JMIR Publications

ABSTRACT

Background:

Advances in cancer treatments have significantly improved patient outcome. However, cancer patients (CPs) and survivors often experience complex multidimensional symptoms responsible for a decreased quality of life (QoL). Different models have been investigated to understand symptom cooccurrence and underlying pathophysiological mechanisms to guide the development of innovative therapeutic strategies to improve CPs’ QoL. Whereas previous models achieved mitigated results, the network analysis approach seems to offer recent advancements. This article aims to report the current knowledge regarding the contribution of network analysis to explore and predict the complexity of multidimensional symptom experience in CPs.

Methods:

A systematic search was performed in four databases (Medline, Embase, Google Scholar and Scopus), from 2010 to 2024, and included all the articles that used the network analysis to evaluate symptoms or symptom clusters in adult CPs, at different stages of the disease management.

Results:

Twenty-two studies reporting different symptom networks during the acute and post-acute treatment periods were included. The main challenge in drawing a general synthesis of the evidence is the heterogeneity of the study characteristics, which argues for further standardized research. Discussion: Network analysis could offer new perspectives, notably in identifying core symptoms in CPs as targets for therapeutic interventions, in exploring the relationship between biological parameters and symptoms, and in generating predictive models and personalized management.