Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis.
Magnetic resonance imaging
Neoplasm staging
Prostatic neoplasms
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
13 Nov 2023
13 Nov 2023
Historique:
received:
02
05
2023
accepted:
27
09
2023
revised:
10
09
2023
medline:
13
11
2023
pubmed:
13
11
2023
entrez:
13
11
2023
Statut:
aheadofprint
Résumé
Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction. Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76. MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed. Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements. • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.
Identifiants
pubmed: 37955670
doi: 10.1007/s00330-023-10427-3
pii: 10.1007/s00330-023-10427-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s).
Références
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 76:340–351. https://doi.org/10.1016/j.eururo.2019.02.033
doi: 10.1016/j.eururo.2019.02.033
pubmed: 30898406
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 71:701–704. https://doi.org/10.1016/j.eururo.2016.08.015
doi: 10.1016/j.eururo.2016.08.015
pubmed: 27576750
Falagario UG, Jambor I, Ratnani P et al (2020) Performance of prostate multiparametric MRI for prediction of prostate cancer extra-prostatic extension according to NCCN risk categories: implication for surgical planning. Minerva Urol Nefrol 72:746–754. https://doi.org/10.23736/S0393-2249.20.03688-7
Gatti M, Faletti R, Gentile F et al (2022) mEPE-score: a comprehensive grading system for predicting pathologic extraprostatic extension of prostate cancer at multiparametric magnetic resonance imaging. Eur Radiol 32:4942–4953. https://doi.org/10.1007/s00330-022-08595-9
doi: 10.1007/s00330-022-08595-9
pubmed: 35290508
pmcid: 9213375
Asfuroğlu U, Asfuroğlu BB, Özer H et al (2022) Which one is better for predicting extraprostatic extension on multiparametric MRI: ESUR score, Likert scale, tumor contact length, or EPE grade? Eur J Radiol 149:110228. https://doi.org/10.1016/j.ejrad.2022.110228
doi: 10.1016/j.ejrad.2022.110228
pubmed: 35255320
Huebner NA, Shariat SF (2021) Clinical impact and statistical significance of multiparametric magnetic resonance imaging for local staging of prostate cancer. Eur Urol 79:186–187. https://doi.org/10.1016/j.eururo.2020.11.002
doi: 10.1016/j.eururo.2020.11.002
pubmed: 33246667
Krishna S, Lim CS, McInnes MDF et al (2018) Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer. J Magn Reson Imaging 47:176–185. https://doi.org/10.1002/jmri.25729
doi: 10.1002/jmri.25729
pubmed: 28387981
EAU Guidelines. Edn. Presented at the EAU Annual Congress Amsterdam 2022. ISBN 978-94-92671-16-5.
Parker C, Castro E, Fizazi K et al (2020) Prostate cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 31:1119–1134. https://doi.org/10.1016/j.annonc.2020.06.011
doi: 10.1016/j.annonc.2020.06.011
pubmed: 32593798
de Rooij M, Hamoen EHJ, Witjes JA et al (2016) Accuracy of magnetic resonance imaging for local staging of prostate cancer: a diagnostic meta-analysis. Eur Urol 70:233–245. https://doi.org/10.1016/j.eururo.2015.07.029
doi: 10.1016/j.eururo.2015.07.029
pubmed: 26215604
Zelic R, Garmo H, Zugna D et al (2020) Predicting prostate cancer death with different pretreatment risk stratification tools: a head-to-head comparison in a nationwide cohort study. Eur Urol 77:180–188. https://doi.org/10.1016/j.eururo.2019.09.027
doi: 10.1016/j.eururo.2019.09.027
pubmed: 31606332
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 30:5004–5010. https://doi.org/10.1007/s00330-020-06839-0
doi: 10.1007/s00330-020-06839-0
pubmed: 32307562
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 79:180–185. https://doi.org/10.1016/j.eururo.2020.09.037
doi: 10.1016/j.eururo.2020.09.037
pubmed: 33023770
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 54:1222–1230. https://doi.org/10.1002/jmri.27678
doi: 10.1002/jmri.27678
pubmed: 33970517
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 31:7575–7583. https://doi.org/10.1007/s00330-021-07856-3
doi: 10.1007/s00330-021-07856-3
pubmed: 33792737
pmcid: 8452573
Damascelli A, Gallivanone F, Cristel G et al (2021) Advanced imaging analysis in prostate MRI: building a radiomic signature to predict tumor aggressiveness. Diagnostics 11:594. https://doi.org/10.3390/diagnostics11040594
doi: 10.3390/diagnostics11040594
pubmed: 33810222
