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Sökning: onr:"swepub:oai:lup.lub.lu.se:160bc376-f64e-4491-aba6-f73cd52bed5b" > Predictive Models f...

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

Lawlor, Ailbhe (författare)
King's College London
Lin, Carol (författare)
Erasmus University Medical Center
Gómez Rivas, Juan (författare)
Hospital Clinico San Carlos de Madrid
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Ibáñez, Laura (författare)
Hospital Clinico San Carlos de Madrid
Abad López, Pablo (författare)
University Hospital La Paz
Willemse, Peter Paul (författare)
University Medical Center Utrecht
Imran Omar, Muhammad (författare)
University of Aberdeen
Remmers, Sebastiaan (författare)
Erasmus University Medical Center
Cornford, Philip (författare)
University of Liverpool
Rajwa, Pawel (författare)
Medical University of Silesia
Nicoletti, Rossella (författare)
University of Florence,Chinese University of Hong Kong
Gandaglia, Giorgio (författare)
Northeastern University,San Raffaele Hospital
Yuen-Chun Teoh, Jeremy (författare)
Chinese University of Hong Kong
Moreno Sierra, Jesús (författare)
Hospital Clinico San Carlos de Madrid
Golozar, Asieh (författare)
Odysseus Data Services,Northeastern University
Bjartell, Anders (författare)
Lund University,Lunds universitet,Avdelningen för translationell cancerforskning,Institutionen för laboratoriemedicin,Medicinska fakulteten,Urologisk cancerforskning, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Division of Translational Cancer Research,Department of Laboratory Medicine,Faculty of Medicine,Urological cancer, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments
Evans-Axelsson, Susan (författare)
N'Dow, James (författare)
Zong, Jihong (författare)
Bayer Corporation, USA
Ribal, Maria J. (författare)
Roobol, Monique J. (författare)
Erasmus University Medical Center
Van Hemelrijck, Mieke (författare)
King's College London
Beyer, Katharina (författare)
Erasmus University Medical Center
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 (creator_code:org_t)
 
2024
2024
Engelska 10 s.
Ingår i: European Urology Open Science. - 2666-1691. ; 63, s. 126-135
  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

Adverse events
Disease progression
Metastatic prostate cancer
Overall survival
Predictive models
Toxicity
Treatment discontinuation
Treatment selection

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