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Patel KR, van der Heide UA, Kerkmeijer LGW, Schoots IG, Turkbey B, Citrin DE, Hall WA. Target Volume Optimization for Localized Prostate Cancer. Pract Radiat Oncol 2024:S1879-8500(24)00148-6. [PMID: 39019208 DOI: 10.1016/j.prro.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/17/2024] [Accepted: 06/26/2024] [Indexed: 07/19/2024]
Abstract
Historically, the treatment of prostate cancer has required little anatomic information beyond the location of the prostate gland and adjacent seminal vesicles. Radiation therapy has classically been prescribed to the whole prostate due to the high frequency of multifocal cancer in surgical specimens and the inability to localize the precise boundaries of individual tumor foci on imaging. The development of prostate magnetic resonance imaging (MRI) and positron emission tomography (PET) using prostate-specific radiotracers has ushered in an era in which radiation oncologists are able to localize and focally dose-escalate high-risk volumes in the prostate gland. Recent phase III data have demonstrated that incorporating focal dose escalation improves biochemical control without significantly increasing toxicity. However, many questions remain regarding the optimal target volume definition and prescription strategy to implement this practice. In this review we summarize the currently available literature on image-based focal target delineation with MRI and PET. Our review includes a summary of the available data on anatomic patterns of spread to inform clinical judgement for the definition of clinical target volumes. Key knowledge gaps are identified and suggestions for novel implementation strategies are provided.
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Affiliation(s)
- Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, NIH, Bethesda, MD.
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute (NKI-AVL), Amsterdam, The Netherlands
| | - Linda G W Kerkmeijer
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ivo G Schoots
- Department of Radiation Oncology, The Netherlands Cancer Institute (NKI-AVL), Amsterdam, The Netherlands
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - William A Hall
- Froedtert and the Medical College of Wisconsin, Milwaukee, WI
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Penzkofer T. Prostate-MRI reporting should be done with the aid of AI systems: Pros. Eur Radiol 2024:10.1007/s00330-024-10909-y. [PMID: 38981893 DOI: 10.1007/s00330-024-10909-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 05/27/2024] [Accepted: 06/08/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Tobias Penzkofer
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health, Berlin, Germany.
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3
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de Rooij M. Prostate-MRI reporting should be done with the aid of AI systems: Cons. Eur Radiol 2024:10.1007/s00330-024-10898-y. [PMID: 38981891 DOI: 10.1007/s00330-024-10898-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Revised: 06/05/2024] [Accepted: 06/08/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Maarten de Rooij
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
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4
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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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Saha A, Bosma JS, Twilt JJ, van Ginneken B, Bjartell A, Padhani AR, Bonekamp D, Villeirs G, Salomon G, Giannarini G, Kalpathy-Cramer J, Barentsz J, Maier-Hein KH, Rusu M, Rouvière O, van den Bergh R, Panebianco V, Kasivisvanathan V, Obuchowski NA, Yakar D, Elschot M, Veltman J, Fütterer JJ, de Rooij M, Huisman H. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 2024; 25:879-887. [PMID: 38876123 DOI: 10.1016/s1470-2045(24)00220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/16/2024] [Accepted: 04/18/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.1) and the standard of care in multidisciplinary routine practice at scale. METHODS In this international, paired, non-inferiority, confirmatory study, we trained and externally validated an AI system (developed within an international consortium) for detecting Gleason grade group 2 or greater cancers using a retrospective cohort of 10 207 MRI examinations from 9129 patients. Of these examinations, 9207 cases from three centres (11 sites) based in the Netherlands were used for training and tuning, and 1000 cases from four centres (12 sites) based in the Netherlands and Norway were used for testing. In parallel, we facilitated a multireader, multicase observer study with 62 radiologists (45 centres in 20 countries; median 7 [IQR 5-10] years of experience in reading prostate MRI) using PI-RADS (2.1) on 400 paired MRI examinations from the testing cohort. Primary endpoints were the sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) of