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Guo S, Ren J, Meng Q, Zhang B, Jiao J, Han D, Wu P, Ma S, Zhang J, Xing N, Qin W, Kang F, Zhang J. The impact of integrating PRIMARY score or SUVmax with MRI-based risk models for the detection of clinically significant prostate cancer. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06916-2. [PMID: 39264425 DOI: 10.1007/s00259-024-06916-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 09/01/2024] [Indexed: 09/13/2024]
Abstract
PURPOSE An MRI-based risk calculator (RC) has been recommended for diagnosing clinically significant prostate cancer (csPCa). PSMA PET/CT can detect lesions that are not visible on MRI, and the addition of PSMA PET/CT to MRI may improve diagnostic performance. The aim of this study was to incorporate the PRIMARY score or SUVmax derived from [68Ga]Ga-PSMA-11 PET/CT into the RC and compare these models with MRI-based RC to assess whether this can further reduce unnecessary biopsies. METHODS A total of 683 consecutive biopsy-naïve men who underwent both [68Ga]Ga-PSMA-11 PET/CT and MRI before biopsy were temporally divided into a development cohort (n = 552) and a temporal validation cohort (n = 131). Three logistic regression RCs were developed and compared: MRI-RC, MRI-SUVmax-RC and MRI-PRIMARY-RC. Discrimination, calibration, and clinical utility were evaluated. The primary outcome was the clinical utility of the risk calculators for detecting csPCa and reducing the number of negative biopsies. RESULTS The prevalence of csPCa was 47.5% (262/552) in the development cohort and 41.9% (55/131) in the temporal validation cohort. In the development cohort, the AUC of MRI-PRIMARY-RC was significantly higher than that of MRI-RC (0.924 vs. 0.868, p < 0.001) and MRI-SUVmax-RC (0.924 vs. 0.904, p = 0.002). In the temporal validation cohort, MRI-PRIMARY-RC also showed the best discriminative ability with an AUC of 0.921 (95% CI: 0.873-0.969). Bootstrapped calibration curves revealed that the model fit was acceptable. MRI-PRIMARY-RC exhibited near-perfect calibration within the range of 0-40%. DCA showed that MRI-PRIMARY-RC had the greatest net benefit for detecting csPCa compared with MRI-RC and MRI-SUVmax-RC at a risk threshold of 5-40% for csPCa in both the development and validation cohorts. CONCLUSION The addition of the PRIMARY score to MRI-based multivariable model improved the accuracy of risk stratification prior to biopsy. Our novel MRI-PRIMARY prediction model is a promising approach for reducing unnecessary biopsies and improving the early detection of csPCa.
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Affiliation(s)
- Shikuan Guo
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
- Department of Urology, No.988 Hospital of Joint Logistic Support Force, Zhengzhou, Henan, 450042, China
| | - Jing Ren
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China
| | - Qingze Meng
- Department of Urology, No.988 Hospital of Joint Logistic Support Force, Zhengzhou, Henan, 450042, China
| | - Boyuan Zhang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
| | - Jianhua Jiao
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
| | - Donghui Han
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
| | - Peng Wu
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
| | - Shuaijun Ma
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China
| | - Jing Zhang
- Department of Pathology, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, China
| | - Nianzeng Xing
- Department of Urology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Weijun Qin
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China.
| | - Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China.
| | - Jingliang Zhang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, No.127, Changle West Road, Xincheng District, Xi'an, Shaanxi, 710032, China.
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Ayerra Perez H, Barba Abad JF, Argaluza Escudero J, Extramiana Cameno J, Tolosa Eizaguirre E. Development of prediction models based on risk scores for clinically significant prostate cancer on MRI/TRUS fusion biopsy. Urol Oncol 2024:S1078-1439(24)00575-1. [PMID: 39227236 DOI: 10.1016/j.urolonc.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/01/2024] [Accepted: 08/08/2024] [Indexed: 09/05/2024]
Abstract
BACKGROUND The implementation of population screening for prostate cancer has increased the number of patients with biochemical suspicion. Prediction models may reduce the number of unnecessary biopsies by identifying patients who benefit the most from them. Our aim is to develop a prediction model that is easily applicable in patients with suspicion of prostate cancer in the urology clinic setting to avoid unnecessary biopsies. METHODS We developed prediction models based on risk scores for the detection of prostate cancer and clinically significant prostate cancer using the TRIPOD guidelines. For this, we conducted an observational and retrospective review of computerised medical records of 204 patients undergoing prostate fusion biopsy between 2018 and 2021. We also reviewed other prediction models for prostate cancer including radiological parameters and targeted sampling of suspicious lesions. RESULTS A total of 204 patients underwent a biopsy, 138 were diagnosed of prostate cancer, and from them, 60 of clinically significant prostate cancer. Multivariate regression and random forest analysis were performed. Age, PSA density, diameter of the index lesions and PIRADS score on MRI were identified as predictors with an Area Under the Curve ranging between 0.71 and 0.80 and acceptable calibration results. Risk scores may avoid between 21.7% and 48.1% of biopsies. CONCLUSION Our prediction models are characterised by ease of use and may reduce unnecessary biopsies with satisfactory discrimination and calibration results while bringing benefits to the healthcare system and patients.
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Affiliation(s)
- Hector Ayerra Perez
- Department of Urology, Araba University Hospital, OSI Araba Osakidetza, Vitoria-Gasteiz, Spain; Urologic Cancer Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain.
| | | | - Julene Argaluza Escudero
- Epidemiology and Public Health Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
| | - Javier Extramiana Cameno
- Department of Urology, Araba University Hospital, OSI Araba Osakidetza, Vitoria-Gasteiz, Spain; Urologic Cancer Group, Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain
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Lacson R, Haj-Mirzaian A, Burk K, Glazer DI, Naik S, Khorasani R, Kibel AS. A Model for Predicting Clinically Significant Prostate Cancer Using Prostate MRI and Risk Factors. J Am Coll Radiol 2024; 21:1419-1427. [PMID: 38719106 DOI: 10.1016/j.jacr.2024.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/16/2024] [Accepted: 02/22/2024] [Indexed: 06/10/2024]
Abstract
PURPOSE The aim of this study was to develop and validate a predictive model for clinically significant prostate cancer (csPCa) using prostate MRI and patient risk factors. METHODS In total, 960 men who underwent MRI from 2015 to 2019 and biopsy either 6 months before or 6 months after MRI were identified. Men diagnosed with csPCa were identified, and csPCa risk was modeled using known patient factors (age, race, and prostate-specific antigen [PSA] level) and prostate MRI findings (location, Prostate Imaging Reporting and Data System score, extraprostatic extension, dominant lesion size, and PSA density). csPCa was defined as Gleason score sum ≥ 7. Using a derivation cohort, a multivariable logistic regression model and a point-based scoring system were developed to predict csPCa. Discrimination and calibration were assessed in a separate independent validation cohort. RESULTS Among 960 MRI reports, 552 (57.5%) were from men diagnosed with csPCa. Using the derivation cohort (n = 632), variables that predicted csPCa were Prostate Imaging Reporting and Data System scores of 4 and 5, the presence of extraprostatic extension, and elevated PSA density. Evaluation using the validation cohort (n = 328) resulted in an area under the curve of 0.77, with adequate calibration (Hosmer-Lemeshow P = .58). At a risk threshold of >2 points, the model identified csPCa with sensitivity of 98.4% and negative predictive value of 78.6% but prevented only 4.3% potential biopsies (0-2 points; 14 of 328). At a higher threshold of >5 points, the model identified csPCa with sensitivity of 89.5% and negative predictive value of 70.1% and avoided 20.4% of biopsies (0-5 points; 67 of 328). CONCLUSIONS The point-based model reported here can potentially identify a vast majority of men at risk for csPCa, while avoiding biopsy in about 1 in 5 men with elevated PSA levels.
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Affiliation(s)
- Ronilda Lacson
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Associate Director, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts.
| | - Arya Haj-Mirzaian
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Kristine Burk
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Quality and Patient Safety Officer, Mass General Brigham, Boston, Massachusetts
| | - Daniel I Glazer
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Medical Director of CT and Director, Cross-Sectional Interventional Radiology, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Sachin Naik
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Ramin Khorasani
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Vice Chair, Radiology Quality and Safety, Mass General Brigham, Boston, Massachusetts; and Vice Chair of Radiology, Distinguished Chair, Medical Informatics, and Director, Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts
| | - Adam S Kibel
- Harvard Medical School, Boston, Massachusetts; Department of Surgery and Chair, Department of Urology, Brigham and Women's Hospital, Boston, Massachusetts
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Sundaram KM, Zafar HM. Improving the Value Proposition of Multiparametric Prostate MRI. J Am Coll Radiol 2024; 21:1428-1429. [PMID: 38719102 DOI: 10.1016/j.jacr.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 05/01/2024] [Accepted: 05/01/2024] [Indexed: 06/19/2024]
Affiliation(s)
- Karthik M Sundaram
- Department of Radiology, University of Pennsylvania Health System, Philadelphia, Pennsylvania. https://twitter.com/RadsKarthik
| | - Hanna M Zafar
- Vice Chair Quality Radiology, Department of Radiology, University of Pennsylvania Health System, Philadelphia, Pennsylvania.
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Michel MS, Gschwend JE, Wullich B, Krege S, Bolenz C, Merseburger AS, Krabbe LM, Schultz-Lampel D, König F, Haferkamp A, Hadaschik B. [Risk-adapted early detection program for prostate cancer 2.0-position paper of the German Society of Urology 2024]. UROLOGIE (HEIDELBERG, GERMANY) 2024; 63:893-898. [PMID: 39134785 DOI: 10.1007/s00120-024-02437-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/23/2024] [Indexed: 08/24/2024]
Abstract
BACKGROUND AND OBJECTIVE Despite the proven effectiveness of organized PSA-based screening in reducing prostate cancer-related mortality, there is currently no program in Germany covered by statutory health insurance. In accordance with the EU Council Decision (2022/0290(NLE)), the German Society of Urology (DGU) has developed a concept for risk-adapted prostate cancer early detection. MATERIALS AND METHODS Based on a literature review of current screening studies, an algorithm for PSA-based prostate cancer early detection was developed. RESULTS Risk-adapted prostate cancer screening involves PSA testing in the age group of 45-70 years, followed by PSA-based individual risk stratification and stepwise expansion of diagnostics through magnetic resonance imaging (MRI) to biopsy. While initially up to 2.6 million men will undergo PSA testing, a reduction in these initial examinations to fewer than 200,000 men per year will occur from year four onwards. CONCLUSIONS The presented algorithm provides clear recommendations for risk-adapted PSA-based early detection for prostate cancer for urologists and patients. The goal is to improve diagnosis of clinically significant prostate cancer, while reducing overdiagnosis and overtreatment.
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Affiliation(s)
- Maurice Stephan Michel
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland.
- Klinik für Urologie und Urochirurgie, Universitätsmedizin Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
| | - Jürgen E Gschwend
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Bernd Wullich
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Susanne Krege
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Christian Bolenz
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Axel S Merseburger
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Laura-Maria Krabbe
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Daniela Schultz-Lampel
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Frank König
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Axel Haferkamp
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
| | - Boris Hadaschik
- Deutsche Gesellschaft für Urologie, Geschäftsstelle Berlin, Martin-Buber-Straße 10, 14163, Berlin, Deutschland
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Schrader A, Netzer N, Hielscher T, Görtz M, Zhang KS, Schütz V, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D. 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. Eur Radiol 2024:10.1007/s00330-024-10818-0. [PMID: 38955845 DOI: 10.1007/s00330-024-10818-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/15/2024] [Accepted: 04/21/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVES 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. MATERIAL AND METHODS 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. RESULTS 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). CONCLUSIONS 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. CLINICAL RELEVANCE STATEMENT 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. KEY POINTS 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.
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Affiliation(s)
- Adrian Schrader
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg University Medical School, Heidelberg, Germany
| | - Nils Netzer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg University Medical School, Heidelberg, Germany
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
- Junior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kevin Sun Zhang
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Heidelberg University Medical School, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany.
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Davik P, Elschot M, Frost Bathen T, Bertilsson H. Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging. EUR UROL SUPPL 2024; 65:21-28. [PMID: 38974460 PMCID: PMC11225807 DOI: 10.1016/j.euros.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 07/09/2024] Open
Abstract
Background and objective The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa). Methods Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis. Results Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter. Conclusions and clinical implications Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations. Patient summary For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy.
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Affiliation(s)
- Petter Davik
- Department of Urology, St. Olav’s Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav’s Hospital, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav’s Hospital, Trondheim, Norway
| | - Helena Bertilsson
- Department of Urology, St. Olav’s Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
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Hyndman ME, Paproski RJ, Kinnaird A, Fairey A, Marks L, Pavlovich CP, Fletcher SA, Zachoval R, Adamcova V, Stejskal J, Aprikian A, Wallis CJD, Pink D, Vasquez C, Beatty PH, Lewis JD. Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform. NPJ Digit Med 2024; 7:163. [PMID: 38902526 PMCID: PMC11190196 DOI: 10.1038/s41746-024-01167-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary.
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Affiliation(s)
- M Eric Hyndman
- Department of Surgical Oncology, University of Calgary, Prostate Cancer Centre, Calgary, T2P 1P9, AB, Canada
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
| | - Robert J Paproski
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
| | - Adam Kinnaird
- Division of Urology, Department of Surgery, University of Alberta, Kipnes Urology Centre, Edmonton, T6G 1Z1, AB, Canada
- Department of Oncology, University of Alberta, Edmonton, T6G 2E1, AB, Canada
| | - Adrian Fairey
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
- Division of Urology, Department of Surgery, University of Alberta, Kipnes Urology Centre, Edmonton, T6G 1Z1, AB, Canada
| | - Leonard Marks
- UCLA Health, Westwood Urology 200 Medical Plaza, Suite 140, Los Angeles, CA, 90095, USA
| | - Christian P Pavlovich
- James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA
| | - Sean A Fletcher
- James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA
| | - Roman Zachoval
- Department of Urology, 3rd Faculty of Medicine of Charles University and Thomayer University Hospital, Prague, Czech Republic
| | - Vanda Adamcova
- Department of Urology, 3rd Faculty of Medicine of Charles University and Thomayer University Hospital, Prague, Czech Republic
| | - Jiri Stejskal
- Department of Urology, 3rd Faculty of Medicine of Charles University and Thomayer University Hospital, Prague, Czech Republic
| | - Armen Aprikian
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
- Department of Surgery, McGill University, Montreal, H3G 2M1, QC, Canada
| | - Christopher J D Wallis
- Division of Urology, Department of Surgery, University of Toronto, Toronto, M5T 1P5, ON, Canada
- Division of Urology, Department of Surgery, Mount Sinai Hospital, Toronto, M5G 1X5, ON, Canada
- Department of Surgical Oncology, University Health Network, Toronto, ON, Canada
| | - Desmond Pink
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
| | - Catalina Vasquez
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
| | - Perrin H Beatty
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada
| | - John D Lewis
- Nanostics Inc., 4550 10230 Jasper Avenue, Edmonton, T5J 4P6, AB, Canada.
