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Insights into Imaging
Published in: Insights into Imaging 1/2023

Open Access 01-12-2023 | Prostate Cancer | Critical Review

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

Authors: MeiLin Zhu, JiaHao Gao, Fang Han, LongLin Yin, LuShun Zhang, Yong Yang, JiaWen Zhang

Published in: Insights into Imaging | Issue 1/2023

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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.

Graphical Abstract

Appendix
Available only for authorised users
Literature
1.
Jeong BC, Chalfin HJ, Lee SB et al (2015) The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. Eur Urol 67:342–346PubMed
2.
Yossepowitch O, Briganti A, Eastham JA et al (2014) Positive surgical margins after radical prostatectomy: a systematic review and contemporary update. Eur Urol 65:303–313PubMed
3.
Eifler JB, Feng Z, Lin BM et al (2013) An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int 111:22–29PubMed
4.
Cooperberg MR, Pasta DJ, Elkin EP et al (2005) The University of California, San Francisco Cancer of the Prostate Risk Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J Urol 173:1938–1942PubMedPubMedCentral
5.
Ohori M, Kattan MW, Koh H et al (2004) Predicting the presence and side of extracapsular extension: a nomogram for staging prostate cancer. J Urol. 171:1844–1849; discussion 1849
6.
Turkbey B, Rosenkrantz AB, Haider MA et al (2019) Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 76:340–351PubMed
7.
de Rooij M, Hamoen EH, Witjes JA, Barentsz JO, Rovers MM (2016) Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis. Eur Urol 70:233–245PubMed
8.
9.
Mehralivand S, Shih JH, Harmon S et al (2019) A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology 290:709–719PubMed
10.
Caglic I, Kovac V, Barrett T (2019) Multiparametric MRI - local staging of prostate cancer and beyond. Radiol Oncol 53:159–170PubMedPubMedCentral
11.
Jansen BHE, Nieuwenhuijzen JA, Oprea-Lager DE et al (2019) Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging? Urol Oncol 37:181.e181–181.e186
12.
Weaver JK, Kim EH, Vetter JM et al (2018) Prostate magnetic resonance imaging provides limited incremental value over the Memorial Sloan Kettering Cancer Center preradical prostatectomy nomogram. Urology 113:119–128PubMed
13.
Salameh JP, Bossuyt PM, McGrath TA et al (2020) Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist. BMJ 370:m2632PubMed
14.
Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350:g7594PubMed
15.
Wolff RF, Moons KGM, Riley RD et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170:51–58PubMed
16.
17.
Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560
18.
Rutter CM, Gatsonis CA (2001) A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 20:2865–2884PubMed
19.
Chen Y, Yu W, Fan Y et al (2017) Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer. Oncotarget 8:22095–22103PubMed
20.
Diamand R, Ploussard G, Roumiguié M et al (2021) External validation of a multiparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension and seminal vesicle invasion in prostate cancer patients undergoing radical prostatectomy. Eur Urol 79:180–185PubMed
21.
Feng TS, Sharif-Afshar AR, Wu J et al (2015) Multiparametric MRI improves accuracy of clinical nomograms for predicting extracapsular extension of prostate cancer. Urology 86:332–337PubMed
22.
Losnegård A, Reisæter LAR, Halvorsen OJ et al (2020) Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients. Acta Radiol 61:1570–1579PubMed
23.
Martini A, Gupta A, Lewis SC et al (2018) Development and internal validation of a side-specific, multiparametric magnetic resonance imaging-based nomogram for the prediction of extracapsular extension of prostate cancer. BJU Int 122:1025–1033PubMed
24.
Morlacco A, Sharma V, Viers BR et al (2017) The incremental role of magnetic resonance imaging for prostate cancer staging before radical prostatectomy. Eur Urol 71:701–704PubMed
25.
Nyarangi-Dix J, Wiesenfarth M, Bonekamp D et al (2020) Combined clinical parameters and multiparametric magnetic resonance imaging for the prediction of extraprostatic disease-a risk model for patient-tailored risk stratification when planning radical prostatectomy. Eur Urol Focus 6:1205–1212PubMed
26.
Ravi C, Sanjeevan KV, Thomas A, Pooleri GK (2021) Development of an Indian nomogram for predicting extracapsular extension in prostate cancer. Indian J Urol 37:65–71PubMedPubMedCentral
27.
Rayn KN, Bloom JB, Gold SA et al (2018) Added value of multiparametric magnetic resonance imaging to clinical nomograms for predicting adverse pathology in prostate cancer. J Urol 200:1041–1047PubMedPubMedCentral
28.