the AI system in comparison with that of all readers using PI-RADS (2.1) and in comparison with that of the historical radiology readings made during multidisciplinary routine practice (ie, the standard of care with the aid of patient history and peer consultation). Histopathology and at least 3 years (median 5 [IQR 4-6] years) of follow-up were used to establish the reference standard. The statistical analysis plan was prespecified with a primary hypothesis of non-inferiority (considering a margin of 0·05) and a secondary hypothesis of superiority towards the AI system, if non-inferiority was confirmed. This study was registered at ClinicalTrials.gov, NCT05489341. FINDINGS Of the 10 207 examinations included from Jan 1, 2012, through Dec 31, 2021, 2440 cases had histologically confirmed Gleason grade group 2 or greater prostate cancer. In the subset of 400 testing cases in which the AI system was compared with the radiologists participating in the reader study, the AI system showed a statistically superior and non-inferior AUROC of 0·91 (95% CI 0·87-0·94; p<0·0001), in comparison to the pool of 62 radiologists with an AUROC of 0·86 (0·83-0·89), with a lower boundary of the two-sided 95% Wald CI for the difference in AUROC of 0·02. At the mean PI-RADS 3 or greater operating point of all readers, the AI system detected 6·8% more cases with Gleason grade group 2 or greater cancers at the same specificity (57·7%, 95% CI 51·6-63·3), or 50·4% fewer false-positive results and 20·0% fewer cases with Gleason grade group 1 cancers at the same sensitivity (89·4%, 95% CI 85·3-92·9). In all 1000 testing cases where the AI system was compared with the radiology readings made during multidisciplinary practice, non-inferiority was not confirmed, as the AI system showed lower specificity (68·9% [95% CI 65·3-72·4] vs 69·0% [65·5-72·5]) at the same sensitivity (96·1%, 94·0-98·2) as the PI-RADS 3 or greater operating point. The lower boundary of the two-sided 95% Wald CI for the difference in specificity (-0·04) was greater than the non-inferiority margin (-0·05) and a p value below the significance threshold was reached (p<0·001). INTERPRETATION An AI system was superior to radiologists using PI-RADS (2.1), on average, at detecting clinically significant prostate cancer and comparable to the standard of care. Such a system shows the potential to be a supportive tool within a primary diagnostic setting, with several associated benefits for patients and radiologists. Prospective validation is needed to test clinical applicability of this system. FUNDING Health~Holland and EU Horizon 2020.
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Affiliation(s)
- Anindo Saha
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands; Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands.
| | - Joeran S Bosma
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jasper J Twilt
- Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Anders Bjartell
- Department of Urology, Skåne University Hospital, Malmö, Sweden; Division of Translational Cancer Research, Lund University Cancer Centre, Lund, Sweden
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, London, UK
| | - David Bonekamp
- Division of Radiology, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany
| | - Geert Villeirs
- Department of Diagnostic Sciences, Ghent University Hospital, Ghent, Belgium
| | - Georg Salomon
- Martini Clinic, Prostate Cancer Center, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Gianluca Giannarini
- Urology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence in Ophthalmology, University of Colorado, Aurora, CO, USA
| | - Jelle Barentsz
- Department of Medical Imaging, Andros Clinics, Arnhem, Netherlands
| | - Klaus H Maier-Hein
- Division of Medical Image Computing, Deutsches Krebsforschungszentrum Heidelberg, Heidelberg, Germany; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mirabela Rusu
- Departments of Radiology, Urology and Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Faculté de Médecine Lyon-Est, Université de Lyon, Lyon, France
| | | | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Veeru Kasivisvanathan
- Division of Surgery and Interventional Sciences, University College London and University College London Hospital, London, UK
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences and Department of Diagnostic Radiology, Cleveland Clinic Foundation, Cleveland OH, USA
| | - Derya Yakar
- Department of Radiology, University Medical Center Groningen, Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Mattijs Elschot
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Tronheim, Norway; Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jeroen Veltman
- Department of Radiology, Ziekenhuisgroep Twente, Hengelo, Netherlands; Department of Multi-Modality Medical Imaging, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Jurgen J Fütterer
- Minimally Invasive Image-Guided Intervention Center, Radboud University Medical Center, Nijmegen, Netherlands