- Department of Oncology, University of Alberta, Edmonton, T6G 2E1, AB, Canada.
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9
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Diamand R, Guenzel K, Jabbour T, Baudewyns A, Bourgeno HA, Lefebvre Y, Ferriero M, Simone G, Fourcade A, Fournier G, Bui AP, Taha F, Oderda M, Gontero P, Rysankova K, Bernal-Gomez A, Mastrorosa A, Roche JB, Fiard G, Abou Zahr R, Ploussard G, Windisch O, Novello Q, Benamran D, Delavar G, Anract J, Barry Delongchamps N, Halinski A, Dariane C, Vlahopoulos L, Assenmacher G, Roumeguère T, Peltier A. External validation and comparison of magnetic resonance imaging-based risk prediction models for prostate biopsy stratification. World J Urol 2024; 42:372. [PMID: 38866949 DOI: 10.1007/s00345-024-05068-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
PURPOSE Magnetic resonance imaging (MRI) is a promising tool for risk assessment, potentially reducing the burden of unnecessary prostate biopsies. Risk prediction models that incorporate MRI data have gained attention, but their external validation and comparison are essential for guiding clinical practice. The aim is to externally validate and compare risk prediction models for the diagnosis of clinically significant prostate cancer (csPCa). METHODS A cohort of 4606 patients across fifteen European tertiary referral centers were identified from a prospective maintained database between January 2016 and April 2023. Transrectal or transperineal image-fusion MRI-targeted and systematic biopsies for PI-RADS score of ≥ 3 or ≥ 2 depending on patient characteristics and physician preferences. Probabilities for csPCa, defined as International Society of Urological Pathology (ISUP) grade ≥ 2, were calculated for each patients using eight models. Performance was characterized by area under the receiver operating characteristic curve (AUC), calibration, and net benefit. Subgroup analyses were performed across various clinically relevant subgroups. RESULTS Overall, csPCa was detected in 2154 (47%) patients. The models exhibited satisfactory performance, demonstrating good discrimination (AUC ranging from 0.75 to 0.78, p < 0.001), adequate calibration, and high net benefit. The model described by Alberts showed the highest clinical utility for threshold probabilities between 10 and 20%. Subgroup analyses highlighted variations in models' performance, particularly when stratified according to PSA level, biopsy technique and PI-RADS version. CONCLUSIONS We report a comprehensive external validation of risk prediction models for csPCa diagnosis in patients who underwent MRI-targeted and systematic biopsies. The model by Alberts demonstrated superior clinical utility and should be favored when determining the need for a prostate biopsy.
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Affiliation(s)
- Romain Diamand
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium.
| | - Karsten Guenzel
- Department of Urology, Vivantes Klinikum Am Urban, Berlin, Germany
| | - Teddy Jabbour
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium
| | - Arthur Baudewyns
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium
| | - Henri-Alexandre Bourgeno
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium
| | - Yolène Lefebvre
- Department of Radiology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Giuseppe Simone
- Department of Urology, IRCCS "Regina Elena" National Cancer Institute, Rome, Italy
| | - Alexandre Fourcade
- Department of Urology, Hôpital Cavale Blanche, CHRU Brest, Brest, France
| | - Georges Fournier
- Department of Urology, Hôpital Cavale Blanche, CHRU Brest, Brest, France
| | | | - Fayek Taha
- Department of Urology, Centre Hospitalier Universitaire de Reims, Reims, France
| | - Marco Oderda
- Department of Urology, Città Della Salute E Della Scienza Di Torino, University of Turin, Turin, Italy
| | - Paolo Gontero
- Department of Urology, Città Della Salute E Della Scienza Di Torino, University of Turin, Turin, Italy
| | - Katerina Rysankova
- Department of Urology and Surgical Studies, Faculty of Medicine, University Hospital Ostrava, Ostrava University, Ostrava, Czech Republic
| | | | | | | | - Gaelle Fiard
- Department of Urology, Grenoble Alpes University Hospital, Université Grenoble Alpes, CNRS, Grenoble INP, TIMC, Grenoble, France
| | - Rawad Abou Zahr
- Department of Urology, La Croix du Sud Hospital, Quint Fonsegrives, France
| | | | - Olivier Windisch
- Department of Urology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Quentin Novello
- Department of Urology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Daniel Benamran
- Department of Urology, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Gina Delavar
- Departement of Urology, Hôpital Cochin, Paris, France
| | - Julien Anract
- Departement of Urology, Hôpital Cochin, Paris, France
| | | | - Adam Halinski
- Department of Urology, Private Medical Center, Klinika Wisniowa", Zielona Góra, Poland
| | - Charles Dariane
- Department of Urology, Hôpital Européen Georges-Pompidou, Université de Paris, Paris, France
| | | | | | - Thierry Roumeguère
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium
| | - Alexandre Peltier
- Department of Urology, Jules Bordet Institute-Erasme Hospital, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Jules Bordet Institute, HUB, Rue Meylemeersch 90, 1070, Brussels, Belgium
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10
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Nakai H, Suman G, Adamo DA, Navin PJ, Bookwalter CA, LeGout JD, Chen FK, Wellnitz CV, Silva AC, Thomas JV, Kawashima A, Fan JW, Froemming AT, Lomas DJ, Humphreys MR, Dora C, Korfiatis P, Takahashi N. Natural language processing pipeline to extract prostate cancer-related information from clinical notes. Eur Radiol 2024:10.1007/s00330-024-10812-6. [PMID: 38842692 DOI: 10.1007/s00330-024-10812-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/28/2024] [Accepted: 04/10/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVES To develop an automated pipeline for extracting prostate cancer-related information from clinical notes. MATERIALS AND METHODS This retrospective study included 23,225 patients who underwent prostate MRI between 2017 and 2022. Cancer risk factors (family history of cancer and digital rectal exam findings), pre-MRI prostate pathology, and treatment history of prostate cancer were extracted from free-text clinical notes in English as binary or multi-class classification tasks. Any sentence containing pre-defined keywords was extracted from clinical notes within one year before the MRI. After manually creating sentence-level datasets with ground truth, Bidirectional Encoder Representations from Transformers (BERT)-based sentence-level models were fine-tuned using the extracted sentence as input and the category as output. The patient-level output was determined by compilation of multiple sentence-level outputs using tree-based models. Sentence-level classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) on 15% of the sentence-level dataset (sentence-level test set). The patient-level classification performance was evaluated on the patient-level test set created by radiologists by reviewing the clinical notes of 603 patients. Accuracy and sensitivity were compared between the pipeline and radiologists. RESULTS Sentence-level AUCs were ≥ 0.94. The pipeline showed higher patient-level sensitivity for extracting cancer risk factors (e.g., family history of prostate cancer, 96.5% vs. 77.9%, p < 0.001), but lower accuracy in classifying pre-MRI prostate pathology (92.5% vs. 95.9%, p = 0.002) and treatment history of prostate cancer (95.5% vs. 97.7%, p = 0.03) than radiologists, respectively. CONCLUSION The proposed pipeline showed promising performance, especially for extracting cancer risk factors from patient's clinical notes. CLINICAL RELEVANCE STATEMENT The natural language processing pipeline showed a higher sensitivity for extracting prostate cancer risk factors than radiologists and may help efficiently gather relevant text information when interpreting prostate MRI. KEY POINTS When interpreting prostate MRI, it is necessary to extract prostate cancer-related information from clinical notes. This pipeline extracted the presence of prostate cancer risk factors with higher sensitivity than radiologists. Natural language processing may help radiologists efficiently gather relevant prostate cancer-related text information.
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Affiliation(s)
| | - Garima Suman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Daniel A Adamo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Frank K Chen
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Alvin C Silva
- Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA
| | - John V Thomas
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Jungwei W Fan
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | | | - Derek J Lomas
- Department of Urology, Mayo Clinic, Rochester, MN, USA
| | | | - Chandler Dora
- Department of Urology, Mayo Clinic, Jacksonville, FL, USA
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11
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Denijs FB, van Harten MJ, Meenderink JJL, Leenen RCA, Remmers S, Venderbos LDF, van den Bergh RCN, Beyer K, Roobol MJ. Risk calculators for the detection of prostate cancer: a systematic review. Prostate Cancer Prostatic Dis 2024:10.1038/s41391-024-00852-w. [PMID: 38830997 DOI: 10.1038/s41391-024-00852-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/17/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm. METHODS We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men. RESULTS We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93. CONCLUSIONS This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.
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Affiliation(s)
- Frederique B Denijs
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Meike J van Harten
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jonas J L Meenderink
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Renée C A Leenen
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Lionne D F Venderbos
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Roderick C N van den Bergh
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Katharina Beyer
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Monique J Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
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12
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Jahnen M, Hausler T, Meissner VH, Ankerst DP, Kattan MW, Sauter A, Gschwend JE, Herkommer K. Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center. World J Urol 2024; 42:290. [PMID: 38702557 PMCID: PMC11068682 DOI: 10.1007/s00345-024-04991-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/04/2024] [Indexed: 05/06/2024] Open
Abstract
PURPOSE mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy. METHODS Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information. RESULTS Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa. CONCLUSION mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.
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Affiliation(s)
- Matthias Jahnen
- Department of Urology, School of Medicine and Health, Technical University of Munich (TUM) Rechts der Isar University Hospital, Ismaningerstr. 22, 81675, Munich, Germany.
| | - Tanja Hausler
- Department of Mathematics, School of Computation, Information, and Technology, Boltzmannstr. 3, 85748, Garching, Germany
| | - Valentin H Meissner
- Department of Urology, School of Medicine and Health, Technical University of Munich (TUM) Rechts der Isar University Hospital, Ismaningerstr. 22, 81675, Munich, Germany
| | - Donna P Ankerst
- Department of Mathematics, School of Computation, Information, and Technology, Boltzmannstr. 3, 85748, Garching, Germany
| | - Michael W Kattan
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Andreas Sauter
- Department of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich (TUM) Rechts der Isar University Hospital, Ismaningerstr. 22, 81675, Munich, Germany
| | - Juergen E Gschwend
- Department of Urology, School of Medicine and Health, Technical University of Munich (TUM) Rechts der Isar University Hospital, Ismaningerstr. 22, 81675, Munich, Germany
| | - Kathleen Herkommer
- Department of Urology, School of Medicine and Health, Technical University of Munich (TUM) Rechts der Isar University Hospital, Ismaningerstr. 22, 81675, Munich, Germany
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13
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Orbe Villota PM, Leiva Centeno JA, Lugones J, Minuzzi PG, Varea SM. Comparison between the European Randomized Study for Screening of Prostate Cancer (ERSPC) and Prostate Biopsy Collaborative Group (PBCG) risk calculators: Prediction of clinically significant Prostate Cancer risk in a cohort of patients from Argentina. Actas Urol Esp 2024; 48:210-217. [PMID: 37827241 DOI: 10.1016/j.acuroe.2023.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 10/14/2023]
Abstract
OBJECTIVE To compare the performance of the risk calculators of the European Randomized Study for Screening of Prostate Cancer (ERSPC) and the Prostate Biopsy Collaborative Group (PBCG) in predicting the risk of presenting clinically significant prostate cancer. MATERIAL AND METHODS Retrospectively, patients who underwent prostate biopsy at Sanatorio Allende Cerro, Ciudad de Córdoba, Argentina, were identified from January 2018 to December 2021. The probability of having prostate cancer was calculated with the two calculators separately and then the results were compared to establish which of the two performed better. For this, areas under the curve (AUC) were analyzed. RESULTS 250 patients were included, 140 (56%) presented prostate cancer, of which 92 (65.71%) had clinically significant prostate cancer (Gleason score ≥7). The patients who presented cancer were older, had a higher prostate-specific antigen (PSA) value, and had a smaller prostate size. The AUC to predict the probability of having clinically significant prostate cancer was 0.79 and 0.73 for PBCG-RC and ERSPC-RC respectively (P=0.0084). CONCLUSION In this cohort of patients, both prostate cancer risk calculators performed well in predicting clinically significant prostate cancer risk, although the PBCG-RC showed better accuracy.
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Affiliation(s)
| | | | - J Lugones
- Servicio de Diagnóstico por Imágenes, Sanatorio Allende, Córdoba, Argentina
| | - P G Minuzzi
- Servicio de Urología, Sanatorio Allende, Córdoba, Argentina
| | - S M Varea
- Servicio de Urología, Sanatorio Allende, Córdoba, Argentina
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14
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Krausewitz P, Büttner T, von Danwitz M, Weiten R, Cox A, Klümper N, Stein J, Luetkens J, Kristiansen G, Ritter M, Ellinger J. Elucidating the need for prostate cancer risk calculators in conjunction with mpMRI in initial risk assessment before prostate biopsy at a tertiary prostate cancer center. BMC Urol 2024; 24:71. [PMID: 38532370 DOI: 10.1186/s12894-024-01460-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 03/17/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVE Utilizing personalized risk assessment for clinically significant prostate cancer (csPCa) incorporating multiparametric magnetic resonance imaging (mpMRI) reduces biopsies and overdiagnosis. We validated both multi- and univariate risk models in biopsy-naïve men, with and without the inclusion of mpMRI data for csPCa detection. METHODS N = 565 men underwent mpMRI-targeted prostate biopsy, and the diagnostic performance of risk calculators (RCs), mpMRI alone, and clinical measures were compared using receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). Subgroups were stratified based on mpMRI findings and quality. RESULTS csPCa was detected in 56.3%. PI-RADS score achieved the highest area under the curve (AUC) when comparing univariate risk models (AUC 0.82, p < 0.001). Multivariate RCs showed only marginal improvement in csPCa detection compared to PI-RADS score alone, with just one of four RCs showing significant superiority. In mpMRI-negative cases, the non-MRI-based RC performed best (AUC 0.80, p = 0.016), with the potential to spare biopsies for 23%. PSA-density and multivariate RCs demonstrated comparable performance for PI-RADS 3 constellation (AUC 0.65 vs. 0.60-0.65, p > 0.5; saved biopsies 16%). In men with suspicious mpMRI, both mpMRI-based RCs and the PI-RADS score predicted csPCa excellently (AUC 0.82-0.79 vs. 0.80, p > 0.05), highlighting superior performance compared to non-MRI-based models (all p < 0.002). Quality-assured imaging consistently improved csPCa risk stratification across all subgroups. CONCLUSION In tertiary centers serving a high-risk population, high-quality mpMRI provides a simple yet effective way to assess the risk of csPCa. Using multivariate RCs reduces multiple biopsies, especially in mpMRI-negative and PI-RADS 3 constellation.