pmcid: 8065545
Fan X, Xie N, Chen J et al (2022) Multiparametric MRI and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer. Front Oncol 12:839621. https://doi.org/10.3389/fonc.2022.839621
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 21:46. https://doi.org/10.1186/s40644-021-00414-6
doi: 10.1186/s40644-021-00414-6
pubmed: 34225808
pmcid: 8259026
Hou Y, Zhang Y-H, 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 48:3805–3816. https://doi.org/10.1007/s00259-021-05381-5
doi: 10.1007/s00259-021-05381-5
pubmed: 34018011
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 61:1570–1579. https://doi.org/10.1177/0284185120905066
doi: 10.1177/0284185120905066
pubmed: 32108505
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 50:1914–1925. https://doi.org/10.1002/jmri.26777
doi: 10.1002/jmri.26777
pubmed: 31062459
Ma S, Xie H, Wang H et al (2020) Preoperative prediction of extracapsular extension: radiomics signature based on magnetic resonance imaging to stage prostate cancer. Mol Imaging Biol 22:711–721. https://doi.org/10.1007/s11307-019-01405-7
doi: 10.1007/s11307-019-01405-7
pubmed: 31321651
Moroianu ŞL, Bhattacharya I, Seetharaman A et al (2022) Computational detection of extraprostatic extension of prostate cancer on multiparametric MRI using deep learning. Cancers (Basel) 14:2821. https://doi.org/10.3390/cancers14122821
doi: 10.3390/cancers14122821
pubmed: 35740487
Shiradkar R, Zuo R, Mahran A et al (2020) Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results. In: Hahn HK, Mazurowski MA (eds) Medical imaging 2020: computer-aided diagnosis. SPIE, p 121
Stanzione A, Cuocolo R, Cocozza S et al (2019) Detection of extraprostatic extension of cancer on biparametric MRI combining texture analysis and machine learning: preliminary results. Acad Radiol 26:1338–1344. https://doi.org/10.1016/j.acra.2018.12.025
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 10:40. https://doi.org/10.3389/fonc.2020.00940
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169
doi: 10.1148/radiol.2015151169
pubmed: 26579733
Kapoor S, Narayanan A (2022) Leakage and the reproducibility crisis in ML-based science. https://doi.org/10.48550/arXiv.2207.07048
Pinto dos Santos D, Dietzel M, Baessler B (2021) A decade of radiomics research: are images really data or just patterns in the noise? Eur Radiol 31:1–4. https://doi.org/10.1007/s00330-020-07108-w
doi: 10.1007/s00330-020-07108-w
pubmed: 32767103
Marcadent S, Hofmeister J, Preti MG et al (2020) Generative adversarial networks improve the reproducibility and discriminative power of radiomic features. Radiol Artif Intell 2:e190035. https://doi.org/10.1148/ryai.2020190035
doi: 10.1148/ryai.2020190035
pubmed: 33937823
pmcid: 8082326
Alderson PO (2020) The quest for generalizability in radiomics. Radiol Artif Intell 2:e200068. https://doi.org/10.1148/ryai.2020200068
doi: 10.1148/ryai.2020200068
pubmed: 33939790
pmcid: 8082370
Koçak B, Cuocolo R, dos Santos DP et al (2023) Must-have qualities of clinical research on artificial intelligence and machine learning. Balkan Med J 40:3–12. https://doi.org/10.4274/balkanmedj.galenos.2022.2022-11-51
doi: 10.4274/balkanmedj.galenos.2022.2022-11-51
pubmed: 36578657
pmcid: 9874249
Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:b2535. https://doi.org/10.1136/bmj.b2535
doi: 10.1136/bmj.b2535
pubmed: 19622551
pmcid: 2714657
PROSPERO: International Prospective Register of Systematic Reviews. https://www.crd.york.ac.uk/prospero/
Whiting PF (2011) QUADAS-2: a revised tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med 155:529. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
doi: 10.7326/0003-4819-155-8-201110180-00009
pubmed: 22007046
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141
doi: 10.1038/nrclinonc.2017.141
pubmed: 28975929
Ponsiglione A, Stanzione A, Spadarella G et al (2022) Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol 33:2239–2247. https://doi.org/10.1007/s00330-022-09180-w
doi: 10.1007/s00330-022-09180-w
pubmed: 36303093
pmcid: 9935717
Zhong J, Hu Y, Si L et al (2021) A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 31:1526–1535. https://doi.org/10.1007/s00330-020-07221-w
doi: 10.1007/s00330-020-07221-w
pubmed: 32876837
Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634. https://doi.org/10.1136/bmj.315.7109.629
doi: 10.1136/bmj.315.7109.629