| | - Maarten de Rooij
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Tronheim, Norway
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Rajagopal A, Westphalen AC, Velarde N, Simko JP, Nguyen H, Hope TA, Larson PEZ, Magudia K. Mixed Supervision of Histopathology Improves Prostate Cancer Classification From MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2610-2622. [PMID: 38547000 DOI: 10.1109/tmi.2024.3382909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Non-invasive prostate cancer classification from MRI has the potential to revolutionize patient care by providing early detection of clinically significant disease, but has thus far shown limited positive predictive value. To address this, we present a image-based deep learning method to predict clinically significant prostate cancer from screening MRI in patients that subsequently underwent biopsy with results ranging from benign pathology to the highest grade tumors. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. Where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=198 ) multi-parametric prostate MRI exams collected at UCSF from 2016-2019 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can feasibly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation (71.6% vs 66.7% balanced accuracy and 0.724 vs 0.716 AUC).
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Stanzione A, Lee KL, Sanmugalingam N, Rajendran I, Sushentsev N, Caglič I, Barrett T. Expect the unexpected: investigating discordant prostate MRI and biopsy results. Eur Radiol 2024; 34:4810-4820. [PMID: 38503918 PMCID: PMC11213781 DOI: 10.1007/s00330-024-10702-x] [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: 12/15/2023] [Revised: 02/08/2024] [Accepted: 02/24/2024] [Indexed: 03/21/2024]
Abstract
OBJECTIVES To evaluate discrepant radio-pathological outcomes in biopsy-naïve patients undergoing prostate MRI and to provide insights into the underlying causes. MATERIALS AND METHODS A retrospective analysis was conducted on 2780 biopsy-naïve patients undergoing prostate MRI at a tertiary referral centre between October 2015 and June 2022. Exclusion criteria were biopsy not performed, indeterminate MRI findings (PI-RADS 3), and clinically insignificant PCa (Gleason score 3 + 3). Patients with discrepant findings between MRI and biopsy results were categorised into two groups: MRI-negative/Biopsy-positive and MRI-positive/Biopsy-negative (biopsy-positive defined as Gleason score ≥ 3 + 4). An expert uroradiologist reviewed discrepant cases, retrospectively re-assigning PI-RADS scores, identifying any missed MRI targets, and evaluating the quality of MRI scans. Potential explanations for discrepancies included MRI overcalls (including known pitfalls), benign pathology findings, and biopsy targeting errors. RESULTS Patients who did not undergo biopsy (n = 1258) or who had indeterminate MRI findings (n = 204), as well as those with clinically insignificant PCa (n = 216), were excluded, with a total of 1102 patients analysed. Of these, 32/1,102 (3%) were classified as MRI-negative/biopsy-positive and 117/1102 (11%) as MRI-positive/biopsy-negative. In the MRI-negative/Biopsy-positive group, 44% of studies were considered non-diagnostic quality. Upon retrospective image review, target lesions were identified in 28% of cases. In the MRI-positive/Biopsy-negative group, 42% of cases were considered to be MRI overcalls, and 32% had an explanatory benign pathological finding, with biopsy targeting errors accounting for 11% of cases. CONCLUSION Prostate MRI demonstrated a high diagnostic accuracy, with low occurrences of discrepant findings as defined. Common reasons for MRI-positive/Biopsy-negative cases included explanatory benign findings and MRI overcalls. CLINICAL RELEVANCE STATEMENT This study highlights the importance of optimal prostate MRI image quality and expertise in reducing diagnostic errors, improving patient outcomes, and guiding appropriate management decisions in the prostate cancer diagnostic pathway. KEY POINTS • Discrepancies between prostate MRI and biopsy results can occur, with higher numbers of MRI-positive/biopsy-negative relative to MRI-negative/biopsy-positive cases. • MRI-positive/biopsy-negative cases were mostly overcalls or explainable by benign biopsy findings. • In about one-third of MRI-negative/biopsy-positive cases, a target lesion was retrospectively identified.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131, Naples, Italy
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
| | - Kang-Lung Lee
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Nimalan Sanmugalingam
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
| | - Ishwariya Rajendran
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
| | - Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
| | - Iztok Caglič
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Hills Road, Box 218, Cambridge, CB2 0QQ, UK.