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Affiliation(s)
- Philipp Krausewitz
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany.
| | - Thomas Büttner
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Marthe von Danwitz
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Richard Weiten
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Alexander Cox
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Niklas Klümper
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
- Institute of Experimental Oncology, University Hospital Bonn, Bonn, Germany
| | - Johannes Stein
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Julian Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany
| | | | - Manuel Ritter
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
| | - Jörg Ellinger
- Department of Urology and Pediatric Urology, University Hospital Bonn, Bonn, Germany
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15
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Bologna E, Licari LC, Franco A, Ditonno F, Manfredi C, De Nunzio C, Antonelli A, De Sio M, Leonardo C, Simone G, Cherullo EE, Autorino R. Incidental Prostate Cancer in Patients Treated for Benign Prostatic Hyperplasia: Analysis from a Contemporary National Dataset. Diagnostics (Basel) 2024; 14:677. [PMID: 38611590 PMCID: PMC11011333 DOI: 10.3390/diagnostics14070677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open
Abstract
(1) Background: Prostate Cancer (PCa) may be incidentally diagnosed during the microscopic evaluation of resected tissue from BPH surgeries, characterizing the clinical condition known as incidental PCa (iPCa). This study aims to assess the prevalence of iPCa following BPH surgery to evaluate the associated surgical procedures and to scrutinize preoperative and postoperative management. (2) Methods: A retrospective analysis was conducted using the PearlDiver™ Mariner database, containing patient records compiled between 2011 and 2021. International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes were employed to identify the population and outcomes. Our primary objective was to assess the prevalence of iPCa, categorized by the type of procedures, and to evaluate the subsequent treatment strategies. The secondary aim was to assess the impact of prostate biopsy (PB) and prostate MRI on iPCa detection. (3) Results: The overall cohort, accounting for 231,626 patients who underwent BPH surgery, exhibited a 2.2% prevalence rate of iPCa. The highest rate was observed for TURP (2.32%), while the lowest was recorded for RASP (1.18%). Preoperative MRI and PB demonstrated opposing trends over the years. Of the 5090 patients identified with iPCa, nearly 68% did not receive active treatment. The most common treatments were RT and ADT; 34.6% underwent RT, 31.75% received ADT, and 21.75% were treated with RT+ADT. RP was administered to approximately 9% of patients undergoing endoscopic procedures. Multivariate logistic regression analysis revealed age and openSP as additional risk factors for iPCa. Conversely, PB and MRI before surgery were linked to a decreased risk. (4) Conclusions: The contemporary prevalence of iPCa after BPH surgery is <3%. The increase in the use of prostate MRI mirrors a decline in the PB biopsy prior to BPH surgery but without resulting in an increased detection rate of iPCa. In contemporary routine clinical practice, iPCa is mostly managed in a different way when compared to biopsy-detected PCa.
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Affiliation(s)
- Eugenio Bologna
- Department of Maternal-Child and Urological Sciences, Sapienza University Rome, Policlinico Umberto I Hospital, 00161 Rome, Italy; (E.B.); (L.C.L.)
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
| | - Leslie Claire Licari
- Department of Maternal-Child and Urological Sciences, Sapienza University Rome, Policlinico Umberto I Hospital, 00161 Rome, Italy; (E.B.); (L.C.L.)
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
| | - Antonio Franco
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
- Department of Urology, Sant’Andrea Hospital, Sapienza University, 00189 Rome, Italy;
| | - Francesco Ditonno
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
- Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, University of Verona, 37129 Verona, Italy;
| | - Celeste Manfredi
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
- Unit of Urology, Department of Woman, Child and General and Specialized Surgery, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy;
| | - Cosimo De Nunzio
- Department of Urology, Sant’Andrea Hospital, Sapienza University, 00189 Rome, Italy;
| | - Alessandro Antonelli
- Department of Urology, Azienda Ospedaliera Universitaria Integrata Verona, University of Verona, 37129 Verona, Italy;
| | - Marco De Sio
- Unit of Urology, Department of Woman, Child and General and Specialized Surgery, University of Campania “Luigi Vanvitelli”, 80131 Naples, Italy;
| | - Costantino Leonardo
- Department of Urology, “Regina Elena” National Cancer Institute, 00144 Rome, Italy; (C.L.); (G.S.)
| | - Giuseppe Simone
- Department of Urology, “Regina Elena” National Cancer Institute, 00144 Rome, Italy; (C.L.); (G.S.)
| | - Edward E. Cherullo
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
| | - Riccardo Autorino
- Department of Urology, Rush University, Chicago, IL 60612, USA; (A.F.); (F.D.); (C.M.); (E.E.C.)
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16
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Patel HD, Remmers S, Ellis JL, Li EV, Roobol MJ, Fang AM, Davik P, Rais-Bahrami S, Murphy AB, Ross AE, Gupta GN. Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk. JAMA Netw Open 2024; 7:e241516. [PMID: 38451522 PMCID: PMC10921249 DOI: 10.1001/jamanetworkopen.2024.1516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/18/2024] [Indexed: 03/08/2024] Open
Abstract
Importance Magnetic resonance imaging (MRI)-based risk calculators can replace or augment traditional prostate cancer (PCa) risk prediction tools. However, few data are available comparing performance of different MRI-based risk calculators in external cohorts across different countries or screening paradigms. Objective To externally validate and compare MRI-based PCa risk calculators (Prospective Loyola University Multiparametric MRI [PLUM], UCLA [University of California, Los Angeles]-Cornell, Van Leeuwen, and Rotterdam Prostate Cancer Risk Calculator-MRI [RPCRC-MRI]) in cohorts from Europe and North America. Design, Setting, and Participants This multi-institutional, external validation diagnostic study of 3 unique cohorts was performed from January 1, 2015, to December 31, 2022. Two cohorts from Europe and North America used MRI before biopsy, while a third cohort used an advanced serum biomarker, the Prostate Health Index (PHI), before MRI or biopsy. Participants included adult men without a PCa diagnosis receiving MRI before prostate biopsy. Interventions Prostate MRI followed by prostate biopsy. Main Outcomes and Measures The primary outcome was diagnosis of clinically significant PCa (grade group ≥2). Receiver operating characteristics for area under the curve (AUC) estimates, calibration plots, and decision curve analysis were evaluated. Results A total of 2181 patients across the 3 cohorts were included, with a median age of 65 (IQR, 58-70) years and a median prostate-specific antigen level of 5.92 (IQR, 4.32-8.94) ng/mL. All models had good diagnostic discrimination in the European cohort, with AUCs of 0.90 for the PLUM (95% CI, 0.86-0.93), UCLA-Cornell (95% CI, 0.86-0.93), Van Leeuwen (95% CI, 0.87-0.93), and RPCRC-MRI (95% CI, 0.86-0.93) models. All models had good discrimination in the North American cohort, with an AUC of 0.85 (95% CI, 0.80-0.89) for PLUM and AUCs of 0.83 for the UCLA-Cornell (95% CI, 0.80-0.88), Van Leeuwen (95% CI, 0.79-0.88), and RPCRC-MRI (95% CI, 0.78-0.87) models, with somewhat better calibration for the RPCRC-MRI and PLUM models. In the PHI cohort, all models were prone to underestimate clinically significant PCa risk, with best calibration and discrimination for the UCLA-Cornell (AUC, 0.83 [95% CI, 0.81-0.85]) model, followed by the PLUM model (AUC, 0.82 [95% CI, 0.80-0.84]). The Van Leeuwen model was poorly calibrated in all 3 cohorts. On decision curve analysis, all models provided similar net benefit in the European cohort, with higher benefit for the PLUM and RPCRC-MRI models at a threshold greater than 22% in the North American cohort. The UCLA-Cornell model demonstrated highest net benefit in the PHI cohort. Conclusions and Relevance In this external validation study of patients receiving MRI and prostate biopsy, the results support the use of the PLUM or RPCRC-MRI models in MRI-based screening pathways regardless of European or North American setting. However, tools specific to screening pathways incorporating advanced biomarkers as reflex tests are needed due to underprediction.
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Affiliation(s)
- Hiten D. Patel
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
- Department of Urology, Loyola University Medical Center, Maywood, Illinois
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jeffrey L. Ellis
- Department of Urology, Loyola University Medical Center, Maywood, Illinois
| | - Eric V. Li
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Monique J. Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Andrew M. Fang
- Department of Urology, University of Alabama at Birmingham
| | - Petter Davik
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim
- Department of Urology, St Olavs Hospital, Trondheim, Norway
| | - Soroush Rais-Bahrami
- Department of Urology, University of Alabama at Birmingham
- Department of Radiology, University of Alabama at Birmingham
- O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham
| | - Adam B. Murphy
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ashley E. Ross
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Gopal N. Gupta
- Department of Urology, Loyola University Medical Center, Maywood, Illinois
- Department of Radiology, Loyola University Medical Center, Maywood, Illinois
- Department of Surgery, Loyola University Medical Center, Maywood, Illinois
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17
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Haj-Mirzaian A, Burk KS, Lacson R, Glazer DI, Saini S, Kibel AS, Khorasani R. Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis. JAMA Netw Open 2024; 7:e244258. [PMID: 38551559 PMCID: PMC10980971 DOI: 10.1001/jamanetworkopen.2024.4258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 02/02/2024] [Indexed: 04/01/2024] Open
Abstract
Importance Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion. Objective To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data. Data Sources PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022. Study Selection English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data. Data Extraction and Synthesis Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Main Outcomes and Measures Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis. Results The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15). Conclusions and Relevance These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.
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Affiliation(s)
- Arya Haj-Mirzaian
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kristine S. Burk
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel I. Glazer
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Sanjay Saini
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Adam S. Kibel
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
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18
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Davik P, Remmers S, Elschot M, Roobol MJ, Bathen TF, Bertilsson H. Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres. BJU Int 2024; 133:278-288. [PMID: 37607322 DOI: 10.1111/bju.16163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
OBJECTIVES To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa). PATIENTS AND METHODS We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%. RESULTS A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD. CONCLUSIONS Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.
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Affiliation(s)
- Petter Davik
- Department of Urology, St Olavs Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Mattijs Elschot
- Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim, Norway
- Department of Circulation and Medical Imaging (ISB), Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Monique J Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Tone Frost Bathen
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim, Norway
| | - Helena Bertilsson
- Department of Urology, St Olavs Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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19
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Krauss W, Frey J, Heydorn Lagerlöf J, Lidén M, Thunberg P. Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer. Acta Radiol 2024; 65:307-317. [PMID: 38115809 DOI: 10.1177/02841851231216555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa. PURPOSE To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting. MATERIAL AND METHODS Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves. RESULTS In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171). CONCLUSION PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.
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Affiliation(s)
- Wolfgang Krauss
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Janusz Frey
- Department of Urology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Jakob Heydorn Lagerlöf
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
- Department of Medical Physics, Karlstad Central Hospital, Sweden
| | - Mats Lidén
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Per Thunberg
- Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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20
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Hermanns T, Wettstein MS, Kaufmann B, Lautenbach N, Kaufmann E, Saba K, Schmid FA, Hötker AM, Müntener M, Umbehr M, Poyet C. BioPrev-C - development and validation of a contemporary prostate cancer risk calculator. Front Oncol 2024; 14:1343999. [PMID: 38450183 PMCID: PMC10915644 DOI: 10.3389/fonc.2024.1343999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/30/2024] [Indexed: 03/08/2024] Open
Abstract
Objectives To develop a novel biopsy prostate cancer (PCa) prevention calculator (BioPrev-C) using data from a prospective cohort all undergoing mpMRI targeted and transperineal template saturation biopsy. Materials and methods Data of all men who underwent prostate biopsy in our academic tertiary care center between 11/2016 and 10/2019 was prospectively collected. We developed a clinical prediction model for the detection of high-grade PCa (Gleason score ≥7) based on a multivariable logistic regression model incorporating age, PSA, prostate volume, digital rectal examination, family history, previous negative biopsy, 5-alpha-reductase inhibitor use and MRI PI-RADS score. BioPrev-C performance was externally validated in another prospective Swiss cohort and compared with two other PCa risk-calculators (SWOP-RC and PBCG-RC). Results Of 391 men in the development cohort, 157 (40.2%) were diagnosed with high-grade PCa. Validation of the BioPrev C revealed good discrimination with an area under the curve for high-grade PCa of 0.88 (95% Confidence Interval 0.82-0.93), which was higher compared to the other two risk calculators (0.71 for PBCG and 0.84 for SWOP). The BioPrev-C revealed good calibration in the low-risk range (0 - 0.25) and moderate overestimation in the intermediate risk range (0.25 - 0.75). The PBCG-RC showed good calibration and the SWOP-RC constant underestimation of high-grade PCa over the whole prediction range. Decision curve analyses revealed a clinical net benefit for the BioPrev-C at a clinical meaningful threshold probability range (≥4%), whereas PBCG and SWOP calculators only showed clinical net benefit above a 30% threshold probability. Conclusion BiopPrev-C is a novel contemporary risk calculator for the prediction of high-grade PCa. External validation of the BioPrev-C revealed relevant clinical benefit, which was superior compared to other well-known risk calculators. The BioPrev-C has the potential to significantly and safely reduce the number of men who should undergo a prostate biopsy.
