Dataset Information

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

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 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.

SUBMITTER: Lawlor A

PROVIDER: S-EPMC11001619 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature

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Publications

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

Lawlor Ailbhe A Lin Carol C Gómez Rivas Juan J Ibáñez Laura L Abad López Pablo P Willemse Peter-Paul PP Imran Omar Muhammad M Remmers Sebastiaan S Cornford Philip P Rajwa Pawel P Nicoletti Rossella R Gandaglia Giorgio G Yuen-Chun Teoh Jeremy J Moreno Sierra Jesús J Golozar Asieh A Bjartell Anders A Evans-Axelsson Susan S N'Dow James J Zong Jihong J Ribal Maria J MJ Roobol Monique J MJ Van Hemelrijck Mieke M Beyer Katharina K

European urology open science 20240404

<h4>Background and objective</h4>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.<h4>Methods</h4>We critically reviewed MEDLINE and CENTRAL in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses ...[more]

PMID: 38596781

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Project description:Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: 'magnetic resonance imaging (MRI)', 'radiomics', and 'stroke'. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75-0.86) to an AUC of 0.92 (95% CI, 0.87-0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients' disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies' findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment.

Project description:Recently, checkpoint inhibitors have been investigated in metastatic prostate cancer, however their overall effect is unclear and needs to be further investigated. The aim of this systematic review is to investigate the oncological response of metastatic castration-resistant prostate cancer patients to immune checkpoint inhibitors. Based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement, a systematic review of the literature was conducted through online electronic databases and the American Society of Clinical Oncology (ASCO) Meeting Library. Eligible publications were selected after a staged screening and selection process. RevMan 5.4 software was employed to run the quantitative analysis and forest plots. Risk of bias assessment was conducted using the Cochrane tool and Newcastle-Ottawa Scale for the randomized and non-randomized trials, respectively. From the 831 results retrieved, 8 studies including 2768 patients were included. There was no significant effect on overall survival (OS) (overall response (OR) = 0.98; Z = 0.42; p = 0.67). Meanwhile, progression-free survival (PFS) was significantly better with immune checkpoint inhibitors administration (OR = 0.85; Z = 3.9; p < 0.0001). The subgroup analysis for oncological outcomes based on programmed death ligand 1 (PD-L1) positivity status displayed no significant effect, except on prostate-specific antigen response rate (PSA RR) (OR = 3.25; Z = 2.29; p = 0.02). Based on DNA damage repair (DDR), positive patients had a significantly better PFS and a trend towards better OS and overall response rate (ORR); the ORR was 40% in positive patients compared to 20% in the negative patients (OR = 2.46; Z = 1.3; p = 0.19), while PSA RR was 23.5% compared to 14.3% (OR = 1.88; Z = 0.88; p = 0.38). Better PFS was clearly associated with DDR positivity (OR = 0.70; Z = 2.48; p = 0.01) with a trend towards better OS in DDR positive patients (OR = 0.71; Z = 1.38; p = 0.17). Based on tumor mutation burden (TMB), ORR was 46.7% with high TMB versus 8.8% in patients with low TMB (OR = 11.88; Z = 3.0; p = 0.003). Checkpoint inhibitors provide modest oncological advantages in metastatic castration-resistant prostate cancer. There are currently no good predictive indicators that indicate a greater response in some patients.

Project description:ObjectivesMen diagnosed with non-metastatic prostate cancer require standardised and robust long-term prognostic information to help them decide on management. Most currently-used tools use short-term and surrogate outcomes. We explored the evidence base in the literature on available pre-treatment, prognostic models built around long-term survival and assess the accuracy, generalisability and clinical availability of these models.DesignSystematic literature review, pre-specified and registered on PROSPERO (CRD42018086394).Data sourcesMEDLINE, Embase and The Cochrane Library were searched from January 2000 through February 2018, using previously-tested search terms.Eligibility criteriaInclusion required a multivariable model prognostic model for non-metastatic prostate cancer, using long-term survival data (defined as ≥5 years), which was not treatment-specific and usable at the point of diagnosis.Data extraction and synthesisTitle, abstract and full-text screening were sequentially performed by three reviewers. Data extraction was performed for items in the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Individual studies were assessed using the new Prediction model Risk Of Bias ASsessment Tool.ResultsDatabase searches yielded 6581 studies after deduplication. Twelve studies were included in the final review. Nine were model development studies using data from over 231 888 men. However, only six of the nine studies included any conservatively managed cases and only three of the nine included treatment as a predictor variable. Every included study had at least one parameter for which there was high risk of bias, with failure to report accuracy, and inadequate reporting of missing data common failings. Three external validation studies were included, reporting two available models: The University of California San Francisco (UCSF) Cancer of the Prostate Risk Assessment score and the Cambridge Prognostic Groups. Neither included treatment effect, and both had potential flaws in design, but represent the most robust and usable prognostic models currently available.ConclusionFew long-term prognostic models exist to inform decision-making at diagnosis of non-metastatic prostate cancer. Improved models are required to inform management and avoid undertreatment and overtreatment of non-metastatic prostate cancer.

