Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
Deep learning
Nomograms
Prostatic neoplasms, Multiparametric magnetic resonance imaging
Risk assessment
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
02 Jul 2024
02 Jul 2024
Historique:
received:
18
12
2023
accepted:
21
04
2024
revised:
15
04
2024
medline:
3
7
2024
pubmed:
3
7
2024
entrez:
2
7
2024
Statut:
aheadofprint
Résumé
Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001). Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment. For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure. The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.
Identifiants
pubmed: 38955845
doi: 10.1007/s00330-024-10818-0
pii: 10.1007/s00330-024-10818-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bundesministerium für Wirtschaft und Klimaschutz
ID : 01MT21004B
Informations de copyright
© 2024. The Author(s).
Références
Panebianco V, Valerio MC, Giuliani A et al (2018) Clinical utility of multiparametric magnetic resonance imaging as the first-line tool for men with high clinical suspicion of prostate cancer. Eur Urol Oncol 1:208–214. https://doi.org/10.1016/j.euo.2018.03.008
doi: 10.1016/j.euo.2018.03.008
pubmed: 31102623
Radtke JP, Schwab C, Wolf MB et al (2016) Multiparametric magnetic resonance imaging (MRI) and MRI-transrectal ultrasound fusion biopsy for index tumor detection: correlation with radical prostatectomy specimen. Eur Urol 70:846–853. https://doi.org/10.1016/j.eururo.2015.12.052
doi: 10.1016/j.eururo.2015.12.052
pubmed: 26810346
Schimmöller L, Blondin D, Arsov C et al (2016) MRI-guided in-bore biopsy: differences between prostate cancer detection and localization in primary and secondary biopsy settings. AJR Am J Roentgenol 206:92–99. https://doi.org/10.2214/AJR.15.14579
doi: 10.2214/AJR.15.14579
pubmed: 26700339
Radtke JP, Kuru TH, Bonekamp D et al (2016) Further reduction of disqualification rates by additional MRI-targeted biopsy with transperineal saturation biopsy compared with standard 12-core systematic biopsies for the selection of prostate cancer patients for active surveillance. Prostate Cancer Prostatic Dis 19:283–291. https://doi.org/10.1038/pcan.2016.16
doi: 10.1038/pcan.2016.16
pubmed: 27184812
Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389:815–822. https://doi.org/10.1016/S0140-6736(16)32401-1
doi: 10.1016/S0140-6736(16)32401-1
pubmed: 28110982
van der Leest M, Cornel E, Israël B et al (2019) Head-to-head comparison of transrectal ultrasound-guided prostate biopsy versus multiparametric prostate resonance imaging with subsequent magnetic resonance-guided biopsy in biopsy-naive men with elevated prostate-specific antigen: a large prospective multicenter clinical study. Eur Urol 75:570–578. https://doi.org/10.1016/j.eururo.2018.11.023
doi: 10.1016/j.eururo.2018.11.023
pubmed: 30477981
Kasivisvanathan V, Rannikko AS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378:1767–1777. https://doi.org/10.1056/NEJMoa1801993
doi: 10.1056/NEJMoa1801993
pubmed: 29552975
pmcid: 9084630
Rouvière O, Puech P, Renard-Penna R et al (2019) Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol 20:100–109. https://doi.org/10.1016/S1470-2045(18)30569-2
doi: 10.1016/S1470-2045(18)30569-2
pubmed: 30470502
Wegelin O, Exterkate L, van der Leest M et al (2019) The FUTURE trial: a multicenter randomised controlled trial on target biopsy techniques based on magnetic resonance imaging in the diagnosis of prostate cancer in patients with prior negative biopsies. Eur Urol 75:582–590. https://doi.org/10.1016/j.eururo.2018.11.040
doi: 10.1016/j.eururo.2018.11.040
pubmed: 30522912
Mottet N, van den Bergh RCN, Briers E et al (2021) EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer—2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 79:243–262. https://doi.org/10.1016/j.eururo.2020.09.042
