Evidence map›Paper›PMID 41318568›Full record

ArticleHealth and quality of life outcomes2025

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

Elke Rammant, Emile Deman, Valérie Fonteyne, Lindsay Poppe, Renée Bultijnck, Piet Dirix, Gert De Meerleer, Karin Haustermans, Ann Van Hecke, Miguel E Aguado-Barrera and 31 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Health and quality of life outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

41 authors.

Elke RammantDepartment of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium. Elke.rammant@ugent.be.
Emile DemanIDLab, Ghent University - imec, Ghent, Belgium.
Valérie FonteyneDepartment of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium.
Lindsay PoppeDepartment of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium.
Renée BultijnckDepartment of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium.
Piet DirixDepartment of Radiation Oncology, Iridium Network, Antwerp, Belgium.
Gert De MeerleerDepartment of Radiation Oncology, Leuven University Hospitals, Leuven, Belgium.
Karin HaustermansDepartment of Radiation Oncology, Leuven University Hospitals, Leuven, Belgium.
Ann Van HeckeDepartment of Public Health and Primary Care - University Center for Nursing and Midwifery, Ghent University, Ghent, Belgium.
Miguel E Aguado-BarreraInstituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain.
Barbara AvuzziUnit of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
David AzriaMontpellier Cancer Institute ICM, University Federation of Radiation Oncology of Mediterranean Occitanie, Université Montpellier, Montpellier, France.
Jenny Chang-ClaudeDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Barbara N ChiordaUnit of Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Ananya ChoudhuryDivision of Cancer Sciences, The University of Manchester, Christie Hospital NHS Foundation Trust, Manchester, UK.
Patricia Calvo-CrespoInstituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain.
Dirk De RuysscherDepartment of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands.
Antonio Gómez-CaamañoInstituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain.
Philipp HeumannUniversity Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Ashley M HopkinsFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Adelaide, SA, Australia.
Kerstie JohnsonDepartment of Genetics & Cancer Sciences, University of Leicester, Leicester, UK.
Maarten LambrechtDepartment of Radiation Oncology, Leuven University Hospitals, Leuven, Belgium.
Alan McwilliamUniversity Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Bradley D MenzFlinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Bedford Park, Adelaide, SA, Australia.
Filip PoelaertDepartment of Urology, ZAS, Antwerp, Belgium.
Tiziana RancatiData Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
Kato RansDepartment of Radiation Oncology, Leuven University Hospitals, Leuven, Belgium.
Tim RattayLeicester Cancer Research Centre, University of Leicester, Leicester, UK.
Barry S RosensteinDepartments of Radiation Oncology & Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, USA.
Petra SeiboldDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Jane ShortallUniversity Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Elena SperkDepartment of Radiation Oncology, Mannheim Cancer Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Nora SundahlDepartment of Radiation Oncology, AZ Groeninge, Pres. Kennedylaan 4, Kortrijk, 8500, Belgium.
Christopher J TalbotLeicester Cancer Research Centre, University of Leicester, Leicester, UK.
Ana VegaInstituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain.
Peter VermeulenOncologisch Centrum, Universiteit Antwerpen, Antwerpen, Belgium.
Adam WebbDepartment of Genetics and Genome Biology, University of Leicester, Leicester, UK.
Catharine M L WestDivision of Cancer Sciences, The University of Manchester, Christie Hospital NHS Foundation Trust, Manchester, UK.
Liv VeldemanDepartment of Human Structure and Repair, Ghent University, Corneel Heymanslaan 10, Ghent, 9000, Belgium.
Sofie Van HoeckeIDLab, Ghent University - imec, Ghent, Belgium.
REQUITE consortium

Funding

the European Union's Seventh Framework Programme for research, technological development, and demonstration no. 601826
6 · The paper itself

Abstract

PURPOSE/

objectiveProstate 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/

methodsWe 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.

resultsData 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.

conclusionAlthough 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.

Indexed as

Machine LearningPatient Reported Outcome MeasuresProstatic NeoplasmsQuality of LifeAgedFatigueHumansMaleMiddle AgedPredictive Learning ModelsProspective StudiesSurveys and QuestionnairesSymptom BurdenMachine learningPatient-reported outcomesProstate cancerRadiotherapySymptom clusters

Identifiers

PMID41318568
PMCPMC12771884

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.