Project description:ContextAdvances in systemic agents have increased overall survival for men diagnosed with metastatic prostate cancer. Additional cytoreductive prostate treatments and metastasis-directed therapies are under evaluation. These confer toxicity but may offer incremental survival benefits. Thus, an understanding of patients' values and treatment preferences is important for counselling, decision-making, and guideline development.ObjectiveTo perform a systematic review of patients' values, preferences, and expectations regarding treatment of metastatic prostate cancer.Evidence acquisitionThe MEDLINE, Embase, and CINAHL databases were systematically searched for qualitative and preference elucidation studies reporting on patients' preferences for treatment of metastatic prostate cancer. Certainty of evidence was assessed using Grading of Recommendation, Assessment, Development and Evaluation (GRADE) or GRADE Confidence in the Evidence from Reviews of Qualitative Research (CERQual). The protocol was registered on PROSPERO as CRD42020201420.Evidence synthesisA total of 1491 participants from 15 studies met the prespecified eligibility for inclusion. The study designs included were discrete choice experiments (n = 5), mixed methods (n = 3), and qualitative methods (n = 7). Disease states reported per study were: metastatic castration-resistant prostate cancer in nine studies (60.0%), metastatic hormone-sensitive prostate cancer in two studies (13.3%), and a mixed cohort in four studies (26.6%). In quantitative preference elicitation studies, patients consistently valued treatment effectiveness and delay in time to symptoms as the two top-ranked treatment attributes (low or very low certainty). Patients were willing to trade off treatment-related toxicity for potential oncological benefits (low certainty). In qualitative studies, thematic analysis revealed cancer progression and/or survival, pain, and fatigue as key components in treatment decisions (low or very low certainty). Patients continue to value oncological benefits in making decisions on treatments under qualitative assessment.ConclusionsThere is limited understanding of how patients make treatment and trade-off decisions following a diagnosis of metastatic prostate cancer. For appropriate investment in emerging cytoreductive local tumour and metastasis-directed therapies, we should seek to better understand how this cohort weighs the oncological benefits against the risks.Patient summaryWe looked at how men with advanced (metastatic) prostate cancer make treatment decisions. We found that little is known about patients' preferences for current and proposed new treatments. Further studies are required to understand how patients make decisions to help guide the integration of new treatments into the standard of care.

Project description:BackgroundThis study explored the value of intraductal carcinoma of the prostate (IDC-P) in predicting the efficacy of abiraterone treatment in metastatic hormone-sensitive prostate cancer (mHSPC) patients.MethodsA retrospective study of 925 patients who underwent prostate biopsies to detect IDC-P was conducted, with participants divided into two cohorts. The first cohort of 165 mHSPC patients receiving abiraterone treatment was analyzed to compare therapeutic effectiveness between IDC-P positive and negative cases. Utilizing propensity score matching (PSM) to reduce bias, outcomes such as PSA response, progression-free survival (PSA-PFS), radiographic progression-free survival (rPFS), and overall survival were assessed. Additionally, the second cohort of 760 mHSPC patients compared the efficacy of abiraterone with conventional hormone therapy, focusing on differences between IDC-P positive and negative individuals.ResultsAfter PSM, our first cohort included 108 patients with similar baseline characteristics. Among them, 50% (54/108) were diagnosed with IDC-P, with 22.2% (12/54) having IDC-P pattern 1 and 77.8% (42/54) with IDC-P pattern 2. While no notable difference was seen in PSA responses between IDC-P positive and negative patients, IDC-P presence linked to worse clinical outcomes (PSA-PFS: 18.6 months vs. not reached [NR], p = 0.009; rPFS: 23.6 months vs. NR, p = 0.020). Further analysis showed comparable outcomes for IDC-P pattern 1 but significantly worse prognosis for IDC-P pattern 2 (PSA-PFS: 18.6 months vs. NR, p = 0.002; rPFS: 22.4 months vs. NR, p = 0.010). Subgroup analysis revealed IDC-P pattern 2 consistently predicted poorer outcomes across patient subgroups. Remarkably, both IDC-P positive and negative patients gained more from androgen deprivation therapy with abiraterone than conventional treatment, with IDC-P negative patients showing a more significant survival advantage, supported by better hazard ratios (0.47 and 0.66).ConclusionThis study found that IDC-P, especially pattern 2, predicts poor prognosis in mHSPC patients on abiraterone therapy. Also, abiraterone's advantage over hormone therapy is reduced in cases with IDC-P compared to those without.

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Dataset Information

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

Background and objective

Methods

Key findings and limitations

Conclusions and clinical implications

Patient summary

Publications

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

Similar Datasets

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