TY - JOUR
T1 - Using machine learning to predict patient-reported symptom clusters in prostate cancer patients receiving radiotherapy
AU - Rammant, Elke
AU - Deman, Emile
AU - Fonteyne, Valerie
AU - Poppe, Lindsay
AU - Bultijnck, Renee
AU - Dirix, Piet
AU - De Meerleer, Gert
AU - Haustermans, Karin
AU - Van Hecke, Ann
AU - Aguado-Barrera, Miguel E.
AU - Avuzzi, Barbara
AU - Azria, David
AU - Chang-Claude, Jenny
AU - Chiorda, Barbara N.
AU - Choudhury, Ananya
AU - Calvo-Crespo, Patricia
AU - De Ruysscher, Dirk
AU - Gomez-Caamano, Antonio
AU - Heumann, Philipp
AU - Hopkins, Ashley M.
AU - Johnson, Kerstie
AU - Lambrecht, Maarten
AU - Mcwilliam, Alan
AU - Menz, Bradley D.
AU - Poelaert, Filip
AU - Rancati, Tiziana
AU - Rans, Kato
AU - Rattay, Tim
AU - Rosenstein, Barry S.
AU - Seibold, Petra
AU - Shortall, Jane
AU - Sperk, Elena
AU - Sundahl, Nora
AU - Talbot, Christopher J.
AU - Vega, Ana
AU - Vermeulen, Peter
AU - Webb, Adam
AU - West, Catharine M. L.
AU - Veldeman, Liv
AU - Van Hoecke, Sofie
AU - REQUITE consortium
PY - 2025/11/29
Y1 - 2025/11/29
N2 - Purpose/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.
AB - Purpose/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.
KW - Prostate cancer
KW - Radiotherapy
KW - Symptom clusters
KW - Patient-reported outcomes
KW - Machine learning
KW - QUALITY-OF-LIFE
KW - QLQ-C30
U2 - 10.1186/s12955-025-02460-1
DO - 10.1186/s12955-025-02460-1
M3 - Article
SN - 1477-7525
VL - 24
JO - Health and Quality of Life Outcomes
JF - Health and Quality of Life Outcomes
IS - 1
M1 - 3
ER -