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Taya M, Behr SC, Westphalen AC. Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability. BJU Int 2024. [PMID: 38923789 DOI: 10.1111/bju.16452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
OBJECTIVES To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions. METHODS A narrative review of PI-RADS interobserver variability. RESULTS PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further. CONCLUSION Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.
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Affiliation(s)
- Michio Taya
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Spencer C Behr
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Antonio C Westphalen
- Departments of Radiology, Urology, and Radiation Oncology, University of Washington, Seattle, WA, USA
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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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10
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Cooperberg MR. PRECISE v2: An Enhanced Framework To Guide Future Research on the Use of Magnetic Resonance Imaging in Prostate Cancer Active Surveillance. Eur Urol 2024:S0302-2838(24)02400-X. [PMID: 38897869 DOI: 10.1016/j.eururo.2024.05.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024]
Affiliation(s)
- Matthew R Cooperberg
- Departments of Urology and Epidemiology & Biostatistics, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA.
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11
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Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01163-w. [PMID: 38856839 DOI: 10.1007/s10334-024-01163-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
Prostate cancer poses significant diagnostic challenges, with conventional methods like prostate-specific antigen (PSA) screening and transrectal ultrasound (TRUS)-guided biopsies often leading to overdiagnosis or miss clinically significant cancers. Multiparametric MRI (mpMRI) has emerged as a more reliable tool. However, it is limited by high inter-observer variability and radiologists missing up to 30% of clinically significant cancers. This article summarizes a few of these recent advancements in quantitative MRI techniques that look at the "Virtual Pathology" of the prostate with an aim to enhance prostate cancer detection and characterization. These techniques include T2 relaxation-based techniques such as luminal water imaging, diffusion based such as vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) and restriction spectrum imaging or combined relaxation-diffusion techniques such as hybrid multi-dimensional MRI (HM-MRI), time-dependent diffusion imaging, and diffusion-relaxation correlation spectrum imaging. These methods provide detailed insights into underlying prostate microstructure and tissue composition and have shown improved diagnostic accuracy over conventional MRI. These innovative MRI methods hold potential for augmenting mpMRI, reducing variability in diagnosis, and paving the way for MRI as a 'virtual histology' tool in prostate cancer diagnosis. However, they require further validation in larger multi-center clinical settings and rigorous in-depth radiological-pathology correlation are needed for broader implementation.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
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12
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Soleimani S. Editorial for "Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates". J Magn Reson Imaging 2024; 59:2224-2225. [PMID: 37787598 DOI: 10.1002/jmri.29033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 10/04/2023] Open
Affiliation(s)
- Sahar Soleimani
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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13
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Lin Y, Belue MJ, Yilmaz EC, Harmon SA, An J, Law YM, Hazen L, Garcia C, Merriman KM, Phelps TE, Lay NS, Toubaji A, Merino MJ, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. Deep Learning-Based T2-Weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates. J Magn Reson Imaging 2024; 59:2215-2223. [PMID: 37811666 PMCID: PMC11001787 DOI: 10.1002/jmri.29031] [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: 07/09/2023] [Revised: 09/15/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis. PURPOSE To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm. STUDY TYPE Retrospective. SUBJECTS 615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy. FIELD STRENGTH/SEQUENCE 3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced. ASSESSMENTS Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category. STATISTICAL TESTS Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant. RESULTS 385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09). DATA CONCLUSION Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Julie An