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Affiliation(s)
- Thomas Hermanns
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Marian S. Wettstein
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Basil Kaufmann
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Noémie Lautenbach
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Ernest Kaufmann
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Karim Saba
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Florian A. Schmid
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
| | - Andreas M. Hötker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Martin Umbehr
- Department of Urology, Stadtspital Triemli, Zürich, Switzerland
| | - Cédric Poyet
- Department of Urology, University Hospital Zürich, University of Zürich, Zürich, Switzerland
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21
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Ptasznik G, Kelly BD, Murphy D, Lawrentschuk N, Kasivisvanathan V, Page M, Ong S, Moon D. How prostate-specific membrane antigen positron emission tomography is refining risk calculators in the primary prostate diagnostic pathway. BJU Int 2024; 133 Suppl 3:13-14. [PMID: 37691457 DOI: 10.1111/bju.16175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
- Gideon Ptasznik
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Young Urology Research Organisation, Melbourne, VIC, Australia
| | - Brian D Kelly
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Declan Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Nathan Lawrentschuk
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Veeru Kasivisvanathan
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Mark Page
- Division of Medical Imaging, St. Vincents Hospital (Melbourne Victoria), Melbourne, VIC, Australia
| | - Sean Ong
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Daniel Moon
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
- Royal Melbourne Hospital Clinical School, University of Melbourne, Melbourne, VIC, Australia
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22
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Morote J, Borque-Fernando Á, Esteban LM, Celma A, Campistol M, Miró B, Méndez O, Trilla E. Investigating Efficient Risk-Stratified Pathways for the Early Detection of Clinically Significant Prostate Cancer. J Pers Med 2024; 14:130. [PMID: 38392564 PMCID: PMC10890536 DOI: 10.3390/jpm14020130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
Risk-stratified pathways (RSPs) are recommended by the European Association of Uro-logy (EAU) to improve the early detection of clinically significant prostate cancer (csPCa). RSPs can reduce magnetic resonance imaging (MRI) demand, prostate biopsies, and the over-detection of insignificant PCa (iPCa). Our goal is to analyze the efficacy and cost-effectiveness of several RSPs by using sequential stratifications from the serum prostate-specific antigen level and digital rectal examination, the Barcelona risk calculators (BCN-RCs), MRI, and Proclarix™. In a cohort of 567 men with a serum PSA level above 3.0 ng/mL who underwent multiparametric MRI (mpMRI) and targeted and/or systematic biopsies, the risk of csPCa was retrospectively assessed using Proclarix™ and BCN-RCs 1 and 2. Six RSPs were compared with those recommended by the EAU that, stratifying men from MRI, avoided 16.7% of prostate biopsies with a prostate imaging-reporting and data system score of <3, with 2.6% of csPCa cases remaining undetected. The most effective RSP avoided mpMRI exams in men with a serum PSA level of >10 ng/mL and suspicious DRE, following stratifications from BCN-RC 1, mpMRI, and Proclarix™. The demand for mpMRI decreased by 19.9%, prostate biopsies by 19.8%, and over-detection of iPCa by 22.7%, while 2.6% of csPCa remained undetected as in the recommended RSP. Cost-effectiveness remained when the Proclarix™ price was assumed to be below EUR 200.
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Affiliation(s)
- Juan Morote
- Department of Urology, Vall d'Hebron Hospital, 08035 Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Research Group in Urology, Vall d'Hebron Research Institute, 08035 Barcelona, Spain
| | | | - Luis M Esteban
- Department of Applied Mathematics, Escuela Universitaria Politécnica La Almunia, Universidad de Zaragoza, 50100 Zaragoza, Spain
| | - Ana Celma
- Department of Urology, Vall d'Hebron Hospital, 08035 Barcelona, Spain
- Research Group in Urology, Vall d'Hebron Research Institute, 08035 Barcelona, Spain
| | - Miriam Campistol
- Department of Urology, Vall d'Hebron Hospital, 08035 Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
| | - Berta Miró
- Statistic Unit, Vall d'Hebron Research Institute, 08035 Barcelona, Spain
| | - Olga Méndez
- Research Group in Urology, Vall d'Hebron Research Institute, 08035 Barcelona, Spain
| | - Enrique Trilla
- Department of Urology, Vall d'Hebron Hospital, 08035 Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
- Research Group in Urology, Vall d'Hebron Research Institute, 08035 Barcelona, Spain
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23
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Lawal IO, Ndlovu H, Kgatle M, Mokoala KMG, Sathekge MM. Prognostic Value of PSMA PET/CT in Prostate Cancer. Semin Nucl Med 2024; 54:46-59. [PMID: 37482489 DOI: 10.1053/j.semnuclmed.2023.07.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
Prostate-specific membrane antigen (PSMA) is a transmembrane glycoprotein expressed in the majority of prostate cancer (PCa). PSMA has an enzymatic function that makes metabolic substrates such as folate available for utilization by PCa cells. Intracellular folate availability drives aggressive tumor phenotype. PSMA expression is, therefore, a marker of aggressive tumor biology. The large extracellular domain of PSMA is available for targeting by diagnostic and therapeutic radionuclides, making it a suitable cellular epitope for theranostics. PET imaging of radiolabeled PSMA ligands has several prognostic utilities. In the prebiopsy setting, intense PSMA avidity in a prostate lesion correlate well with clinically significant PCa (csPCa) on histology. When used for staging, PSMA PET imaging outperforms conventional imaging for the accurate staging of primary PCa, and findings on imaging predict post-treatment outcomes. The biggest contribution of PSMA PET imaging to PCa management is in the biochemical recurrence setting, where it has emerged as the most sensitive imaging modality for the localization of PCa recurrence by helping to guide salvage therapy. PSMA PET obtained for localizing the site of recurrence is prognostic, such that a higher lesion number predicts a less favorable outcome to salvage radiotherapy or surgical intervention. Systemic therapy is given to patients with advanced PCa with distant metastasis. PSMA PET is useful for predicting response to treatments with chemotherapy, first- and second-line androgen deprivation therapies, and PSMA-targeted radioligand therapy. Artificial intelligence using machine learning algorithms allows for the mining of information from clinical images not visible to the human eyes. Artificial intelligence applied to PSMA PET images, therefore, holds great promise for prognostication in PCa management.
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Affiliation(s)
- Ismaheel O Lawal
- Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA; Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa
| | - Honest Ndlovu
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Mankgopo Kgatle
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Kgomotso M G Mokoala
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa
| | - Mike M Sathekge
- Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa; Nuclear Medicine Research Infrastructure (NuMeRI), Steve Biko Academic Hospital, Pretoria, South Africa.
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24
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Song Z, Zhang W, Jiang Q, Deng L, Du L, Mou W, Lai Y, Zhang W, Yang Y, Lim J, Liu K, Park JY, Ng CF, Ong TA, Wei Q, Li L, Wei X, Chen M, Cao Z, Wang F, Chen R. Artificial intelligence-aided detection for prostate cancer with multimodal routine health check-up data: an Asian multi-center study. Int J Surg 2023; 109:3848-3860. [PMID: 37988414 PMCID: PMC10720852 DOI: 10.1097/js9.0000000000000862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 10/22/2023] [Indexed: 11/23/2023]
Abstract
BACKGROUND The early detection of high-grade prostate cancer (HGPCa) is of great importance. However, the current detection strategies result in a high rate of negative biopsies and high medical costs. In this study, the authors aimed to establish an Asian Prostate Cancer Artificial intelligence (APCA) score with no extra cost other than routine health check-ups to predict the risk of HGPCa. PATIENTS AND METHODS A total of 7476 patients with routine health check-up data who underwent prostate biopsies from January 2008 to December 2021 in eight referral centres in Asia were screened. After data pre-processing and cleaning, 5037 patients and 117 features were analyzed. Seven AI-based algorithms were tested for feature selection and seven AI-based algorithms were tested for classification, with the best combination applied for model construction. The APAC score was established in the CH cohort and validated in a multi-centre cohort and in each validation cohort to evaluate its generalizability in different Asian regions. The performance of the models was evaluated using area under the receiver operating characteristic curve (ROC), calibration plot, and decision curve analyses. RESULTS Eighteen features were involved in the APCA score predicting HGPCa, with some of these markers not previously used in prostate cancer diagnosis. The area under the curve (AUC) was 0.76 (95% CI:0.74-0.78) in the multi-centre validation cohort and the increment of AUC (APCA vs. PSA) was 0.16 (95% CI:0.13-0.20). The calibration plots yielded a high degree of coherence and the decision curve analysis yielded a higher net clinical benefit. Applying the APCA score could reduce unnecessary biopsies by 20.2% and 38.4%, at the risk of missing 5.0% and 10.0% of HGPCa cases in the multi-centre validation cohort, respectively. CONCLUSIONS The APCA score based on routine health check-ups could reduce unnecessary prostate biopsies without additional examinations in Asian populations. Further prospective population-based studies are warranted to confirm these results.
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Affiliation(s)
- Zijian Song
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
| | - Wei Zhang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Qingchao Jiang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Longxin Deng
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Le Du
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Weiming Mou
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine
| | - Yancheng Lai
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Wenhui Zhang
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
| | - Yang Yang
- Department of Clinical Laboratory, Nanjing Jinling Hospital, Nanjing University School of Medicine
| | - Jasmine Lim
- Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Kang Liu
- SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jae Young Park
- Department of Urology, Korea University Ansan Hospital, Soule, Korea
| | - Chi-Fai Ng
- SH Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Teng Aik Ong
- Department of Urology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan
| | - Lei Li
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an Shaanxi
| | - Xuedong Wei
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou
| | - Ming Chen
- Department of Urology, Zhongda Hospital, Southeast University, Nanjing
| | - Zhixing Cao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai
| | - Fubo Wang
- School of Life Sciences, Guangxi Medical University, Nanning, Guangxi
- Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi
- Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Guangxi China
| | - Rui Chen
- Department of Urology, Shanghai Changhai Hospital, Second Military Medical University
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine
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Chen J, Feng B, Hu M, Huang F, Chen Y, Ma X, Long W. A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI. BMC Med Imaging 2023; 23:200. [PMID: 38036991 PMCID: PMC10691068 DOI: 10.1186/s12880-023-01163-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Deep learning has been used to detect or characterize prostate cancer (PCa) on medical images. The present study was designed to develop an integrated transfer learning nomogram (TLN) for the prediction of PCa and benign conditions (BCs) on magnetic resonance imaging (MRI). METHODS In this retrospective study, a total of 709 patients with pathologically confirmed PCa and BCs from two institutions were included and divided into training (n = 309), internal validation (n = 200), and external validation (n = 200) cohorts. A transfer learning signature (TLS) that was pretrained with the whole slide images of PCa and fine-tuned on prebiopsy MRI images was constructed. A TLN that integrated the TLS, the Prostate Imaging-Reporting and Data System (PI-RADS) score, and the clinical factor was developed by multivariate logistic regression. The performance of the TLS, clinical model (CM), and TLN were evaluated in the validation cohorts using the receiver operating characteristic (ROC) curve, the Delong test, the integrated discrimination improvement (IDI), and decision curve analysis. RESULTS TLS, PI-RADS score, and age were selected for TLN construction. The TLN yielded areas under the curve of 0.9757 (95% CI, 0.9613-0.9902), 0.9255 (95% CI, 0.8873-0.9638), and 0.8766 (95% CI, 0.8267-0.9264) in the training, internal validation, and external validation cohorts, respectively, for the discrimination of PCa and BCs. The TLN outperformed the TLS and the CM in both the internal and external validation cohorts. The decision curve showed that the TLN added more net benefit than the CM. CONCLUSIONS The proposed TLN has the potential to be used as a noninvasive tool for PCa and BCs differentiation.
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Affiliation(s)
- Junhao Chen
- Department of Medical Imaging Center, The First Affiliated Hospital of Jinan University, 613 West Huangpu Street, Tianhe District, Guangzhou, Guangdong Province, 510630, PR China
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529000, PR China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529000, PR China
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, PR China
| | - Maoqing Hu
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529000, PR China
| | - Feidong Huang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi Province, 541004, PR China
| | - Yehang Chen
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, PR China
| | - Xilun Ma
- Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, 515000, PR China
| | - Wansheng Long
- Department of Medical Imaging Center, The First Affiliated Hospital of Jinan University, 613 West Huangpu Street, Tianhe District, Guangzhou, Guangdong Province, 510630, PR China.
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529000, PR China.
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province, 529000, PR China.
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Paulino Pereira LJ, Reesink DJ, de Bruin P, Gandaglia G, van der Hoeven EJRJ, Marra G, Prinsen A, Rajwa P, Soeterik T, Kasivisvanathan V, Wever L, Zattoni F, van Melick HHE, van den Bergh RCN. Outcomes of a Diagnostic Pathway for Prostate Cancer Based on Biparametric MRI and MRI-Targeted Biopsy Only in a Large Teaching Hospital. Cancers (Basel) 2023; 15:4800. [PMID: 37835494 PMCID: PMC10571962 DOI: 10.3390/cancers15194800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/22/2023] [Accepted: 09/26/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Diagnostic pathways for prostate cancer (PCa) balance detection rates and burden. MRI impacts biopsy indication and strategy. METHODS A prospectively collected cohort database (N = 496) of men referred for elevated PSA and/or abnormal DRE was analyzed. All underwent biparametric MRI (3 Tesla scanner) and ERSPC prostate risk-calculator. Indication for biopsy was PIRADS ≥ 3 or risk-calculator ≥ 20%. Both targeted (cognitive-fusion) and systematic cores were combined. A hypothetical full-MRI-based pathway was retrospectively studied, omitting systematic biopsies in: (1) PIRADS 1-2 but risk-calculator ≥ 20%, (2) PIRADS ≥ 3, receiving targeted biopsy-cores only. RESULTS Significant PCa (GG ≥ 2) was detected in 120 (24%) men. Omission of systematic cores in cases with PIRADS 1-2 but risk-calculator ≥ 20%, would result in 34% less biopsy indication, not-detecting 7% significant tumors. Omission of systematic cores in PIRADS ≥ 3, only performing targeted biopsies, would result in a decrease of 75% cores per procedure, not detecting 9% significant tumors. Diagnosis of insignificant PCa dropped by 52%. PCa undetected by targeted cores only, were ipsilateral to MRI-index lesions in 67%. CONCLUSIONS A biparametric MRI-guided PCa diagnostic pathway would have missed one out of six cases with significant PCa, but would have considerably reduced the number of biopsy procedures, cores, and insignificant PCa. Further refinement or follow-up may identify initially undetected cases. Center-specific data on the performance of the diagnostic pathway is required.
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Affiliation(s)
- Leonor J. Paulino Pereira
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Daan J. Reesink
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Peter de Bruin
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Giorgio Gandaglia
- Unit of Urology, Division of Oncology, Gianfranco Soldera Prostate Cancer Laboratory, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
| | - Erik J. R. J. van der Hoeven
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Giancarlo Marra
- Department of Urology, Città della Salute e della Scienza, University of Turin, 10124 Turin, Italy
| | - Anne Prinsen
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Pawel Rajwa
- Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
- Department of Urology, Medical University of Silesia, 41-800 Zabrze, Poland
| | - Timo Soeterik
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Veeru Kasivisvanathan
- Division of Surgery and Interventional Science, University College London, London WC1E 6BT, UK
| | - Lieke Wever
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Fabio Zattoni
- Urologic Unit, Department of Surgery, Oncology and Gastroenterology, University of Padova, 35122 Padua, Italy
| | - Harm H. E. van Melick
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
| | - Roderick C. N. van den Bergh
- Department of Urology, St Antonius Hospital, 3435CM Nieuwegein, The Netherlands (P.d.B.); (H.H.E.v.M.); (R.C.N.v.d.B.)
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27
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Siddiqui MR, Li EV, Kumar SKSR, Busza A, Lin JS, Mahenthiran AK, Aguiar JA, Shah PV, Ansbro B, Rich JM, Moataz SAS, Keeter MK, Mai Q, Mi X, Tosoian JJ, Schaeffer EM, Patel HD, Ross AE. Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men. Prostate Cancer Prostatic Dis 2023; 26:588-595. [PMID: 36973367 DOI: 10.1038/s41391-023-00660-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/16/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023]
Abstract
PURPOSE To develop nomograms that predict the detection of clinically significant prostate cancer (csPCa, defined as ≥GG2 [Grade Group 2]) at diagnostic biopsy based on multiparametric prostate MRI (mpMRI), serum biomarkers, and patient clinicodemographic features. MATERIALS AND METHODS Nomograms were developed from a cohort of biopsy-naïve men presenting to our 11-hospital system with prostate specific antigen (PSA) of 2-20 ng/mL who underwent pre-biopsy mpMRI from March 2018-June 2021 (n = 1494). The outcomes were the presence of csPCa and high-grade prostate cancer (defined as ≥GG3 prostate cancer). Using significant variables on multivariable logistic regression, individual nomograms were developed for men with total PSA, % free PSA, or prostate health index (PHI) when available. The nomograms were both internally validated and evaluated in an independent cohort of 366 men presenting to our hospital system from July 2021-February 2022. RESULTS 1031 of 1494 men (69%) underwent biopsy after initial evaluation with mpMRI, 493 (47.8%) of whom were found to have ≥GG2 PCa, and 271 (26.3%) were found to have ≥GG3 PCa. Age, race, highest PIRADS score, prostate health index when available, % free PSA when available, and PSA density were significant predictors of ≥GG2 and ≥GG3 PCa on multivariable analysis and were used for nomogram generation. Accuracy of nomograms in both the training cohort and independent cohort were high, with areas under the curves (AUC) of ≥0.885 in the training cohort and ≥0.896 in the independent validation cohort. In our independent validation cohort, our model for ≥GG2 prostate cancer with PHI saved 39.1% of biopsies (143/366) while only missing 0.8% of csPCa (1/124) with a biopsy threshold of 20% probability of csPCa. CONCLUSIONS Here we developed nomograms combining serum testing and mpMRI to help clinicians risk stratify patients with elevated PSA of 2-20 ng/mL who are being considered for biopsy. Our nomograms are available at https://rossnm1.shinyapps.io/MynMRIskCalculator/ to aid with biopsy decisions.
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Affiliation(s)
- Mohammad R Siddiqui
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Eric V Li
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Sai K S R Kumar
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Busza
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jasmine S Lin
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ashorne K Mahenthiran
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jonathan A Aguiar
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Parth V Shah
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Brandon Ansbro
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jordan M Rich
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Soliman A S Moataz
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Mary-Kate Keeter
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Quan Mai
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Xinlei Mi
- Department of Preventative Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Edward M Schaeffer
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Hiten D Patel
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ashley E Ross
- Department of Urology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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28
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Wang Y, Wang L, Tang X, Zhang Y, Zhang N, Zhi B, Niu X. Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies. BMC Med Imaging 2023; 23:106. [PMID: 37582697 PMCID: PMC10426075 DOI: 10.1186/s12880-023-01074-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/03/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Biparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa). METHODS The patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score. RESULTS Logistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade. CONCLUSION A predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.
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Affiliation(s)
- Yunhan Wang
- Department of Urology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China
| | - Lei Wang
- Department of Radiology, Ninety-Three Hospital, Jiangyou City, 610000, Sichuan, China
| | - Xiaohua Tang
- Department of Radiology, Ninety-Three Hospital, Jiangyou City, 610000, Sichuan, China
| | - Yong Zhang
- Department of Radiology, DeYang People's Hospital, Deyang City, 610000, Sichuan, China
| | - Na Zhang
- Department of General Practice Medicine, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China
| | - Biao Zhi
- Department of Interventional Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China
| | - Xiangke Niu
- Department of Interventional Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, Sichuan, China.
- Department of Interventional Radiology, School of Medicine, Sichuan Cancer Hospital & Research Institute, University of Electronic Science and Technology of China (UESTC), Chengdu, 610041, China.
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29
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Morote J, Pye H, Campistol M, Celma A, Regis L, Semidey M, de Torres I, Mast R, Planas J, Santamaria A, Trilla E, Athanasiou A, Singh S, Heavey S, Stopka-Farooqui U, Freeman A, Haider A, Schiess R, Whitaker HC, Punwani S, Ahmed HU, Emberton M. Accurate diagnosis of prostate cancer by combining Proclarix with magnetic resonance imaging. BJU Int 2023; 132:188-195. [PMID: 36855895 DOI: 10.1111/bju.15998] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
OBJECTIVES To assess of the clinical performance of Proclarix® (a novel Conformité Européenne [CE]-marked biomarker test aiding in the identification of clinically significant prostate cancer [csPCa]) alone or in combination with multiparametric magnetic resonance imaging (mpMRI) to predict csPCa (International Society of Urological Pathology Grade Group ≥2). PATIENTS AND METHODS The study included blood samples from 721 men undergoing mpMRI followed by biopsy at University College London, London, and Vall d'Hebron University Hospital, Barcelona. Samples were tested blindly. The Proclarix-MRI model combining prostate volume, Proclarix and mpMRI results was trained using the UCL cohort (n = 159) and validated in the Vall d'Hebron cohort (n = 562). Its diagnostic performance was established in correlation to biopsy outcome and compared to available clinical parameters and risk calculators. RESULTS Clinical performance of the Proclarix-MRI model in the validation cohort did not significantly differ from the training cohort and resulted in a sensitivity for csPCa of 90%, 90% negative predictive value and 66% positive predictive value. The Proclarix-MRI score's specificity (68%) was significantly (P < 0.001) better than the MRI-European Randomized study of Screening for Prostate Cancer risk score (51%), Proclarix (27%) or mpMRI (28%) alone. In addition, Proclarix by itself was found to be useful in the MRI Prostate Imaging-Reporting and Data System (PI-RADS) score 3 subgroup by outperforming prostate-specific antigen density in terms of specificity (25% vs 13%, P = 0.004) at 100% sensitivity. CONCLUSION When combined with mpMRI and prostate volume, Proclarix reliably predicted csPCa and ruled out men with no or indolent cancer. A large reduction of two thirds of unneeded biopsies was achieved. Proclarix can further be used with high confidence to reliably detect csPCa in men with an indeterminate PI-RADS score 3 mpMRI. Despite these encouraging results, further validation is needed.
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Affiliation(s)
- Juan Morote
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | - Miriam Campistol
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anna Celma
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Lucas Regis
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria Semidey
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ines de Torres
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Richard Mast
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jacques Planas
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Anna Santamaria
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Enrique Trilla
- Vall d'Hebron Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Saurabh Singh
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | - Susan Heavey
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | | | - Alex Freeman
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | - Aiman Haider
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | | | - Hayley C Whitaker
- Molecular Diagnostics and Therapeutics Group, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Hashim U Ahmed
- Imperial Prostate, Department of Surgery and Cancer, Imperial College London, London, UK
- Imperial Urology, Imperial College Healthcare NHS Trust, London, UK
| | - Mark Emberton
- Division of Surgery and Interventional Science, University College London, London, UK
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30
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Kelly BD, Ptasznik G, Roberts MJ, Doan P, Stricker P, Thompson J, Buteau J, Chen K, Alghazo O, O'Brien JS, Hofman MS, Frydenberg M, Lawrentschuk N, Lundon D, Murphy DG, Emmett L, Moon D. A Novel Risk Calculator Incorporating Clinical Parameters, Multiparametric Magnetic Resonance Imaging, and Prostate-Specific Membrane Antigen Positron Emission Tomography for Prostate Cancer Risk Stratification Before Transperineal Prostate Biopsy. EUR UROL SUPPL 2023; 53:90-97. [PMID: 37441340 PMCID: PMC10334234 DOI: 10.1016/j.euros.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2023] [Indexed: 07/15/2023] Open
Abstract
Background Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) can detect multiparametric magnetic resonance imaging (mpMRI)-invisible prostate tumours and improve the sensitivity of detection of prostate cancer (PCa) in comparison to mpMRI alone. Numerous risk calculators have been validated as tools for stratification of men at risk of being diagnosed with clinically significant (cs)PCa. Objective To develop a novel risk calculator using clinical parameters and imaging parameters from mpMRI and PSMA PET/CT in a cohort of patients undergoing mpMRI and PSMA PET/CT before biopsy. Design setting and participants A total of 291 men from the PRIMARY prospective trial underwent mpMRI and PSMA PET/CT before transperineal prostate biopsy with sampling of systematic and targeted cores. Outcome measurements and statistical analysis Novel risk calculators were developed using multivariable logistic regression analysis to predict detection of overall PCa (International Society of Urological Pathology grade group [GG] ≥1) and csPCa (GG ≥2). The risk calculators were then compared with the European Randomised Study of Screening for Prostate Cancer risk calculator incorporating mpMRI (ERSPC-MRI). Resampling methods were used to evaluate the discrimination and calibration of the risk calculators and to perform decision curve analysis. Results and limitations Age, prostate-specific antigen, prostate volume, and mpMRI Prostate Imaging-Reporting and Data System scores were included in the MRI risk calculator, resulting in area under the receiver operating characteristic curve (AUC) values of 0.791 for overall PCa (GG ≥1) and 0.812 for csPCa (GG ≥2). Addition of the maximum standardised uptake value (SUVmax) on PSMA PET/CT for the prostate lesion, and of SUVmax for the mpMRI lesions for the MRI-PSMA risk calculator resulted in AUCs of 0.831 for overall PCa and 0.876 for csPCa (≥ISUP2).The ERSPC-MRI risk calculator had AUCs of 0.758 (p = 0.02) for overall PCa and 0.805 (p = 0.001) for csPCa. Both the MRI and MRI-PSMA risk calculators were superior to the ERSPC-MRI for both overall PCa and csPCa. Conclusions These novel risk calculators incorporate clinical and radiological parameters for stratification of men at risk of csPCa. The risk calculator including PSMA PET/CT data is superior to a calculator incorporating mpMRI data alone. Patient summary We evaluated a new risk calculator that uses clinical information and results from two types of scan to predict the risk of clinically significant prostate cancer on prostate biopsy. This risk model can guide patients and clinicians in shared decision-making and may help in avoiding unnecessary prostate biopsies.
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Affiliation(s)
- Brian D. Kelly
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
- Department of Urology, Eastern Health, Melbourne, Australia
| | - Gideon Ptasznik
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | | | - Paul Doan
- Garvan Institute of Medical Research, Darlinghurst, Australia
| | | | | | - James Buteau
- Department of Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging and Prostate Cancer Theranostics and Imaging Centre of Excellence, Peter MacCallum Cancer, Melbourne, Australia
| | - Kenneth Chen
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Omar Alghazo
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Jonathan S. O'Brien
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Michael S. Hofman
- Department of Molecular Imaging and Therapeutic Nuclear Medicine, Cancer Imaging and Prostate Cancer Theranostics and Imaging Centre of Excellence, Peter MacCallum Cancer, Melbourne, Australia
| | - Mark Frydenberg
- Department of Surgery, Monash University and Cabrini Institute, Cabrini Health, Melbourne, Australia
| | - Nathan Lawrentschuk
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Dara Lundon
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Declan G. Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Louise Emmett
- Department of Theranostics and Nuclear Medicine, St. Vincent’s Hospital Sydney, Darlinghurst, Australia
| | - Daniel Moon
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
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Morote J, Picola N, Paesano N, Celma A, Muñoz-Rodriguez J, Asiain I, Ruiz-Plazas X, Muñoz-Rivero MV, de Manuel GG, Servian P, Abascal JM. Are magnetic resonance imaging and targeted biopsies needed in men with serum prostate-specific antigen over 10 ng/ml and an abnormal digital rectal examination? Urol Oncol 2023; 41:299-301. [PMID: 37244767 DOI: 10.1016/j.urolonc.2023.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/12/2023] [Accepted: 05/04/2023] [Indexed: 05/29/2023]
Abstract
The European Association of Urology currently recommends the use of risk-organized models to decrease the demand of prebiopsy magnetic resonance imaging (MRI) and unnecessary prostate biopsies in men with suspected prostate cancer (CaP). Low evidence suggests that men with prostate-specific antigen >10 ng/ml and an abnormal digital rectal examination (DRE) do not benefit from prebiopsy MRI and targeted biopsies. We aim to validate this low evidence in a sizable cohort and knowing how many clinically significant CaP (csCaP) would go undetected if only random biopsies were performed in these cases. We analyze a subset of 545 men with PSA >10 ng/ml and an abnormal DRE who met the previous criteria among 5,329 participants in a prospective trial in whom random biopsy was always performed and targeted biopsies of PI-RADS ≥3 lesions (10.2%). CsCaP (grade group ≥2) was detected in 370 men (67.9%), with 11 of 49 with negative MRI (22.5%) and 359 of 496 (72.4%) having PI-RADS ≥3. CsCaP was identified in random and targeted biopsies in 317 (88.7%) men, in targeted biopsies only in 23 (6.4%), and in random biopsies only in 19 (5.3%). If only random biopsies were performed in these men, 23 of overall 1,914 csCaP (1.2%) would go undetected in this population. Prebiopsy MRI can be saved in men with serum PSA >10 ng/ml and an abnormal DRE and only random biopsy performed. However, a close follow-up of men with negative random biopsy seems appropriate due to the high-risk of csCaP in these men.
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Affiliation(s)
- Juan Morote
- Department of Urology, Vall d'Hebron Hospital, and Department of Surgery, Universitat Autònoma de Barcelona, Barcelona, Spain.
| | - Natàlia Picola
- Department of Urology, Hospital Universitari de Bellvitge, Hospitalet de Llobregat, Spain
| | | | - Anna Celma
- Department of Urology, Vall d'Hebron Hospital, and Department of Surgery, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Ignacio Asiain
- Department of Urology, Hospital Clinic, Barcelona, Spain
| | - Xavier Ruiz-Plazas
- Deparment of Utology, Hospital Universitari Joan XXIII, Tarragona, Spain
| | | | | | - Pol Servian
- Department of Urology, Hospital Germans Trias i Pujol, Badalona, Spain
| | - José M Abascal
- Department of Urology, Parc de Salut Mar, Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
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Morote J, Borque-Fernando Á, Triquell M, Campistol M, Servian P, Abascal JM, Planas J, Méndez O, Esteban LM, Trilla E. Comparison of Rotterdam and Barcelona Magnetic Resonance Imaging Risk Calculators for Predicting Clinically Significant Prostate Cancer. EUR UROL SUPPL 2023; 53:46-54. [PMID: 37441350 PMCID: PMC10334241 DOI: 10.1016/j.euros.2023.03.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2023] [Indexed: 07/15/2023] Open
Abstract
Background Magnetic resonance imaging (MRI)-based risk calculators (MRI-RCs) individualise the likelihood of clinically significant prostate cancer (csPCa) and improve candidate selection for prostate biopsy beyond the Prostate Imaging Reporting and Data System (PI-RADS). Objective To compare the Barcelona (BCN) and Rotterdam (ROT) MRI-RCs in an entire population and according to the PI-RADS categories. Design setting and participants A prospective comparison of BCN- and ROT-RC in 946 men with suspected prostate cancer in whom systematic biopsy was performed, as well as target biopsies of PI-RADS ≥3 lesions. Outcome measurements and statistical analysis Saved biopsies and undetected csPCa (grade group ≥2) were determined. Results and limitations The csPCa detection was 40.8%. The median risks of csPCa from BCN- and ROT-RC were, respectively, 67.1% and 25% in men with csPCa, whereas 10.5% and 3% in those without csPCa (p < 0.001). The areas under the curve were 0.856 and 0.844, respectively (p = 0.116). BCN-RC showed a higher net benefit and clinical utility over ROT-RC. Using appropriate thresholds, respectively, 75% and 80% of biopsies were needed to identify 50% of csPCa detected in men with PI-RADS <3, whereas 35% and 21% of biopsies were saved, missing 10% of csPCa detected in men with PI-RADS 3. BCN-RC saved 15% of biopsies, missing 2% of csPCa in men with PI-RADS 4, whereas ROT-RC saved 10%, missing 6%. No RC saved biopsies without missing csPCa in men with PI-RADS 5. Conclusions ROT-RC provided a lower and narrower range of csPCa probabilities than BCN-RC. BCN-RC showed a net benefit over ROT-RC in the entire population. However, BCN-RC was useful in men with PI-RADS 3 and 4, whereas ROT-RC was useful only in those with PI-RADS 3. No RC seemed to be helpful in men with negative MRI and PI-RADS 5. Patient summary Barcelona risk calculator was more helpful than Rotterdam risk calculator to select candidates for prostate biopsy.
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Affiliation(s)
- Juan Morote
- Department of Urology, Vall d́Hebron Hospital, Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | | | - Marina Triquell
- Department of Urology, Vall d́Hebron Hospital, Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Miriam Campistol
- Department of Urology, Vall d́Hebron Hospital, Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Pol Servian
- Department of Urology, Hospital Germans Trias I Pujol, Badalona, Spain
| | - José M. Abascal
- Department of Urology, Parc de Salut Mar, Barcelona, Spain
- Department of Surgery, Universitat Pompeu Fabra, Badalona, Spain
| | - Jacques Planas
- Department of Urology, Vall d́Hebron Hospital, Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Olga Méndez
- Biomedical Research in Urology Unit, Vall d́Hebron Research Institute, Barcelona, Spain
| | - Luis M. Esteban
- Department of Applied Mathematics, Escuela Universitaria Politécnica La Almunia, Universidad de Zaragoza, Zaragoza, Spain
| | - Enrique Trilla
- Department of Urology, Vall d́Hebron Hospital, Barcelona, Spain
- Department of Surgery, Universitat Autònoma de Barcelona, Bellaterra, Spain
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Díaz-Fernández F, Celma A, Salazar A, Moreno O, López C, Cuadras M, Regis L, Planas J, Morote J, Trilla E. Systematic review of methods used to improve the efficacy of magnetic resonance in early detection of clinically significant prostate cancer. Actas Urol Esp 2023; 47:127-139. [PMID: 36462603 DOI: 10.1016/j.acuroe.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 04/28/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND AND OBJECTIVE Prostate cancer (PC) is the malignant neoplasm with the highest incidence after lung cancer worldwide. The objective of this study is to review the literature on the methods that improve the efficacy of the current strategy for the early diagnosis of clinically significant PC (csPC), based on the performance of magnetic resonance imaging (RM) and targeted biopsies when suspicious lesions are detected, in addition to systematic biopsy. EVIDENCE ACQUISITION A systematic literature review was performed in PubMed, Web of Science and Cochrane according to the PRISMA criteria (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), using the search terms: multiparametric magnetic resonance imaging, biparametric magnetic resonance imaging, biomarkers in prostate cancer, prostate cancer y early diagnosis. A total of 297 references were identified and, using the PICO selection criteria, 21 publications were finally selected to synthesize the evidence. EVIDENCE SYNTHESIS With the consolidation of MRI as the test of choice for the diagnosis of prostate cancer, the role of PSA density (PSAD) becomes relevant as a predictive tool included in prediction nomograms, without added cost. PSAD and diagnostic markers, combined with MRI, offer a high diagnostic power with an area under curve (AUC) above 0.7. Only the SHTLM3 model integrates markers in the creation of a nomogram. Prediction models also offer consistent efficacy with an AUC greater than 0.8 when associating MRI. CONCLUSIONS The efficacy of MRI in clinically significant prostate cancer detection can be improved with different parameters in order to generate predictive models that support decision making.
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Affiliation(s)
- F Díaz-Fernández
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
| | - A Celma
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - A Salazar
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - O Moreno
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - C López
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - M Cuadras
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - L Regis
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - J Planas
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - J Morote
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Universistat Autònoma de Barcelona, Barcelona, Spain
| | - E Trilla
- Departamento de Urología y Trasplante Renal, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Universistat Autònoma de Barcelona, Barcelona, Spain
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Yang J, Tang Y, Zhou C, Zhou M, Li J, Hu S. The use of 68 Ga-PSMA PET/CT to stratify patients with PI-RADS 3 lesions according to clinically significant prostate cancer risk. Prostate 2023; 83:430-439. [PMID: 36544382 DOI: 10.1002/pros.24475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 11/13/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Prostate imaging reporting and data system (PI-RADS) category 3 lesions represent a "gray zone," having an equivocal risk of presenting as clinically significant prostate cancer (csPCa). 68 Ga-labelled prostate-specific membrane antigen (68 Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has been identified as a diagnostic tool that can help to predict cases of primary PCa. We aimed to explore diagnostic value of 68 Ga-PSMA PET/CT for csPCa in PI-RADS 3 lesions to aid in decision-making and avoid unnecessary biopsies. METHODS A total of 78 men with PI-RADS 3 lesions who underwent both 68 Ga-PSMA PET/CT and transrectal ultrasound/magnetic resonance imaging (MRI) fusion-guided biopsy were enrolled. Images were analyzed by respective physicians who were blinded to the pathological results. Receiver operating characteristic (ROC) curve analysis and decision curve analysis were used to evaluate the diagnostic performance of univariate and multivariate analyses. RESULTS A total of 26/78 men had pathologically confirmed csPCa. A lower ADCT/ADCCLP (0.65 vs. 0.71, p = 0.018), smaller prostate volume (25.27 vs. 42.79 ml, p < 0.001), lower free prostate-specific antigen/total prostate-specific antigen (0.11 vs. 0.16, p < 0.001), higher PSA level (13.45 vs. 7.90 ng/ml, p = 0.001), higher PSA density (0.40 vs. 0.16 ng/ml2 , p < 0.001), higher SUVmax (9.80 vs. 4.40, p < 0.001) and SUVT/BGp (2.41 vs. 1.00, p < 0.001) were associated with csPCa. ROC analysis illustrated the improvement in SUVmax and SUVT/BGp compared with all independent and combined clinical features as well as multiparametric magnetic resonance imaging (mpMRI) features for csPCa detection. The net benefits of SUVmax and SUVT/BGp were superior to those of other features, respectively. With cutoff values of 5.0 for SUVmax and 1.4 for SUVT/BGp, the diagnostic sensitivity and specificity for csPCa were 96.2%, 100% and 80.8%, 84.6%, respectively. CONCLUSION 68 Ga-PSMA PET/CT is potentially capable of stratifying men with PI-RADS 3 lesions according to the presence of csPCa and has better performance than the model established based on clinical and mpMRI features.
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Affiliation(s)
- Jinhui Yang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chuanchi Zhou
- Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ming Zhou
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Li
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Xiangya Hospital, Central South University, Changsha, Hunan, China
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Noh TI, Shim JS, Kang SH, Cheon J, Kang SG. Diagnostic performance of transperineal prostate targeted biopsy alone according to the PI-RADS score based on bi-parametric magnetic resonance imaging. Front Oncol 2023; 13:1142022. [PMID: 37035173 PMCID: PMC10080665 DOI: 10.3389/fonc.2023.1142022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/10/2023] [Indexed: 04/11/2023] Open
Abstract
Purpose To compare the diagnostic performance of transperineal targeted biopsy (TB) or systematic biopsy (SB) alone based on combined TB+SB and radical prostatectomy (RP) specimen for detecting prostate cancer (PCa) according to the prostate imaging reporting and data system (PI-RADS) score. Materials and methods This study included 1077 men who underwent transperineal bi-parametric (bp) magnetic resonance imaging (MRI)-ultrasound (US) fusion TB+SB (bpMRI-US FTSB) between April 2019 and March 2022. To compare the performance of each modality (TB, SB, and combined TB+SB) with the RP specimen (as the standard) for detecting PCa and clinically significant PCa (csPCa), receiver operating characteristic (ROC) curves were plotted. Results PCa was detected in 581 of 1077 men (53.9%) using bpMRI-US FTSB. CsPCa was detected in 383 of 1077 men (35.6%), 17 of 285 (6.0%) with PI-RADS 0 to 2, 35 of 277 (12.6%) with PI-RADS 3, 134 of 274 (48.9%) with PI-RADS 4, and 197 of 241 (81.7%) with PI-RADS 5, respectively. The additional diagnostic value of TB vs. SB compared to combined TB+SB for diagnosing csPCa were 4.3% vs. 3.2% (p=0.844), 20.4% vs 5.1% (p<0.001), and 20.3% vs. 0.7% (p<0.001) with PI-RADS 3, 4, and 5, respectively. TB alone showed no significant difference in diagnostic performance for csPCa with combined TB+SB based on RP specimens in patients with PI-RADS 5 (p=0.732). Conclusion A need for addition of SB to TB in patients with PI-RADS 3 and 4 lesions, however, TB alone may be performed without affecting the management of patients with PI-RADS 5.
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Affiliation(s)
| | | | | | | | - Sung Gu Kang
- Department of Urology, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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36
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Handke AE, Albers P, Schimmöller L, Bonekamp D, Asbach P, Schlemmer HP, Hadaschik BA, Radtke JP. [Systematic or targeted fusion-guided biopsy]. UROLOGIE (HEIDELBERG, GERMANY) 2023; 62:464-472. [PMID: 36941382 DOI: 10.1007/s00120-023-02062-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/16/2023] [Indexed: 03/23/2023]
Abstract
BACKGROUND Early detection of prostate cancer (PCa) is associated with a high risk for detecting low-risk disease. In the primary biopsy indication, systematic biopsy leads to an increased detection of clinically insignificant PCa, and significant prostate cancers are not detected with sufficient sensitivity, especially without prior magnetic resonance imaging (MRI). Similar data have recently become available for PCa screening. OBJECTIVES In light of the current literature, this article aims to discuss the data on systematic and combined targeted and systematic multiparametric MRI (mpMRI)-guided fusion biopsy to improve PCa diagnosis in clinically suspected cancer even in screening using multivariable risk stratification. MATERIALS AND METHODS Literature review on mpMRI and MRI/TRUS fusion biopsy (TRUS: transrectal ultrasonography) for tumor detection in suspected prostate cancer and PCa screening was performed. RESULTS Multiparametric MRI as a reflex test after prostate-specific antigen (PSA) determination (PSA cut-off 4 ng/ml) in combination with targeted biopsy alone reduces the detection of clinically nonsignificant tumors in early detection by half. On the other hand, in the form of a target saturation or in combination with a systematic biopsy, the sensitivity for the detection of cancers of International Society of Urogenital Pathology (ISUP) grade groups 2 or higher can be improved. Similar results are also shown in PCa screening with a PSA cut-off of 3 ng/ml. CONCLUSIONS The evidence for performing a targeted fusion biopsy alone is currently insufficient. Therefore, the combination of mpMRI-guided targeted and systematic biopsy continues to be the recommended standard for prostate cancer diagnosis.
