Ontology Representation for Cholangiocarcinoma

Introduction: Cholangiocarcinoma is a critical public health problem in Thailand. Several research projects have been conducted and data related to CCA have been collected to solve this problem. Data about CCA are found in varied sources such as research-based databases and electronic health records that have been collected and stored using different methods and standards. The objective of this study is to develop the Cholangiocarcinoma Ontology (CCAO) to describe findings related to cholangiocarcinoma in a structured and standardized way in order to integrate and analyze data from these diverse sources. Methods: CCAO has been developed based on data collection forms (CCA forms) of the Cholangiocarcinoma Screening and Care Program (CASCAP). The forms contain data elements about demographics, ultrasound findings, confirmatory diagnoses, final staging diagnoses, and post-operative and follow-up outcomes. These data elements were used to search the Ontobee web browser for matching ontology classes in existing ontologies. Ontology classes from various sources were extracted using a ROBOT tool and imported to CCAO, and new CCAO classes for unmatched classes were added to CCAO manually. CCAO is an application ontology beneath the Basic Formal Ontology (BFO) along with the Ontology of General Medical Science (OGMS), the Information Artifact Ontology (IAO), and the Ontology for Biomedical Investigations (OBI). Results: Based on the CCA forms we developed 210 novel CCAO classes and created 108 CCAO classes based on NCI Thesaurus classes. We reused classes from various domain ontologies including the Phenotype And Trait Ontology (PATO), the Ontology of Biological Attributes (OBA), the Cell Ontology (CL), the Ontology of Medically Related Social Entities (OMRSE), and Drug Ontology (DRON). Imported classes in CCAO were reorganized under the top-level classes such as OGMS:‘clinical finding’, OGMS:‘disorder’, and OBI:‘conclusion based on data’. Moreover, we generated logical definitions for many CCAO classes. Conclusion: CCAO is reusable, interoperable, and easily integrated with related datasets, as well as being human and machine readable. It is compatible with future expansion to represent relevant evidence and knowledge that is not be part of this initial version. CCAO is publicly available on Github (https://github.com/Buffalo-Ontology-Group/CCA-Ontology).