doi: 10.1016/j.eururo.2020.09.042
pubmed: 33172724
Steyerberg E, Roobol M, Kattan M, Van der Kwast T, De Koning H, Schröder F (2007) Prediction of indolent prostate cancer: validation and updating of a prognostic nomogram. J Urol 177:107–112. https://doi.org/10.1016/j.juro.2006.08.068
doi: 10.1016/j.juro.2006.08.068
pubmed: 17162015
Kranse R, Roobol M, Schröder FH (2008) A graphical device to represent the outcomes of a logistic regression analysis. Prostate 68:1674–1680. https://doi.org/10.1002/pros.20840
doi: 10.1002/pros.20840
pubmed: 18712715
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
Venderink W, van Luijtelaar A, van der Leest M et al (2019) Multiparametric magnetic resonance imaging and follow-up to avoid prostate biopsy in 4259 men. BJU Int 124:775–784. https://doi.org/10.1111/bju.14853
doi: 10.1111/bju.14853
pubmed: 31237388
Westphalen AC, McCulloch CE, Anaokar JM et al (2020) Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the society of abdominal radiology prostate cancer disease-focused panel. Radiology 296:76–84. https://doi.org/10.1148/radiol.2020190646
doi: 10.1148/radiol.2020190646
pubmed: 32315265
Drost FH, Osses D, Nieboer D et al (2020) Prostate magnetic resonance imaging, with or without magnetic resonance imaging-targeted biopsy, and systematic biopsy for detecting prostate cancer: a cochrane systematic review and meta-analysis. Eur Urol 77:78–94. https://doi.org/10.1016/j.eururo.2019.06.023
doi: 10.1016/j.eururo.2019.06.023
pubmed: 31326219
Loeb S, Vellekoop A, Ahmed HU et al (2013) Systematic review of complications of prostate biopsy. Eur Urol 64:876–892. https://doi.org/10.1016/j.eururo.2013.05.049
doi: 10.1016/j.eururo.2013.05.049
pubmed: 23787356
Radtke JP, Giganti F, Wiesenfarth M et al (2019) Prediction of significant prostate cancer in biopsy-naïve men: validation of a novel risk model combining MRI and clinical parameters and comparison to an ERSPC risk calculator and PI-RADS. PLoS One 14:e0221350. https://doi.org/10.1371/journal.pone.0221350
doi: 10.1371/journal.pone.0221350
pubmed: 31450235
pmcid: 6710031
Netzer N, Weißer C, Schelb P et al (2021) Fully automatic deep learning in bi-institutional prostate magnetic resonance imaging: effects of cohort size and heterogeneity. Invest Radiol 56:799–808. https://doi.org/10.1097/rli.0000000000000791
doi: 10.1097/rli.0000000000000791
pubmed: 34049336
Schelb P, Kohl S, Radtke JP et al (2019) Classification of cancer at prostate mri: deep learning versus clinical PI-RADS assessment. Radiology 293:607–617. https://doi.org/10.1148/radiol.2019190938
doi: 10.1148/radiol.2019190938
pubmed: 31592731
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203–211. https://doi.org/10.1038/s41592-020-01008-z
doi: 10.1038/s41592-020-01008-z
pubmed: 33288961
Baumgartner M, Jäger PF, Isensee F, Maier-Hein KH (2021) nnDetection: a self-configuring method for medical object detection. In: de Bruijne M, Cattin PC, Cotin S et al (eds) Medical image computing and computer assisted intervention—MICCAI 2021. Springer International Publishing, Cham, pp 530–539. https://doi.org/10.1007/978-3-030-87240-3_51
Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention—MICCAI 2015. Springer International Publishing, Cham, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28
Weinreb JC, Barentsz JO, Choyke PL et al (2016) PI-RADS prostate imaging—reporting and data system: 2015, version 2. Eur Urol 69:16–40. https://doi.org/10.1016/j.eururo.2015.08.052
doi: 10.1016/j.eururo.2015.08.052
pubmed: 26427566
JO Barentsz J Richenberg R Clements et al (2012) ESUR prostate MR guidelines 2012 Eur Radiol 22 746–757. https://doi.org/10.1007/s00330-011-2377-y .