Renard-Penna R, Rouprêt M, Comperat E et al (2013) Accuracy of high resolution (1.5 tesla) pelvic phased array magnetic resonance imaging (MRI) in staging prostate cancer in candidates for radical prostatectomy: results from a prospective study. Urol Oncol 31:448–454PubMed
29.
30.
Wang L, Mullerad M, Chen HN et al (2004) Prostate cancer: incremental value of endorectal MR imaging findings for prediction of extracapsular extension. Radiology 232:133–139PubMed
31.
Wibmer AG, Kattan MW, Alessandrino F et al (2021) International multi-site initiative to develop an MRI-inclusive nomogram for side-specific prediction of extraprostatic extension of prostate cancer. Cancers (Basel) 13(11):2627PubMed
32.
Gandaglia G, Ploussard G, Valerio M et al (2020) The Key Combined Value of Multiparametric Magnetic Resonance Imaging, and Magnetic Resonance Imaging-targeted and Concomitant Systematic Biopsies for the Prediction of Adverse Pathological Features in Prostate Cancer Patients Undergoing Radical Prostatectomy. Eur Urol 77:733–741PubMed
33.
Liu H, Tang K, Xia D et al (2020) Added value of biparametric MRI and TRUS-guided systematic biopsies to clinical parameters in predicting adverse pathology in prostate cancer. Cancer Manag Res 12:7761–7770PubMedPubMedCentral
34.
Sandeman K, Eineluoto JT, Pohjonen J et al (2020) Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy. PLoS One 15:e0235779
35.
Blas L, Shiota M, Nagakawa S et al (2023) Validation of user-friendly models predicting extracapsular extension in prostate cancer patients. Asian J Urol 10:81–88PubMed
36.
Diamand R, Roche JB, Lievore E et al (2023) External validation of models for prediction of side-specific extracapsular extension in prostate cancer patients undergoing radical prostatectomy. Eur Urol Focus 9:309–316PubMed
37.
Sun YK, Yu Y, Xu G et al (2023) Added value of shear-wave elastography in the prediction of extracapsular extension and seminal vesicle invasion before radical prostatectomy. Asian J Androl 25:259–264PubMed
38.
Alves JR, Muglia VF, Lucchesi FR et al (2020) Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer. Eur Radiol 30:5004–5010PubMed
39.
Bai H, Xia W, Ji X et al (2021) Multiparametric magnetic resonance imaging-based peritumoral radiomics for preoperative prediction of the presence of extracapsular extension with prostate cancer. J Magn Reson Imaging 54:1222–1230PubMed
40.
Giganti F, Coppola A, Ambrosi A et al (2016) Apparent diffusion coefficient in the evaluation of side-specific extracapsular extension in prostate cancer: Development and external validation of a nomogram of clinical use. Urol Oncol 34:291.e299–291.e217
41.
Hara T, Nakanishi H, Nakagawa T et al (2013) Ability of preoperative 3.0-Tesla magnetic resonance imaging to predict the absence of side-specific extracapsular extension of prostate cancer. Int J Urol 20:993–999PubMed
42.
He D, Wang X, Fu C et al (2021) MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging 21:46PubMedPubMedCentral
43.
Hou Y, Zhang YH, Bao J et al (2021) Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study. Eur J Nucl Med Mol Imaging 48:3805–3816PubMed
44.
Lebacle C, Roudot-Thoraval F, Moktefi A, Bouanane M, De La Taille A, Salomon L (2017) Integration of MRI to clinical nomogram for predicting pathological stage before radical prostatectomy. World J Urol 35:1409–1415PubMed
45.
Xu L, Zhang G, Zhao L et al (2020) Radiomics based on multiparametric magnetic resonance imaging to predict extraprostatic extension of prostate cancer. Front Oncol 10:940PubMedPubMedCentral
46.
Zapała P, Dybowski B, Bres-Niewada E et al (2019) Predicting side-specific prostate cancer extracapsular extension: a simple decision rule of PSA, biopsy, and MRI parameters. Int Urol Nephrol 51:1545–1552PubMedPubMedCentral
47.
Zapała P, Kozikowski M, Dybowski B, Zapała Ł, Dobruch J, Radziszewski P (2021) External validation of a magnetic resonance imaging-based algorithm for prediction of side-specific extracapsular extension in prostate cancer. Cent European J Urol 74:327–333PubMedPubMedCentral
48.
49.
Guerra A, Alves FC, Maes K et al (2022) Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features. Cancer Imaging 22:74PubMedPubMedCentral
50.
Boyce S, Fan Y, Watson RW, Murphy TB (2013) Evaluation of prediction models for the staging of prostate cancer. BMC Med Inform Decis Mak 13:126PubMedPubMedCentral
51.
Dal Moro F, Prayer-Galetti T, Angelini L et al (2018) Study of diagnostic accuracy of Fagan’s two-step nomogram in increasing the value of predictive tools for prostate cancer: application of specific spatial distribution of positive/negative bioptic cores to predict extracapsular extension. Aging Clin Exp Res 30:1497–1504
52.
