title: Representing and extracting lung cancer study metadata: study objective and study design.
abstract: This paper describes the information retrieval step in Casama (Contextualized Semantic Maps), a project that summarizes and contextualizes current research papers on driver mutations in non-small cell lung cancer. Casama׳s representation of lung cancer studies aims to capture elements that will assist an end-user in retrieving studies and, importantly, judging their strength. This paper focuses on two types of study metadata: study objective and study design. 430 abstracts on EGFR and ALK mutations in lung cancer were annotated manually. Casama׳s support vector machine (SVM) automatically classified the abstracts by study objective with as much as 129% higher F-scores compared to PubMed׳s built-in filters. A second SVM classified the abstracts by epidemiological study design, suggesting strength of evidence at a more granular level than in previous work. The classification results and the top features determined by the classifiers suggest that this scheme would be generalizable to other mutations in lung cancer, as well as studies on driver mutations in other cancer domains.
authors.affiliation: Department of Bioengineering, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA. Electronic address: jigarcia@ucla.edu.
authors.forename: Jean I
authors.initials: JI
authors.lastname: Garcia-Gathright
authors.affiliation: Department of Radiological Sciences, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA.
authors.forename: Andrea
authors.initials: A
authors.lastname: Oh
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: Phillip A
authors.initials: PA
authors.lastname: Abarca
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: Mary
authors.initials: M
authors.lastname: Han
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: William
authors.initials: W
authors.lastname: Sago
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: Marshall L
authors.initials: ML
authors.lastname: Spiegel
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Wolf
authors.affiliation: Department of Medicine - Division of Hematology-Oncology, University of California, 924 Westwood Boulevard, Suite 200, Los Angeles, CA 90024, USA.
authors.forename: Edward B
authors.initials: EB
authors.lastname: Garon
authors.affiliation: Department of Bioengineering, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA; Department of Radiological Sciences, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA.
authors.forename: Alex A T
authors.initials: AA
authors.lastname: Bui
authors.affiliation: Department of Bioengineering, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA; Department of Radiological Sciences, University of California, 924 Westwood Boulevard, Suite 420, Los Angeles, CA 90024, USA.
authors.forename: Denise R
authors.initials: DR
authors.lastname: Aberle
mesh_terms: D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D008175:Lung Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000628:therapy; D009154:Mutation; D011379:Prognosis; D012372:ROC Curve; D012107:Research Design; D060388:Support Vector Machine
keywords: Automatic summarization; Information retrieval; Quality of evidence