doi: 10.1007/s00330-011-2377-y
pubmed: 22322308
pmcid: 3297750
Dickinson L, Ahmed HU, Allen C et al (2011) Magnetic resonance imaging for the detection, localisation, and characterisation of prostate cancer: recommendations from a European Consensus Meeting. Eur Urol 59:477–494. https://doi.org/10.1016/j.eururo.2010.12.009
doi: 10.1016/j.eururo.2010.12.009
pubmed: 21195536
Kuru TH, Wadhwa K, Chang RT et al (2013) Definitions of terms, processes and a minimum dataset for transperineal prostate biopsies: a standardization approach of the Ginsburg Study Group for Enhanced Prostate Diagnostics. BJU Int 112:568–577. https://doi.org/10.1111/bju.12132
doi: 10.1111/bju.12132
pubmed: 23773772
Egevad L, Delahunt B, Srigley JR, Samaratunga H (2016) International Society of Urological Pathology (ISUP) grading of prostate cancer—an ISUP consensus on contemporary grading. APMIS 124:433–435. https://doi.org/10.1111/apm.12533
doi: 10.1111/apm.12533
pubmed: 27150257
Schelb P, Wang X, Radtke JP et al (2021) Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. Eur Radiol 31:302–313. https://doi.org/10.1007/s00330-020-07086-z
doi: 10.1007/s00330-020-07086-z
pubmed: 32767102
Radtke JP, Wiesenfarth M, Kesch C et al (2017) Combined clinical parameters and multiparametric magnetic resonance imaging for advanced risk modeling of prostate cancer—patient-tailored risk stratification can reduce unnecessary biopsies. Eur Urol 72:888–896. https://doi.org/10.1016/j.eururo.2017.03.039
doi: 10.1016/j.eururo.2017.03.039
pubmed: 28400169
van Leeuwen PJ, Hayen A, Thompson JE et al (2017) A multiparametric magnetic resonance imaging-based risk model to determine the risk of significant prostate cancer prior to biopsy. BJU Int 120:774–781. https://doi.org/10.1111/bju.13814
doi: 10.1111/bju.13814
pubmed: 28207981
Alberts AR, Roobol MJ, Verbeek JFM et al (2019) Prediction of high-grade prostate cancer following multiparametric magnetic resonance imaging: improving the Rotterdam European Randomized Study of Screening for Prostate cancer Risk Calculators. Eur Urol 75:310–318. https://doi.org/10.1016/j.eururo.2018.07.031
doi: 10.1016/j.eururo.2018.07.031
pubmed: 30082150
Görtz M, Radtke JP, Hatiboglu G et al (2021) The value of prostate-specific antigen density for prostate imaging-reporting and data system 3 lesions on multiparametric magnetic resonance imaging: a strategy to avoid unnecessary prostate biopsies. Eur Urol Focus 7:325–331. https://doi.org/10.1016/j.euf.2019.11.012
doi: 10.1016/j.euf.2019.11.012
pubmed: 31839564
Deniffel D, Healy GM, Dong X et al (2021) Avoiding unnecessary biopsy: MRI-based risk models versus a PI-RADS and PSA density strategy for clinically significant prostate cancer. Radiology 300:369–379. https://doi.org/10.1148/radiol.2021204112
doi: 10.1148/radiol.2021204112
pubmed: 34032510
Püllen L, Radtke JP, Wiesenfarth M et al (2020) External validation of novel magnetic resonance imaging-based models for prostate cancer prediction. BJU Int 125:407–416. https://doi.org/10.1111/bju.14958
doi: 10.1111/bju.14958
pubmed: 31758738
Remmers S, Kasivisvanathan V, Verbeek JFM, Moore CM, Roobol MJ (2022) Reducing biopsies and magnetic resonance imaging scans during the diagnostic pathway of prostate cancer: applying the rotterdam prostate cancer risk calculator to the PRECISION trial data. Eur Urol Open Science 36:1–8. https://doi.org/10.1016/j.euros.2021.11.002
doi: 10.1016/j.euros.2021.11.002
Van Calster B, Wynants L, Verbeek JFM et al (2018) Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol 74:796–804. https://doi.org/10.1016/j.eururo.2018.08.038
doi: 10.1016/j.eururo.2018.08.038
pubmed: 30241973
pmcid: 6261531
Vickers AJ, Elkin EB (2006) Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 26:565–574. https://doi.org/10.1177/0272989x06295361
doi: 10.1177/0272989x06295361
pubmed: 17099194
pmcid: 2577036
Zhang KS, Schelb P, Kohl S et al (2021) Improvement of PI-RADS-dependent prostate cancer classification by quantitative image assessment using radiomics or mean ADC. Magn Reson Imaging 82:9–17. https://doi.org/10.1016/j.mri.2021.06.013