pubmed: 9310563
pmcid: 2127453
Song J, Yin Y, Wang H et al (2020) A review of original articles published in the emerging field of radiomics. Eur J Radiol 127:108991. https://doi.org/10.1016/j.ejrad.2020.108991
doi: 10.1016/j.ejrad.2020.108991
pubmed: 32334372
Stanzione A, Ponsiglione A, Alessandrino F et al (2023) Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 7:13. https://doi.org/10.1186/s41747-023-00321-4
doi: 10.1186/s41747-023-00321-4
pubmed: 36907973
pmcid: 10008761
van Timmeren JE, Cester D, Tanadini-Lang S et al (2020) Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 11:91. https://doi.org/10.1186/s13244-020-00887-2
doi: 10.1186/s13244-020-00887-2
pubmed: 32785796
pmcid: 7423816
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338. https://doi.org/10.1148/radiol.2020191145
doi: 10.1148/radiol.2020191145
pubmed: 32154773
Bai K, Sun Y, Li W, Zhang L (2019) Apparent diffusion coefficient in extraprostatic extension of prostate cancer: a systematic review and diagnostic meta-analysis. Cancer Manag Res 11:3125–3137. https://doi.org/10.2147/CMAR.S191738
doi: 10.2147/CMAR.S191738
pubmed: 31114355
pmcid: 6489658
Guiot J, Vaidyanathan A, Deprez L et al (2022) A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev 42:426–440. https://doi.org/10.1002/med.21846
doi: 10.1002/med.21846
pubmed: 34309893
Mongan J, Moy L, Kahn CE (2020) Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029. https://doi.org/10.1148/ryai.2020200029
doi: 10.1148/ryai.2020200029
pubmed: 33937821
pmcid: 8017414
Kocak B, Baessler B, Bakas S et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:75. https://doi.org/10.1186/s13244-023-01415-8
doi: 10.1186/s13244-023-01415-8
pubmed: 37142815
pmcid: 10160267
Calimano-Ramirez LF, Virarkar MK, Hernandez M et al (2023) MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review. Abdominal Radiol (NY) 48:2379–2400. https://doi.org/10.1007/s00261-023-03924-y
doi: 10.1007/s00261-023-03924-y
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 111:22–29. https://doi.org/10.1111/j.1464-410X.2012.11324.x
doi: 10.1111/j.1464-410X.2012.11324.x
pubmed: 22834909
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 171:1844–9; discussion 1849. https://doi.org/10.1097/01.ju.0000121693.05077.3d
Li W, Shang W, Lu F et al (2022) Diagnostic performance of extraprostatic extension grading system for detection of extraprostatic extension in prostate cancer: a diagnostic systematic review and meta-analysis. Front Oncol 11:792120. https://doi.org/10.3389/fonc.2021.792120
Li W, Dong A, Hong G et al (2021) Diagnostic performance of ESUR scoring system for extraprostatic prostate cancer extension: a meta-analysis. Eur J Radiol 143:109896. https://doi.org/10.1016/j.ejrad.2021.109896
doi: 10.1016/j.ejrad.2021.109896
pubmed: 34416449
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 290:709–719. https://doi.org/10.1148/radiol.2018181278
doi: 10.1148/radiol.2018181278
pubmed: 30667329
Steyerberg EW, Vickers AJ, Cook NR et al (2010) Assessing the performance of prediction models. Epidemiology 21:128–138. https://doi.org/10.1097/EDE.0b013e3181c30fb2
doi: 10.1097/EDE.0b013e3181c30fb2
pubmed: 20010215
pmcid: 3575184
Stanzione A, Cuocolo R, Ugga L et al (2022) Oncologic imaging and radiomics: a walkthrough review of methodological challenges. Cancers (Basel) 14:4871. https://doi.org/10.3390/cancers14194871
doi: 10.3390/cancers14194871
pubmed: 36230793
Moons KGM, Altman DG, Reitsma JB et al (2015) Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 162:W1–W73. https://doi.org/10.7326/M14-0698
doi: 10.7326/M14-0698
pubmed: 25560730
Cuocolo R, Cipullo MB, Stanzione A et al (2020) Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis. Eur Radiol 30:6877–6887. https://doi.org/10.1007/s00330-020-07027-w
doi: 10.1007/s00330-020-07027-w
pubmed: 32607629
Cronin P, Kelly AM, Altaee D et al (2018) How to perform a systematic review and meta-analysis of diagnostic imaging studies. Acad Radiol 25:573–593. https://doi.org/10.1016/j.acra.2017.12.007
doi: 10.1016/j.acra.2017.12.007
pubmed: 29371119
Adams J, Hillier-Brown FC, Moore HJ et al (2016) Searching and synthesising ‘grey literature’ and ‘grey information’ in public health: critical reflections on three case studies. Syst Rev 5:164. https://doi.org/10.1186/s13643-016-0337-y
doi: 10.1186/s13643-016-0337-y
pubmed: 27686611
pmcid: 5041336