Knowledge Integration for Disease Characterization: A Breast Cancer Example

TL;DR

This paper presents a semantic technology system that automatically updates a breast cancer staging ontology from authoritative sources, aiding physicians in accurate disease characterization and patient management.

Contribution

It introduces an automated mechanism for integrating new cancer staging guidelines into an ontology, enhancing clinical decision support with minimal human effort.

Findings

Successfully constructed an ontology from authoritative manuals.

Enabled rapid re-staging of patients and cohorts.

Facilitated integration of new guidelines with minimal intervention.

Abstract

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.