Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer: A Systematic Review

• Published in: European urology open science (Online) Vol. 63; pp. 126 - 135
• Main Authors: Lawlor, Ailbhe, Lin, Carol, Gómez Rivas, Juan, Ibáñez, Laura, Abad López, Pablo, Willemse, Peter-Paul, Imran Omar, Muhammad, Remmers, Sebastiaan, Cornford, Philip, Rajwa, Pawel, Nicoletti, Rossella, Gandaglia, Giorgio, Yuen-Chun Teoh, Jeremy, Moreno Sierra, Jesús, Golozar, Asieh, Bjartell, Anders, Evans-Axelsson, Susan, N'Dow, James, Zong, Jihong, Ribal, Maria J., Roobol, Monique J., Van Hemelrijck, Mieke, Beyer, Katharina
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2024; Elsevier
• Subjects: Adverse events; Disease progression; Metastatic prostate cancer; Overall survival; Predictive models; Review – Prostate Cancer; Toxicity; Treatment discontinuation; Treatment selection; Treatment discontinuation; Adverse events; Treatment selection; Toxicity; Disease progression; Predictive models; Metastatic prostate cancer; Overall survival
• ISSN: 2666-1683; 2666-1691; 2666-1683
• DOI: 10.1016/j.euros.2024.03.012

Existing predictive models for men diagnosed with metastatic prostate cancer are not suitable for use due to a lack of model performance and external validation. There remains a need for high-quality predictive models to guide treatment selection in clinical practice. The treatment landscape of metastatic prostate cancer (mPCa) has evolved significantly over the past two decades. Despite this, the optimal therapy for patients with mPCa has not been determined. This systematic review identifies available predictive models that assess mPCa patients’ response to treatment. We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. Only quantitative studies in English were included with no time restrictions. The quality of the included studies was assessed using the PROBAST tool. Data were extracted following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews criteria. The search identified 616 citations, of which 15 studies were included in our review. Nine of the included studies were validated internally or externally. Only one study had a low risk of bias and a low risk concerning applicability. Many studies failed to detail model performance adequately, resulting in a high risk of bias. Where reported, the models indicated good or excellent performance. Most of the identified predictive models require additional evaluation and validation in properly designed studies before these can be implemented in clinical practice to assist with treatment decision-making for men with mPCa. In this review, we evaluate studies that predict which treatments will work best for which metastatic prostate cancer patients. We found that existing studies need further improvement before these can be used by health care professionals.