Background: Colorectal cancer (CRC) is a heterogeneous disease with different responses to targeted therapies due to various factors, and the treatment effect differs significantly between individuals. Personalize medical treatment (PMT) is a method that takes individual patient characteristics into consideration, making it the most effective way to deal with this issue. Patient similarity and clustering analysis is an important aspect of PMT. This paper describes how to build a knowledge base using formal concept analysis (FCA), which clusters patients based on their similarity and preserves the relations between clusters in hierarchical structural form.
Methods: Prognostic factors (attributes) of 2442 CRC patients, including patient age, cancer cell differentiation, lymphatic invasion and metastasis stages were used to build a formal context in FCA. A concept was defined as a set of patients with their shared attributes. The formal context was formed based on the similarity scores between each concept identified from the dataset, which can be used as a knowledge base.
Results: A hierarchical knowledge base was constructed along with the clinical records of the diagnosed CRC patients. For each new patient, a similarity score to each existing concept in the knowledge base can be retrieved with different similarity calculations. The ranked similarity scores that are associated with the concepts can offer references for treatment plans.
Conclusions: Patients that share the same concept indicates the potential similar effect from same clinical procedures or treatments. In conjunction with a clinician's ability to undergo flexible analyses and apply appropriate judgement, the knowledge base allows faster and more effective decisions to be made for patient treatment and care.
Keywords: Colorectal cancer; Concept retrieval; Formal concept analysis; Knowledge base; Patient similarity.
© 2021. The Author(s).