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Updated: Aug 10, 2026
A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
Published on: March 6, 2018
Prediction models for adverse events from prostate cancer curative radiotherapy: a systematic review of
Claudia Cruz Oliveira1, Christian A M Jongen2, Ana Mikolić1
1Department of Public Health, Erasmus MC University Medical Center, Dr. Molewaterplein 40, 3015 GD Rotterdam, Rotterdam, The Netherlands.
Background:
Predicting risks of urinary, bowel, sexual, and other adverse effects following prostate cancer curative radiotherapy (PCa-RT) is essential for treatment personalization and patient counseling. Several clinical prediction models (CPMs) have been published; however, their methodological quality remains unclear.
Methods:
We systematically reviewed studies developing or validating CPMs for adverse events after PCa-RT. Embase and Medline were searched for CPM studies for patient- or clinician-reported outcomes (PROs/ClinROs), published between January 1, 2004, and August 6, 2024. To focus the appraisal on methodologically more robust models, models were pre-selected based on events per variable ≥10 or the reporting of optimism-corrected performance metrics or effect estimates. We appraised models based on performance and ROB, using a six-item short form of the Prediction model Risk Of Bias ASsessment Tool (SF-PROBAST).
Results:
Of 3606 records screened, 136 CPM studies were identified, and 35 were included, yielding 107 CPMs. Most models (n = 87) were developed in external beam radiation therapy populations. 22 models were externally validated. Only two models (AUC= 0.59 and 0.80) - developed in two different studies - were classified as low risk of bias (fulfilled all the SF-PROBAST criteria). 32 models, from 13 studies, met at least four SF-PROBAST criteria and showed at least moderate discrimination (AUC ≥ 0.70) at internal (n = 25) and/or external (n = 11) validation.
Conclusion:
Ready-to-use CPMs in PCa-RT remain scarce due to methodological limitations, miscalibration, and lack of external validation. Future efforts should prioritize validation and refinement of existing models rather than development of new ones.
