ArticleCancer medicine2024
Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Natural history study of adults with primary central nervous system tumors: Initial results for first 1000 participants.Neuro-oncology practice · 2026Article
- Personalizing post-stroke symptom management: integrating network analysis and in silico intervention to identify subgroup-specific targets.BMC neurology · 2026Article
- A structural equation modeling framework for estimating symptom burden based on symptom clusters in cancer survivors.Scientific reports · 2026Article
- "When symptoms interact and change": longitudinal network analysis of dynamic symptom changes in oral cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Symptom clusters and core symptoms in early postoperative esophageal cancer patients.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Article
- Longitudinal relationships of malnutrition risk, sleep disturbance, and fear of cancer recurrence in thyroid cancer patients: a cross-lagged panel model.Frontiers in nutrition · 2026Article
- Core preoperative symptoms and patients' symptom experiences in oral cancer: a mixed-methods study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Optimized BERT-based NLP outperforms zero-shot methods for automated symptom detection in clinical practice.Frontiers in digital health · 2025Article
- PRONA: an R-package for Patient Reported Outcomes Network Analysis.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
9 authors.
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Abstract
backgroundInterindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden.
methodsSeverity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression.
resultsNetwork analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership.
conclusionsComputational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.
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