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Purpose/objective <p>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.</p> Material/methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月29日

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  • Elke Rammant,
  • Emile Deman,
  • Valérie Fonteyne,
  • Lindsay Poppe,
  • Renée Bultijnck,
  • Piet Dirix,
  • Gert De Meerleer,
  • Karin Haustermans,
  • Ann Van Hecke,
  • Miguel E. Aguado-Barrera,
  • Barbara Avuzzi,
  • David Azria,
  • Jenny Chang-Claude,
  • Barbara N. Chiorda,
  • Ananya Choudhury,
  • Patricia Calvo-Crespo,
  • Dirk De Ruysscher,
  • Antonio Gómez-Caamaño,
  • Philipp Heumann,
  • Ashley M. Hopkins,
  • Kerstie Johnson,
  • Maarten Lambrecht,
  • Alan Mcwilliam,
  • Bradley D. Menz,
  • Filip Poelaert,
  • Tiziana Rancati,
  • Kato Rans,
  • Tim Rattay,
  • Barry S. Rosenstein,
  • Petra Seibold,
  • Jane Shortall,
  • Elena Sperk,
  • Nora Sundahl,
  • Christopher J. Talbot,
  • Ana Vega,
  • Peter Vermeulen,
  • Adam Webb,
  • Catharine M. L. West,
  • Liv Veldeman,
  • Sofie Van Hoecke

摘要

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.