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ABSTRACT Background Interindividual 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. Methods Severity 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. Results Network 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. Conclusions Computational 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.

Abstract Objective High self‐efficacy is associated with improved self‐care and reduced symptoms in cancer patients but has not been fully interrogated in adults with central nervous system (CNS) cancers. We aimed to identify the relationship between self‐efficacy levels in managing emotions (SEMEM) and social interactions (SEMSI) by examining sample characteristics and symptom burden. Methods Sample characteristics and patient‐reported outcome (PRO) measures addressing self‐efficacy (PROMIS SEMEM & SEMSI) and symptom burden (MDASI BT or SP) were collected in a novel web‐based study of 158 adult patients diagnosed with rare CNS tumors. Results The sample was predominantly female (73%), diagnosed with an ependymoma (66%), and had a median age of 45 (19–75). Low SEMEM was associated with a longer duration of symptoms before surgery (r = −0.26) and female gender (92%) among brain tumor (BT) participants and in spinal cord tumors (SCT), those with lower education (r = 0.29). Reporting low SEMSI was associated with being married (42%), lower education (r = 0.22), and a prolonged time with symptoms before surgery (r = 0.29) in those with BTs, with no associations identified in SCT. More severe mood‐related interference (including mood, enjoyment of life, and relationship with others) was associated with lower SEMEM among both locations (r = −0.61 brain, r = −0.28 spine) and SEMSI in BT participants (r = −0.54). Conclusions Low self‐efficacy was linked to a prolonged time between symptom onset and initial surgery, education, gender, and marital status and was associated with higher mood‐related interference. Understanding characteristics associated with low self‐efficacy underscores a need for future studies to tailor interventions that enhance self‐efficacy.