Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer : A Systematic Review
(2024) In European Urology Open Science 63. p.126-135- Abstract
Background and objective: 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. Methods: 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... (More)
Background and objective: 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. Methods: 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. Key findings and limitations: 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. Conclusions and clinical implications: 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. Patient summary: 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.
(Less)
- author
- author collaboration
- organization
- publishing date
- 2024-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adverse events, Disease progression, Metastatic prostate cancer, Overall survival, Predictive models, Toxicity, Treatment discontinuation, Treatment selection
- in
- European Urology Open Science
- volume
- 63
- pages
- 10 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:38596781
- scopus:85189533807
- ISSN
- 2666-1691
- DOI
- 10.1016/j.euros.2024.03.012
- language
- English
- LU publication?
- yes
- id
- 160bc376-f64e-4491-aba6-f73cd52bed5b
- date added to LUP
- 2024-04-24 14:37:58
- date last changed
- 2024-06-05 18:57:23
@article{160bc376-f64e-4491-aba6-f73cd52bed5b,
abstract = {{<p>Background and objective: 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. Methods: 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. Key findings and limitations: 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. Conclusions and clinical implications: 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. Patient summary: 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.</p>}},
author = {{Lawlor, Ailbhe and Lin, Carol and Gómez Rivas, Juan and Ibáñez, Laura and Abad López, Pablo and Willemse, Peter Paul and Imran Omar, Muhammad and Remmers, Sebastiaan and Cornford, Philip and Rajwa, Pawel and Nicoletti, Rossella and Gandaglia, Giorgio and Yuen-Chun Teoh, Jeremy and Moreno Sierra, Jesús and Golozar, Asieh and Bjartell, Anders and Evans-Axelsson, Susan and N'Dow, James and Zong, Jihong and Ribal, Maria J. and Roobol, Monique J. and Van Hemelrijck, Mieke and Beyer, Katharina}},
issn = {{2666-1691}},
keywords = {{Adverse events; Disease progression; Metastatic prostate cancer; Overall survival; Predictive models; Toxicity; Treatment discontinuation; Treatment selection}},
language = {{eng}},
pages = {{126--135}},
publisher = {{Elsevier}},
series = {{European Urology Open Science}},
title = {{Predictive Models for Assessing Patients’ Response to Treatment in Metastatic Prostate Cancer : A Systematic Review}},
url = {{http://dx.doi.org/10.1016/j.euros.2024.03.012}},
doi = {{10.1016/j.euros.2024.03.012}},
volume = {{63}},
year = {{2024}},
}