Dataset Information

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.

ABSTRACT:

Purpose

In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.

Materials and methods

The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.

Results

Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.

Conclusion

Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.

Critical relevance statement

This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.

Key points

• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

SUBMITTER: Zhu M

PROVIDER: S-EPMC10444717 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.

Zhu MeiLin M Gao JiaHao J Han Fang F Yin LongLin L Zhang LuShun L Yang Yong Y Zhang JiaWen J

Insights into imaging 20230822 1

<h4>Purpose</h4>In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.<h4>Materials and methods</h4>The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023 ...[more]

PMID: 37606802

Similar Datasets

Project description:PurposeTo validate the MRI grading system proposed by Mehralivand et al in 2019 (the "extraprostatic extension [EPE] grade") in an independent cohort and to compare the Mehralivand EPE grading system with EPE interpretation on the basis of a five-point Likert score ("EPE Likert").Materials and methodsA total of 310 consecutive patients underwent multiparametric MRI according to a standardized institutional protocol before radical prostatectomy was performed by using the same 1.5-T MRI unit at a single institution between 2010 and 2012. Two radiologists blinded to clinical information assessed EPE according to standardized criteria. On the basis of the readings performed until 2017, the diagnostic performance of EPE Likert and Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision curve methodology against histologic EPE as standard of reference. Prediction of biochemical recurrence-free survival (BRFS) was assessed by Kaplan-Meier analysis and log rank test.ResultsOf the 310 patients, 80 patients (26%) had EPE, including 33 with radial distance 1.1 mm or greater. Interrater reliability was fair (weighted κ 0.47 and 0.45) for both EPE grade and EPE Likert. Sensitivity for identifying EPE using EPE grade versus EPE Likert was 0.83 versus 0.86 and 0.86 versus 0.91 for radiologist 1 and 2, respectively. Specificity was 0.48 versus 0.58 and 0.39 versus 0.70 (P < .05 for radiologist 2). There were no significant differences in the ROC area under the curve or on decision curve analysis. Both EPE grade and EPE Likert were significant predictors of BRFS.ConclusionMehralivand EPE grade and EPE Likert have equivalent diagnostic performance for predicting EPE and BRFS with a similar degree of observer dependence.© RSNA, 2020Keywords: MR-Imaging, Neoplasms-Primary, Observer Performance, Outcomes Analysis, Prostate, StagingSupplemental material is available for this article.See also the commentary by Choyke in this issue.

Project description:PurposeMany radiotracers are currently available for the detection of recurrent prostate cancer (rPC), yet many have not been compared head-to-head in comparative imaging studies. There is therefore an unmet need for evidence synthesis to guide evidence-based decisions in the selection of radiotracers. The objective of this study was therefore to assess the detection rate of various radiotracers for the rPC.MethodsThe PUBMED, EMBASE, and the EU and NIH trials databases were searched without date or language restriction for comparative imaging tracers for 13 radiotracers of principal interest. Key search terms included 18F-PSMA-1007, 18F-DCPFyl, 68Ga-PSMA-11, 18F-PSMA-11, 68Ga-PSMA-I&T, 68Ga-THP-PSMA, 64Cu-PSMA-617, 18F-JK-PSMA-7, 18F-Fluciclovine, 18F-FABC, 18F-Choline, 11C-Choline, and 68Ga-RM2. Studies reporting comparative imaging data in humans in rPC were selected. Single armed studies and matched pair analyses were excluded. Twelve studies with eight radiotracers were eligible for inclusion. Two independent reviewers screened all studies (using the PRISMA-NMA statement) for inclusion criteria, extracted data, and assessed risk of bias (using the QUADAS-2 tool). A network meta-analysis was performed using Markov-Chain Monte Carlo Bayesian analysis to obtain estimated detection rate odds ratios for each tracer combination.ResultsA majority of studies were judged to be at risk of publication bias. With the exception of 18F-PSMA-1007, little difference in terms of detection rate was revealed between the three most commonly used PSMA-radiotracers (68Ga-PSMA-11, 18F-PSMA-1007, 18F-DCFPyl), which in turn showed clear superiority to choline and fluciclovine using the derived network.ConclusionDifferences in patient-level detection rates were observed between PSMA- and choline-radiotracers. However, there is currently insufficient evidence to favour one of the four routinely used PSMA-radioligands (PSMA-11, PSMA-1007, PSMA-I&T, and DCFPyl) over another owing to the limited evidence base and risk of publication bias revealed by our systematic review. A further limitation was lack of reporting on diagnostic accuracy, which might favour radiotracers with low specificity in an analysis restricted only to detection rate. The NMA derived can be used to inform the design of future clinical trials and highlight areas where current evidence is weak.

Project description:The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Key pointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

Dataset Information

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.

Purpose

Materials and methods

Results

Conclusion

Critical relevance statement

Key points

Publications

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets