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Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis.
Zhu, MeiLin; Gao, JiaHao; Han, Fang; Yin, LongLin; Zhang, LuShun; Yang, Yong; Zhang, JiaWen.
Afiliación
  • Zhu M; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
  • Gao J; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
  • Han F; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
  • Yin L; Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, China.
  • Zhang L; Department of Pathology and Pathophysiology, Chengdu Medical College, Development and Regeneration Key Laboratory of Sichuan Province, Chengdu, 610500, China.
  • Yang Y; School of Big Health & Intelligent Engineering, Chengdu Medical College, Chengdu, 610500, China. 365925141@qq.com.
  • Zhang J; Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China. zhangjw2000@126.com.
Insights Imaging ; 14(1): 140, 2023 Aug 22.
Article en En | MEDLINE | ID: mdl-37606802
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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Insights Imaging Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Insights Imaging Año: 2023 Tipo del documento: Article País de afiliación: China
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