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Affiliation(s)
- Analena Elisa Handke
- Urologische Klinik, Universitätsklinikum Essen, Essen, Deutschland
- Deutsches Konsortium für Translationale Krebsforschung, Essen, Deutschland
| | - Peter Albers
- Klinik für Urologie, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
- Abteilung für Personalisierte Früherkennung des Prostatakarzinoms, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - Lars Schimmöller
- Medizinische Fakultät, Institut für Diagnostische und Interventionelle Radiologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland
| | - David Bonekamp
- Abteilung Radiologie, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - Patrick Asbach
- Klinik für Radiologie, Charité Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Deutschland
| | - Heinz-Peter Schlemmer
- Abteilung Radiologie, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland
| | - Boris A Hadaschik
- Urologische Klinik, Universitätsklinikum Essen, Essen, Deutschland
- Deutsches Konsortium für Translationale Krebsforschung, Essen, Deutschland
| | - Jan Philipp Radtke
- Klinik für Urologie, Medizinische Fakultät, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Deutschland.
- Abteilung Radiologie, Deutsches Krebsforschungszentrum (dkfz), Heidelberg, Deutschland.
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37
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Krilaviciute A, Albers P, Lakes J, Radtke JP, Herkommer K, Gschwend J, Peters I, Kuczyk M, Koerber SA, Debus J, Kristiansen G, Schimmöller L, Antoch G, Makowski M, Wacker F, Schlemmer H, Benner A, Giesel F, Siener R, Arsov C, Hadaschik B, Becker N, Kaaks R. Adherence to a risk-adapted screening strategy for prostate cancer: First results of the PROBASE trial. Int J Cancer 2023; 152:854-864. [PMID: 36121664 DOI: 10.1002/ijc.34295] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 01/11/2023]
Abstract
PROBASE is a population-based, randomized trial of 46 495 German men recruited at age 45 to compare effects of risk-adapted prostate cancer (PCa) screening starting either immediately at age 45, or at a deferred age of 50 years. Based on prostate-specific antigen (PSA) levels, men are classified into risk groups with different screening intervals: low-risk (<1.5 ng/ml, 5-yearly screening), intermediate-risk (1.5-2.99 ng/ml, 2 yearly), and high risk (>3 ng/ml, recommendation for immediate biopsy). Over the first 6 years of study participation, attendance rates to scheduled screening visits varied from 70.5% to 79.4%, depending on the study arm and risk group allocation, in addition 11.2% to 25.4% of men reported self-initiated PSA tests outside the PROBASE protocol. 38.5% of participants had a history of digital rectal examination or PSA testing prior to recruitment to PROBASE, frequently associated with family history of PCa. These men showed higher rates (33% to 57%, depending on subgroups) of self-initiated PSA testing in-between PROBASE screening rounds. In the high-risk groups (both arms), the biopsy acceptance rate was 64% overall, but was higher among men with screening PSA ≥4 ng/ml (>71%) and with PIRADS ≥3 findings upon multiparameter magnetic resonance imaging (mpMRI) (>72%), compared with men with PSA ≥3 to 4 ng/ml (57%) or PIRADS score ≤ 2 (59%). Overall, PROBASE shows good acceptance of a risk-adapted PCa screening strategy in Germany. Implementation of such a strategy should be accompanied by a well-structured communication, to explain not only the benefits but also the harms of PSA screening.
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Affiliation(s)
- Agne Krilaviciute
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Albers
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Department of Urology, University Hospital, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Jale Lakes
- Department of Urology, University Hospital, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Jan Philipp Radtke
- Department of Urology, University Hospital, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Kathleen Herkommer
- Department of Urology, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munchen, Germany
| | - Jürgen Gschwend
- Department of Urology, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munchen, Germany
| | - Inga Peters
- Department of Urology, Medical University Hannover, Hannover, Germany.,Department of Urology, Krankenhaus Nordwest, Frankfurt am Main, Germany
| | - Markus Kuczyk
- Department of Urology, Medical University Hannover, Hannover, Germany
| | - Stefan A Koerber
- Department of Radiation Oncology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany
| | | | - Lars Schimmöller
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Marcus Makowski
- Institute of Diagnostic and Interventional Radiology, Technical University Munich, München, Germany
| | - Frank Wacker
- Institute of Diagnostic and Interventional Radiology, Medical University Hannover, Hannover, Germany
| | - Heinz Schlemmer
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik Giesel
- Department of Nuclear Medicine, Medical Faculty, Duesseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Roswitha Siener
- Department of Radiation Oncology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany.,Department of Urology, University Hospital Bonn, Bonn, Germany
| | - Christian Arsov
- Department of Urology, University Hospital, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Boris Hadaschik
- Department of Urology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany
| | - Nikolaus Becker
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Bao J, Hou Y, Qin L, Zhi R, Wang XM, Shi HB, Sun HZ, Hu CH, Zhang YD. High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade. Br J Cancer 2023; 128:1267-1277. [PMID: 36646808 PMCID: PMC10050457 DOI: 10.1038/s41416-022-02134-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 12/11/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa). METHODS This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms. RESULTS PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction. CONCLUSIONS PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.
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Affiliation(s)
- Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China
| | - Ying Hou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Lang Qin
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Rui Zhi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Xi-Ming Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China
| | - Hai-Bin Shi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Chun-Hong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188N, Shizi Road, 215006, Suzhou, Jiangsu, China.
| | - Yu-Dong Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300N, Guangzhou Road, 210029, Nanjing, Jiangsu, China.
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Pellegrino F, Tin AL, Martini A, Vertosick EA, Porwal SP, Stabile A, Gandaglia G, Eastham JA, Briganti A, Montorsi F, Vickers AJ. Prostate-specific Antigen Density Cutoff of 0.15 ng/ml/cc to Propose Prostate Biopsies to Patients with Negative Magnetic Resonance Imaging: Efficient Threshold or Legacy of the Past? Eur Urol Focus 2023; 9:291-297. [PMID: 36270887 PMCID: PMC10578357 DOI: 10.1016/j.euf.2022.10.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 08/29/2022] [Accepted: 10/03/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND A prostate-specific antigen density (PSAd) cutoff of 0.15 ng/ml/cc is a commonly recommended threshold to identify patients with negative prostate magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We were unable to find any study that explicitly examined the properties of this threshold compared with others. OBJECTIVE To investigate whether the 0.15 cutoff is justified for selecting patients at risk of harboring high-grade cancer (Gleason score ≥3 + 4) despite negative MRI. DESIGN, SETTING, AND PARTICIPANTS A cohort of 8974 prostate biopsies provided by the Prostate Biopsy Collaborative Group (PBCG) was included in the study. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Locally weighted scatterplot smoothing was used to investigate whether there was a change in the risk of high-grade cancer around this value. We examined whether the use of this cutoff in patients with negative MRI corresponds to a reasonable threshold probability for a biopsy (defined as a 10% risk of high-grade disease). To do so, we applied the negative likelihood ratio of MRI, calculated from eight studies on prostate MRI, to the risk curve derived from the PBCG. RESULTS AND LIMITATIONS There was no discontinuity in the risk of high-grade prostate cancer at a PSAd cutoff of 0.15. This cutoff corresponded to a probability of high-grade disease ranging from 2.6% to 10%, depending on MRI accuracy. Using 10% as threshold probability, the corresponding PSAd cutoff varied between 0.15 and 0.38, with the threshold increasing for greater MRI accuracy. Possible limitations include difference between studies on MRI and the use of ultrasound to measure prostate volume. CONCLUSIONS The 0.15 cutoff to recommend prostate biopsies in patients with negative MRI is justified only under an extreme scenario of poor MRI properties. We recommend a value of at least ≥0.20. Our results suggest the need for future studies to look at how to best identify patients who need prostate biopsies despite negative MRI, likely by using individualized risk prediction. PATIENT SUMMARY In this study, we investigated whether the commonly used prostate-specific antigen density cutoff of 0.15 is justified to identify patients with negative magnetic resonance imaging (MRI) who should proceed to a prostate biopsy. We found that this cutoff is appropriate only in case of very poor MRI quality, and a higher cutoff (≥0.20) should be used for the average MRI.
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Affiliation(s)
- Francesco Pellegrino
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy; Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Amy L Tin
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alberto Martini
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy
| | - Emily A Vertosick
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shaun P Porwal
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Armando Stabile
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy
| | - Giorgio Gandaglia
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy
| | - James A Eastham
- Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alberto Briganti
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy
| | - Francesco Montorsi
- Division of Oncology/Unit of Urology, IRCCS San Raffaele Hospital, Urological Research Institute, Milan, Italy
| | - Andrew J Vickers
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Patel HD, Koehne EL, Shea SM, Fang AM, Gerena M, Gorbonos A, Quek ML, Flanigan RC, Goldberg A, Rais‐Bahrami S, Gupta GN. A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators. BJU Int 2023; 131:227-235. [PMID: 35733400 PMCID: PMC9772358 DOI: 10.1111/bju.15835] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVES To develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the Prostate Biopsy Collaborative Group (PBCG) RC. PATIENTS AND METHODS Men without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015-2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging-Reporting Data System [PI-RADS] version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver-operating characteristic curve, calibration curves, and decision-curve analysis were evaluated in the training and validation cohorts. RESULTS A total of 1010 patients were included for development (N = 674 training [47.8% PCa, 30.9% csPCa], N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve [AUC] 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI-European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05). CONCLUSIONS The mpMRI-based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI-based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.
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Affiliation(s)
- Hiten D. Patel
- Department of UrologyLoyola University Medical CenterMaywoodILUSA
- Department of UrologyFeinberg School of Medicine, Northwestern UniversityChicagoILUSA
| | | | - Steven M. Shea
- Department of RadiologyLoyola University Medical CenterMaywoodILUSA
| | - Andrew M. Fang
- Department of UrologyUniversity of Alabama at BirminghamBirminghamALUSA
| | - Marielia Gerena
- Department of RadiologyLoyola University Medical CenterMaywoodILUSA
| | - Alex Gorbonos
- Department of UrologyLoyola University Medical CenterMaywoodILUSA
| | - Marcus L. Quek
- Department of UrologyLoyola University Medical CenterMaywoodILUSA
| | | | - Ari Goldberg
- Department of RadiologyLoyola University Medical CenterMaywoodILUSA
| | - Soroush Rais‐Bahrami
- Department of UrologyUniversity of Alabama at BirminghamBirminghamALUSA
- Department of RadiologyUniversity of Alabama at BirminghamBirminghamALUSA
- O'Neal Comprehensive Cancer CenterUniversity of Alabama at BirminghamBirminghamALUSA
| | - Gopal N. Gupta
- Department of UrologyLoyola University Medical CenterMaywoodILUSA
- Department of RadiologyLoyola University Medical CenterMaywoodILUSA
- Department of SurgeryLoyola University Medical CenterMaywoodILUSA
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Natural History of Patients with Prostate MRI Likert 1-3 and Development of RosCaP: a Multivariate Risk Score for Clinically Significant Cancer. Clin Genitourin Cancer 2023; 21:162-170. [PMID: 35970760 DOI: 10.1016/j.clgc.2022.07.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/21/2022] [Accepted: 07/25/2022] [Indexed: 02/01/2023]
Abstract
INTRODUCTION Clinically significant prostate cancer (csCaP) with Gleason ≥3 + 4 is found in 10% negative prebiopsy multiparametric (mp) MRI cases and varies widely for equivocal mpMRI cases. The objective of this study was to investigate long-term outcomes of patients with negative and equivocal mpMRIs and to develop a predictive score for csCaP risk stratification in this group. PATIENTS AND METHODS Patients who underwent an upfront mpMRI between May 2015 and March 2018 with an MRI score Likert 1 to 3 were included in the study. Patients had either a CaP diagnosis at MRI-targeted biopsy or were not diagnosed and attended follow-up in the community. Outcomes were analysed through the Kaplan-Meier estimator and Cox Model. Regression coefficients of significant variables were used to develop a Risk of significant Cancer of the Prostate score (RosCaP). RESULTS At first assessment 281/469 patients had mpMRI only and 188/469 mpMRI and biopsy, 26 csCaP were found at biopsy, including 10/26 in Likert 3 patients. 12/371 patients discharged without CaP after first assessment were diagnosed with csCaP during a median of 34.2 months' follow-up, 11/12 diagnosis occurred in patients omitting initial biopsy. csCaP diagnosis-free survival was 95.7% in the MRI group and 99.1% in the biopsy group. From these outcomes, a continuous RosCaP score was developed: RosCaP = 0.083 x Age - 0.202 x (1/PSA Density) + 0.786 (if Likert 3), and 4 risk classes were proposed. Limitations include retrospective design and absence of external validation. CONCLUSION Age, PSA Density and MRI Likert score were significantly associated to the risk of csCaP and utilised to devise the novel RosCap predictive score focused to support risk assessment in patients with negative or equivocal mpMRI results.
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Hagens MJ, Stelwagen PJ, Veerman H, Rynja SP, Smeenge M, van der Noort V, Roeleveld TA, van Kesteren J, Remmers S, Roobol MJ, van Leeuwen PJ, van der Poel HG. External validation of the Rotterdam prostate cancer risk calculator within a high-risk Dutch clinical cohort. World J Urol 2023; 41:13-18. [PMID: 36245015 DOI: 10.1007/s00345-022-04185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/04/2022] [Indexed: 01/21/2023] Open
Abstract
PURPOSE This study aims to externally validate the Rotterdam Prostate Cancer Risk Calculator (RPCRC)-3/4 and RPCRC-MRI within a Dutch clinical cohort. METHODS Men subjected to prostate biopsies, between 2018 and 2021, due to a clinical suspicion of prostate cancer (PCa) were retrospectively included. The performance of the RPCRC-3/4 and RPCRC-MRI was analyzed in terms of discrimination, calibration and net benefit. In addition, the need for recalibration and adjustment of risk thresholds for referral was investigated. Clinically significant (cs) PCa was defined as Gleason score ≥ 3 + 4. RESULTS A total of 1575 men were included in the analysis. PCa was diagnosed in 63.2% (996/1575) of men and csPCa in 41.7% (656/1575) of men. Use of the RPCRC-3/4 could have prevented 37.3% (587/1575) of all MRIs within this cohort, thereby missing 18.3% (120/656) of csPCa diagnoses. After recalibration and adjustment of risk thresholds to 20% for PCa and 10% for csPCa, use of the recalibrated RPCRC-3/4 could have prevented 15.1% (238/1575) of all MRIs, resulting in 5.3% (35/656) of csPCa diagnoses being missed. The performance of the RPCRC-MRI was good; use of this risk calculator could have prevented 10.7% (169/1575) of all biopsies, resulting in 1.2% (8/656) of csPCa diagnoses being missed. CONCLUSION The RPCRC-3/4 underestimates the probability of having csPCa within this Dutch clinical cohort, resulting in significant numbers of csPCa diagnoses being missed. For optimal performance of a risk calculator in a specific cohort, evaluation of its performance within the population under study is essential.
