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DOI 10.1186/s12955-025-02460-1

Медицинская библиотека · Health and quality of life outcomes · 2025

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

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

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Аннотация

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.

Для цитирования: Rammant Elke, Deman Emile, Fonteyne Valérie, Poppe Lindsay, Bultijnck Renée, Dirix Piet, De Meerleer Gert, Haustermans Karin, Van Hecke Ann, Aguado-Barrera Miguel E, Avuzzi Barbara, Azria David, Chang-Claude Jenny, Chiorda Barbara N, Choudhury Ananya, Calvo-Crespo Patricia, De Ruysscher Dirk, Gómez-Caamaño Antonio, Heumann Philipp, Hopkins Ashley M, Johnson Kerstie, Lambrecht Maarten, Mcwilliam Alan, Menz Bradley D, Poelaert Filip, Rancati Tiziana, Rans Kato, Rattay Tim, Rosenstein Barry S, Seibold Petra, Shortall Jane, Sperk Elena, Sundahl Nora, Talbot Christopher J, Vega Ana, Vermeulen Peter, Webb Adam, West Catharine M L, Veldeman Liv, Van Hoecke Sofie, REQUITE consortium. Using machine learning to predict patient-reported symptom clusters in prostate cancer patients receiving radiotherapy.. Health and quality of life outcomes. 2025. DOI: 10.1186/s12955-025-02460-1.

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