Egawa S, Suyama K, Matsumoto K et al (1998) Improved predictability of extracapsular extension and seminal vesicle involvement based on clinical and biopsy findings in prostate cancer in Japanese men. Urology 52:433–440PubMed
53.
Tsao CW, Liu CY, Cha TL et al (2014) Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population. J Chin Med Assoc 77:513–518PubMed
54.
Song C, Kang T, Ro JY, Lee MS, Kim CS, Ahn H (2005) Nomograms for the prediction of pathologic stage of clinically localized prostate cancer in Korean men. J Korean Med Sci 20:262–266PubMedPubMedCentral
55.
Thalgott M, Düwel C, Rauscher I et al (2018) One-stop-shop whole-body (68)Ga-PSMA-11 PET/MRI compared with clinical nomograms for preoperative T and N staging of high-risk prostate cancer. J Nucl Med 59:1850–1856PubMed
56.
Partin AW, Kattan MW, Subong EN et al (1997) Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. JAMA. 277:1445–1451
57.
Sighinolfi MC, Assumma S, Cassani A et al (2023) Pre-operative prediction of extracapsular extension of prostate cancer: first external validation of the PRECE model on an independent dataset. Int Urol Nephrol 55:93–97PubMed
58.
Majchrzak N, Cieśliński P, Głyda M, Karmelita-Katulska K (2021) Prostate magnetic resonance imaging analyses, clinical parameters, and preoperative nomograms in the prediction of extraprostatic extension. Clin Pract 11:763–774PubMedPubMedCentral
59.
Wang J, Wu CJ, Bao ML, Zhang J, Shi HB, Zhang YD (2018) Using support vector machine analysis to assess PartinMR: a new prediction model for organ-confined prostate cancer. J Magn Reson Imaging 48:499–506PubMed
60.
Xu L, Zhang G, Zhang X et al (2021) External validation of the extraprostatic extension grade on MRI and its incremental value to clinical models for assessing extraprostatic cancer. Front Oncol 11:655093PubMedPubMedCentral
61.
Zanelli E, Giannarini G, Cereser L et al (2019) Head-to-head comparison between multiparametric MRI, the partin tables, memorial sloan kettering cancer center nomogram, and CAPRA score in predicting extraprostatic cancer in patients undergoing radical prostatectomy. J Magn Reson Imaging 50:1604–1613PubMed
62.
Xiang JY, Huang XS, Xu JX et al (2022) MRI extraprostatic extension grade: accuracy and clinical incremental value in the assessment of extraprostatic cancer. Biomed Res Int 2022:3203965PubMedPubMedCentral
63.
Patel VR, Sandri M, Grasso AAC et al (2018) A novel tool for predicting extracapsular extension during graded partial nerve sparing in radical prostatectomy. BJU Int 121:373–382PubMed
64.
Soeterik TFW, van Melick HHE, Dijksman LM et al (2020) External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy. Urol Oncol 38:372–378PubMed
65.
Wibmer A, Vargas HA, Donahue TF et al (2015) Diagnosis of extracapsular extension of prostate cancer on prostate MRI: impact of second-opinion readings by subspecialized genitourinary oncologic radiologists. AJR Am J Roentgenol 205:W73–78PubMed
66.
Costa DN, Passoni NM, Leyendecker JR et al (2018) Diagnostic utility of a Likert scale versus qualitative descriptors and length of capsular contact for determining extraprostatic tumor extension at multiparametric prostate MRI. AJR Am J Roentgenol 210:1066–1072PubMed
67.
Eberhardt SC (2019) Local staging of prostate cancer with MRI: a need for standardization. Radiology 290:720–721PubMed
68.
Ma S, Xie H, Wang H et al (2019) MRI-based radiomics signature for the preoperative prediction of extracapsular extension of prostate cancer. J Magn Reson Imaging 50:1914–1925PubMed
69.
Cuocolo R, Stanzione A, Faletti R et al (2021) MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol 31:7575–7583PubMedPubMedCentral
70.
Gugliandolo SG, Pepa M, Isaksson LJ et al (2021) MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218). Eur Radiol 31:716–728PubMed
71.
Kattan MW, Shariat SF, Andrews B et al (2003) The addition of interleukin-6 soluble receptor and transforming growth factor beta1 improves a preoperative nomogram for predicting biochemical progression in patients with clinically localized prostate cancer. J Clin Oncol 21:3573–3579PubMed
72.
Lu LI, Zhang H, Pang J, Hou GL, Lu MH, Gao X (2016) ERG rearrangement as a novel marker for predicting the extra-prostatic extension of clinically localised prostate cancer. Oncol Lett 11:2532–2538PubMedPubMedCentral
73.
Turo R, Forster JA, West RM, Prescott S, Paul AB, Cross WR (2015) Do prostate cancer nomograms give accurate information when applied to European patients? Scand J Urol 49:16–24PubMed
Metadata
Title
Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis
Authors
MeiLin Zhu
JiaHao Gao
Fang Han
LongLin Yin
LuShun Zhang
Yong Yang
JiaWen Zhang
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-023-01486-7

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