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Affiliation(s)
- Marinus J Hagens
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. .,Prostate Cancer Network Netherlands, Amsterdam, The Netherlands. .,Department of Urology, Amsterdam University Medical Centers Location VUmc, Amsterdam, The Netherlands.
| | - Piter J Stelwagen
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Department of Urology, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
| | - Hans Veerman
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Prostate Cancer Network Netherlands, Amsterdam, The Netherlands.,Department of Urology, Amsterdam University Medical Centers Location VUmc, Amsterdam, The Netherlands
| | - Sybren P Rynja
- Prostate Cancer Network Netherlands, Amsterdam, The Netherlands.,Department of Urology, Spaarne Gasthuis, Hoofddorp, The Netherlands
| | - Martijn Smeenge
- Prostate Cancer Network Netherlands, Amsterdam, The Netherlands.,Department of Urology, Hospital St Jansdal, Harderwijk, The Netherlands
| | - Vincent van der Noort
- Department of Statistics, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Amsterdam, The Netherlands
| | - Ton A Roeleveld
- Prostate Cancer Network Netherlands, Amsterdam, The Netherlands.,Department of Urology, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
| | - Jolien van Kesteren
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Monique J Roobol
- Department of Urology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Pim J van Leeuwen
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Prostate Cancer Network Netherlands, Amsterdam, The Netherlands
| | - Henk G van der Poel
- Department of Urology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,Prostate Cancer Network Netherlands, Amsterdam, The Netherlands.,Department of Urology, Amsterdam University Medical Centers Location VUmc, Amsterdam, The Netherlands
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Improved Prediction of Significant Prostate Cancer Following Repeated Prostate Biopsy by the Random Forest Classifier. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00768-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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44
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Xiong T, Cao F, Zhu G, Ye X, Cui Y, Zhang H, Niu Y. MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population. Adipocyte 2022; 11:653-664. [PMID: 36415995 PMCID: PMC9704414 DOI: 10.1080/21623945.2022.2148885] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65-5.86] vs. 3.54 [2.49-4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05-35.95] vs. 27.90 [21.43-33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80-5.88] vs. 4.52 [3.80-5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs. 0.819, p < 0.001) and csPCa (0.827 vs. 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.
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Affiliation(s)
- Tianyu Xiong
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Fang Cao
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Guangyi Zhu
- Department of Urology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Xiaobo Ye
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Yun Cui
- Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Huibo Zhang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China,Huibo Zhang Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, China
| | - Yinong Niu
- Department of Urology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China,CONTACT Yinong Niu Department of Urology, Beijing Shijitan Hospital, Capital Medical University, 10 Tieyiyuan Road, Haidian District, Beijing, China
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Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review. Cancers (Basel) 2022; 14:cancers14194747. [PMID: 36230670 PMCID: PMC9562712 DOI: 10.3390/cancers14194747] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 11/18/2022] Open
Abstract
Simple Summary Magnetic resonance imaging (MRI) has allowed the early detection of PCa to evolve towards clinically significant PCa (csPCa), decreasing unnecessary prostate biopsies and overdetection of insignificant tumours. MRI identifies suspicious lesions of csPCa, predicting the semi-quantitative risk through the prostate imaging report and data system (PI-RADS), and enables guided biopsies, increasing the sensitivity of csPCa. Predictive models that individualise the risk of csPCa have also evolved adding PI-RADS score (MRI-PMs), improving the selection of candidates for prostate biopsy beyond the PI-RADS category. During the last five years, many MRI-PMs have been developed. Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness through a systematic review. We have found high heterogeneity between MRI technique, PI-RADS versions, biopsy schemes and approaches, and csPCa definitions. MRI-PMs outperform the selection of candidates for prostate biopsy beyond MRI alone and PMs based on clinical predictors. However, few developed MRI-PMs are externally validated or have available risk calculators (RCs), which constitute the appropriate requirements used in routine clinical practice. Abstract MRI can identify suspicious lesions, providing the semi-quantitative risk of csPCa through the Prostate Imaging-Report and Data System (PI-RADS). Predictive models of clinical variables that individualise the risk of csPCa have been developed by adding PI-RADS score (MRI-PMs). Our objective is to analyse the current developed MRI-PMs and define their clinical usefulness. A systematic review was performed after a literature search performed by two independent investigators in PubMed, Cochrane, and Web of Science databases, with the Medical Subjects Headings (MESH): predictive model, nomogram, risk model, magnetic resonance imaging, PI-RADS, prostate cancer, and prostate biopsy. This review was made following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria and studied eligibility based on the Participants, Intervention, Comparator, and Outcomes (PICO) strategy. Among 723 initial identified registers, 18 studies were finally selected. Warp analysis of selected studies was performed with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Clinical predictors in addition to the PI-RADS score in developed MRI-PMs were age, PCa family history, digital rectal examination, biopsy status (initial vs. repeat), ethnicity, serum PSA, prostate volume measured by MRI, or calculated PSA density. All MRI-PMs improved the prediction of csPCa made by clinical predictors or imaging alone and achieved most areas under the curve between 0.78 and 0.92. Among 18 developed MRI-PMs, 7 had any external validation, and two RCs were available. The updated PI-RADS version 2 was exclusively used in 11 MRI-PMs. The performance of MRI-PMs according to PI-RADS was only analysed in a single study. We conclude that MRI-PMs improve the selection of candidates for prostate biopsy beyond the PI-RADS category. However, few developed MRI-PMs meet the appropriate requirements in routine clinical practice.
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The Diagnostic Value of PI-RADS v2.1 in Patients with a History of Transurethral Resection of the Prostate (TURP). Curr Oncol 2022; 29:6373-6382. [PMID: 36135071 PMCID: PMC9497547 DOI: 10.3390/curroncol29090502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/28/2022] [Accepted: 09/01/2022] [Indexed: 11/24/2022] Open
Abstract
To explore the diagnostic value of the Prostate Imaging−Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608−0.812), AUC 0.768 (95%CI 0.676−0.860), AUC 0.777 (95%CI 0.688−0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665−0.854; p < 0.001)) while having limited capability in distinguishing transitional zone lesions (AUC 0.533 (95%CI 0.410−0.557; p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions.
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Davik P, Remmers S, Elschot M, Roobol MJ, Bathen TF, Bertilsson H. Reducing prostate biopsies and magnetic resonance imaging with prostate cancer risk stratification. BJUI COMPASS 2022; 3:344-353. [PMID: 35950035 PMCID: PMC9349589 DOI: 10.1002/bco2.146] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/26/2022] [Accepted: 03/14/2022] [Indexed: 11/08/2022] Open
Abstract
Objectives To recalibrate and validate the European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC RCs) 3/4 and the magnetic resonance imaging (MRI)-ERSPC-RCs to a contemporary Norwegian setting to reduce upfront prostate multiparametric MRI (mpMRI) and prostate biopsies. Patients and Methods We retrospectively identified and entered all men who underwent prostate mpMRI and subsequent prostate biopsy between January 2016 and March 2017 in a Norwegian centre into a database. mpMRI was reported using PI-RADS v2.0 and clinically significant prostate cancer (csPCa) defined as Gleason ≥ 3 + 4. Probabilities of csPCa and any prostate cancer (PCa) on biopsy were calculated by the ERSPC RCs 3/4 and the MRI-ERSPC-RC and compared with biopsy results. RCs were then recalibrated to account for differences in prevalence between the development and current cohorts (if indicated), and calibration, discrimination and clinical usefulness assessed. Results Three hundred and three patients were included. The MRI-ERSPC-RCs were perfectly calibrated to our cohort, although the ERSPC RCs 3/4 needed recalibration. Area under the receiver operating curve (AUC) for the ERSPC RCs 3/4 was 0.82 for the discrimination of csPCa and 0.77 for any PCa. The AUC for the MRI-ERSPC-RCs was 0.89 for csPCa and 0.85 for any PCa. Decision curve analysis showed clear net benefit for both the ERSPC RCs 3/4 (>2% risk of csPCa threshold to biopsy) and for the MRI-ERSPC-RCs (>1% risk of csPCa threshold), with a greater net benefit for the MRI-RCs. Using a >10% risk of csPCa or 20% risk of any PCa threshold for the ERSPC RCs 3/4, 15.5% of mpMRIs could be omitted, missing 0.8% of csPCa. Using the MRI-ERSPC-RCs, 23.4% of biopsies could be omitted with the same threshold, missing 0.8% of csPCa. Conclusion The ERSPC RCs 3/4 and MRI-ERSPC-RCs can considerably reduce both upfront mpMRI and prostate biopsies with little risk of missing csPCa.
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Affiliation(s)
- Petter Davik
- Department of Clinical and Molecular MedicineNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- Department of UrologySt. Olav's HospitalTrondheimNorway
| | - Sebastiaan Remmers
- Department of Urology, Erasmus MC Cancer InstituteUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Mattijs Elschot
- Department of Circulation and Medical ImagingNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- Department of Radiology and Nuclear MedicineSt. Olav's HospitalTrondheimNorway
| | - Monique J. Roobol
- Department of Urology, Erasmus MC Cancer InstituteUniversity Medical Center RotterdamRotterdamThe Netherlands
| | - Tone Frost Bathen
- Department of Circulation and Medical ImagingNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- Department of Radiology and Nuclear MedicineSt. Olav's HospitalTrondheimNorway
| | - Helena Bertilsson
- Department of Clinical and Molecular MedicineNTNU ‐ Norwegian University of Science and TechnologyTrondheimNorway
- Department of UrologySt. Olav's HospitalTrondheimNorway
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de Rooij M, van Poppel H, Barentsz JO. Risk Stratification and Artificial Intelligence in Early Magnetic Resonance Imaging-based Detection of Prostate Cancer. Eur Urol Focus 2022; 8:1187-1191. [PMID: 34922897 DOI: 10.1016/j.euf.2021.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/18/2021] [Accepted: 11/26/2021] [Indexed: 12/16/2022]
Abstract
Magnetic resonance imaging (MRI) has transformed the diagnostic pathway for prostate cancer and now plays an upfront role before prostate biopsies. If a suspicious lesion is found on MRI, the subsequent biopsy can be targeted. A sharp increase is expected in the number of men who will undergo prostate MRI. The challenge is to provide good image quality and diagnostic accuracy while meeting the demands of the expected higher workload. A possible solution to this challenge is to include a suitable risk stratification tool before imaging. Other solutions, such as smarter and shorter MRI protocols, need to be explored. For most of these solutions, artificial intelligence (AI) can play an important role. AI applications have the potential to improve the diagnostic quality of the prostate MRI pathway and speed up the work. PATIENT SUMMARY: The use of prostate magnetic resonance imaging (MRI) for diagnosis of prostate cancer is increasing. Risk stratification of patients before imaging and the use of shorter scan protocols can help in managing MRI resources. Artificial intelligence can also play a role in automating some tasks.
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Affiliation(s)
- Maarten de Rooij
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Hendrik van Poppel
- Department of Development and Regeneration, University Hospital KU Leuven, Leuven, Belgium
| | - Jelle O Barentsz
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
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Israël B, Hannink G, Barentsz JO, van der Leest MM. Implications of the European Association of Urology Recommended Risk Assessment Algorithm for Early Prostate Cancer Detection. EUR UROL SUPPL 2022; 43:1-4. [PMID: 35845549 PMCID: PMC9278493 DOI: 10.1016/j.euros.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/25/2022] [Indexed: 10/31/2022] Open
Abstract
Patient summary
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Van Poppel H, Albreht T, Basu P, Hogenhout R, Collen S, Roobol M. Serum PSA-based early detection of prostate cancer in Europe and globally: past, present and future. Nat Rev Urol 2022; 19:562-572. [PMID: 35974245 DOI: 10.1038/s41585-022-00638-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2022] [Indexed: 12/14/2022]
Abstract
In the pre-PSA-detection era, a large proportion of men were diagnosed with metastatic prostate cancer and died of the disease; after the introduction of the serum PSA test, randomized controlled prostate cancer screening trials in the USA and Europe were conducted to assess the effect of PSA screening on prostate cancer mortality. Contradictory outcomes of the trials and the accompanying overdiagnosis resulted in recommendations against prostate cancer screening by organizations such as the United States Preventive Services Task Force. These recommendations were followed by a decline in PSA testing and a rise in late-stage diagnosis and prostate cancer mortality. Re-evaluation of the randomized trials, which accounted for contamination, showed that PSA-based screening does indeed reduce prostate cancer mortality; however, the debate about whether to screen or not to screen continues because of the considerable overdiagnosis that occurs using PSA-based screening. Meanwhile, awareness among the population of prostate cancer as a potentially lethal disease stimulates opportunistic screening practices that further increase overdiagnosis without the benefit of mortality reduction. However, in the past decade, new screening tools have been developed that make the classic PSA-only-based screening an outdated strategy. With improved use of PSA, in combination with age, prostate volume and with the application of prostate cancer risk calculators, a risk-adapted strategy enables improved stratification of men with prostate cancer and avoidance of unnecessary diagnostic procedures. This combination used with advanced detection techniques (such as MRI and targeted biopsy), can reduce overdiagnosis. Moreover, new biomarkers are becoming available and will enable further improvements in risk stratification.
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Affiliation(s)
| | - Tit Albreht
- National Institute of Public Health, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Partha Basu
- International Agency for Research on Cancer, Lyon, France
| | - Renée Hogenhout
- Erasmus University Medical Center, Cancer Institute, Rotterdam, Netherlands
| | - Sarah Collen
- European Association of Urology, Arnhem, Netherlands
| | - Monique Roobol
- Erasmus University Medical Center, Cancer Institute, Rotterdam, Netherlands
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