doi: 10.1016/j.mri.2021.06.013
pubmed: 34147597
Petersmann A-L, Remmers S, Klein T et al (2021) External validation of two MRI-based risk calculators in prostate cancer diagnosis. World J Urol 39:4109–4116. https://doi.org/10.1007/s00345-021-03770-x
doi: 10.1007/s00345-021-03770-x
pubmed: 34169337
Doan P, Graham P, Lahoud J et al (2021) A comparison of prostate cancer prediction models in men undergoing both magnetic resonance imaging and transperineal biopsy: Are the models still relevant? BJU Int 128:36–44. https://doi.org/10.1111/bju.15554
doi: 10.1111/bju.15554
pubmed: 34374190
Nan L, Guo K, Li M, Wu Q, Huo S (2022) Development and validation of a multi-parameter nomogram for predicting prostate cancer: a retrospective analysis from Handan Central Hospital in China. PeerJ 10:e12912. https://doi.org/10.7717/peerj.12912
doi: 10.7717/peerj.12912
pubmed: 35256916
pmcid: 8898009
Patel HD, Koehne EL, Shea SM et al (2022) Risk of prostate cancer for men with prior negative biopsies undergoing magnetic resonance imaging compared with biopsy-naive men: a prospective evaluation of the PLUM cohort. Cancer 128:75–84. https://doi.org/10.1002/cncr.33875
doi: 10.1002/cncr.33875
pubmed: 34427930
Nasri J, Barthe F, Parekh S et al (2022) Nomogram predicting adverse pathology outcome on radical prostatectomy in low-risk prostate cancer men. Urology 166:189–195. https://doi.org/10.1016/j.urology.2022.02.019
doi: 10.1016/j.urology.2022.02.019
pubmed: 35263642
Hu D, Cao Q, Tong M et al (2022) A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer. BMC Med Genomics 15:24. https://doi.org/10.1186/s12920-022-01172-5
doi: 10.1186/s12920-022-01172-5
pubmed: 35135561
pmcid: 8822680
Beksac AT, Ratnani P, Dovey Z et al (2021) Unified model involving genomics, magnetic resonance imaging and prostate-specific antigen density outperforms individual co-variables at predicting biopsy upgrading in patients on active surveillance for low risk prostate cancer. Cancer Rep 5:e1492. https://doi.org/10.1002/cnr2.1492
doi: 10.1002/cnr2.1492
Wu C, Zhu J, King A et al (2021) Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer. Cancer Commun (Lond) 41:1387–1397. https://doi.org/10.1002/cac2.12205
doi: 10.1002/cac2.12205
pubmed: 34520132
Huang W, Randhawa R, Jain P et al (2022) A novel artificial intelligence-powered method for prediction of early recurrence of prostate cancer after prostatectomy and cancer drivers. JCO Clin Cancer Inform 6:e2100131. https://doi.org/10.1200/CCI.21.00131
Mazzone E, Gandaglia G, Ploussard G et al (2022) Risk stratification of patients candidate to radical prostatectomy based on clinical and multiparametric magnetic resonance imaging parameters: development and external validation of novel risk groups. Eur Urol 81:193–203. https://doi.org/10.1016/j.eururo.2021.07.027
doi: 10.1016/j.eururo.2021.07.027
pubmed: 34399996
van Dijk-de Haan MC, Boellaard TN, Tissier R et al (2022) Value of different magnetic resonance imaging-based measurements of anatomical structures on preoperative prostate imaging in predicting urinary continence after radical prostatectomy in men with prostate cancer: a systematic review and meta-analysis. Eur Urol Focus 8:1211–1225. https://doi.org/10.1016/j.euf.2022.01.015
doi: 10.1016/j.euf.2022.01.015
pubmed: 35181284
Tavakoli AA, Hielscher T, Badura P et al (2022) Contribution of dynamic contrast-enhanced and Diffusion MRI to PI-RADS for detecting clinically significant prostate cancer. Radiology 306:186–199. https://doi.org/10.1148/radiol.212692
doi: 10.1148/radiol.212692
pubmed: 35972360
Tan YG, Fang AHS, Lim JKS et al (2022) Incorporating artificial intelligence in urology: Supervised machine learning algorithms demonstrate comparative advantage over nomograms in predicting biochemical recurrence after prostatectomy. Prostate 82:298–305. https://doi.org/10.1002/pros.24272
doi: 10.1002/pros.24272
pubmed: 34855228
Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277:826–832. https://doi.org/10.1148/radiol.2015151516
doi: 10.1148/radiol.2015151516
pubmed: 26509226