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Using machine learning to predict patient-reported symptom clusters in prostate cancer patients receiving radiotherapy

 
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid0000-0001-5738-4293
cris.virtual.orcid0000-0002-7865-6793
cris.virtualsource.departmentf28bbc7e-df56-4771-8176-bd9f9790c319
cris.virtualsource.department43fd6f27-126a-4a10-8c2e-2c15e86e4898
cris.virtualsource.orcidf28bbc7e-df56-4771-8176-bd9f9790c319
cris.virtualsource.orcid43fd6f27-126a-4a10-8c2e-2c15e86e4898
dc.contributor.authorRammant, Elke
dc.contributor.authorDeman, Emile
dc.contributor.authorFonteyne, Valerie
dc.contributor.authorPoppe, Lindsay
dc.contributor.authorBultijnck, Renee
dc.contributor.authorDirix, Piet
dc.contributor.authorDe Meerleer, Gert
dc.contributor.authorHaustermans, Karin
dc.contributor.authorVan Hecke, Ann
dc.contributor.authorAguado-Barrera, Miguel E.
dc.contributor.authorAvuzzi, Barbara
dc.contributor.authorAzria, David
dc.contributor.authorChang-Claude, Jenny
dc.contributor.authorChiorda, Barbara N.
dc.contributor.authorChoudhury, Ananya
dc.contributor.authorCalvo-Crespo, Patricia
dc.contributor.authorDe Ruysscher, Dirk
dc.contributor.authorGomez-Caamano, Antonio
dc.contributor.authorHeumann, Philipp
dc.contributor.authorHopkins, Ashley M.
dc.date.accessioned2026-07-28T12:35:20Z
dc.date.available2026-07-28T12:35:20Z
dc.date.issued2026
dc.description.abstractPURPOSE/OBJECTIVE: Prostate cancer (PC) survivors frequently experience multiple co-occurring symptoms that adversely affect health-related quality of life (HRQoL). Identifying symptom clusters (SCs) may help to improve symptom management and patient care. The aim of this study is to investigate (1) SCs in PC patients, (2) associations of SCs with HRQoL, and (3) predictors of SCs. MATERIAL/METHODS: We used data from an international, multi-centre, prospective cohort study (REQUITE). SCs were identified from patient-reported outcomes collected with the EORTC Core Quality of Life questionnaire (EORTC QLQ-C30) and pelvic symptom questionnaires. Machine learning techniques identified SCs, associations with HRQoL and SCs predictors. The dataset was divided into training (80%) and validation (20%) cohorts. RESULTS: Data were analysed from 1538 (before radiotherapy (T0)), 1490 (end of radiotherapy (T1)), 1322 (12-months (T2)), and 1219 (24-months (T3)) patients. SCs identified at T0: SC1 (gastro-intestinal), SC2 (fatigue, urinary, emotional and cognitive functioning), and SC3 (pain, physical, role, and social functioning). SCs changed at T1: SC1 (gastro-intestinal symptoms), SC2 (fatigue, urinary problems, insomnia), SC3 (social and role functioning), and SC4 (pain, bowel problems, physical, emotional, and cognitive functioning). At T2, symptoms returned to baseline clusters. SCs including ‘fatigue’ or ‘urinary symptoms’ were most frequent across time-points. At T0, T2 and T3, HRQoL was best predicted by clusters 2 and 3 (35–45% explained variance). At T1, cluster 4 was the best predictor (52% explained variance). Planned radiotherapy target volume, prostate specific antigen (PSA) at pre-diagnostic biopsy, age and alcohol consumption were the best predictors of SC2 at T1 and SC3 and fatigue-dyspnoea at T3. CONCLUSION: Although SCs including fatigue and urinary symptoms were most common, the ‘pain, bowel problems, physical, emotional and cognitive functioning’ SC at T1 was associated most strongly with HRQoL. The predictors can help to identify men at risk for specific SCs.
dc.identifier.doi10.1186/s12955-025-02460-1
dc.identifier.issn1477-7525
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/60029
dc.language.iso1
dc.provenance.editstepusergreet.vanhoof@imec.be
dc.publisherBMC
dc.relation.ispartofHEALTH AND QUALITY OF LIFE OUTCOMES
dc.relation.ispartofseriesHEALTH AND QUALITY OF LIFE OUTCOMES
dc.source.beginpage3
dc.source.issue1
dc.source.journalHealth and Quality Outcomes
dc.source.numberofpages11
dc.source.volume24
dc.subjectQUALITY-OF-LIFE
dc.subjectQLQ-C30
dc.subjectProstate cancer
dc.subjectRadiotherapy
dc.subjectSymptom clusters
dc.subjectPatient-reported outcomes
dc.subjectMachine learning
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.title

Using machine learning to predict patient-reported symptom clusters in prostate cancer patients receiving radiotherapy

dc.typeJournal article
dspace.entity.typePublication
oaire.citation.editionWOS.SCI
oaire.citation.editionWOS.SSCI
oaire.citation.issue1
oaire.citation.volume24
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