors.initials: MJ
authors.lastname: Courage
mesh_terms: D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006652:Histological Techniques / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D009367:Neoplasm Staging; D009369:Neoplasms / Q000145:classification / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D018875:Vocabulary, Controlled