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Lindsey Hazen
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Charisse Garcia
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradford J Wood
- Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
- Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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14
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Guo S, Zhang J, Wang Y, Jiao J, Li Z, Cui C, Chen J, Yang W, Ma S, Wu P, Jing Y, Wen W, Kang F, Wang J, Qin W. Avoiding unnecessary biopsy: the combination of PRIMARY score with prostate-specific antigen density for prostate biopsy decision. Prostate Cancer Prostatic Dis 2024; 27:288-293. [PMID: 38160227 DOI: 10.1038/s41391-023-00782-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Avoiding unnecessary biopsies for men with suspected prostate cancer remains a clinical priority. The recently proposed PRIMARY score improves diagnostic accuracy in detecting clinically significant prostate cancer (csPCa). The aim of this study was to determine the best strategy combining PRIMARY score or MRI reporting scores (Prostate Imaging Reporting and Data System [PI-RADS]) with prostate-specific antigen density (PSAD) for prostate biopsy decision making. METHODS A retrospective analysis of 343 patients who underwent both 68Ga-PSMA PET/CT and MRI before prostate biopsy was performed. PSA was restricted to <20 ng/ml. Different biopsy strategies were developed and compared based on PRIMARY score or PI-RADS with PSAD thresholds. Decision curve analysis (DCA) was plotted to define the optimal biopsy strategy. RESULTS The prevalence of csPCa was 41.1% (141/343). According to DCA, the strategies of PRIMARY score +PSAD (strategy #1, strategy #2, strategy #6) had a higher net benefit than the strategies of PI-RADS + PSAD at the risk threshold of 8-20%. The best diagnostic strategy was strategy #1 (PRIMARY score 4-5 or PSAD ≥ 0.20), which avoided 38.2% biopsy procedures while missed 9.2% of csPCa cases. From a clinical perspective, strategies with a lower risk of missing csPCa were strategy #2 (PRIMARY score ≥4 or PSAD ≥ 0.15), which avoided 28.6% biopsies while missed 5.7% of csPCa cases, or strategy #6 (PRIMARY score≥3 or PSAD ≥ 0.15), which avoided 20.7% biopsies while missed only 3.5% of csPCa cases. The limitations of the study were the retrospective single-center nature. CONCLUSIONS The combination of PRIMARY score +PSAD allows individualized decisions to avoid unnecessary biopsy, outperforming the strategies of PI-RADS + PSAD. Further prospective trials are needed to validate these findings.
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Affiliation(s)
- Shikuan Guo
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
- Department of Urology, No.988th Hospital of Joint Logistic Support Force of PLA, Zhengzhou, 450042, Henan, China
| | - Jingliang Zhang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Yingmei Wang
- Department of Pathology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Jianhua Jiao
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Zeyu Li
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Chaochao Cui
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Jian Chen
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Wenhui Yang
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Shuaijun Ma
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Peng Wu
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Yuming Jing
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China
| | - Weihong Wen
- Institute of Medical Research, Northwestern Polytechnical University, 710032, Xi'an, China
| | - Fei Kang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, Shaanxi, China
| | - Jing Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, Shaanxi, China
| | - Weijun Qin
- Department of Urology, Xijing Hospital, Fourth Military Medical University, 710032, Xi'an, China.
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15
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Schieda N, Nisha Y, Hadziomerovic AR, Prabhakar S, Flood TA, Breau RH, McGrath TA, Ramsay T, Morash C. Comparison of Positive Predictive Values of Biparametric MRI and Multiparametric MRI-directed Transrectal US-guided Targeted Prostate Biopsy. Radiology 2024; 311:e231383. [PMID: 38860899 DOI: 10.1148/radiol.231383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
Background Biparametric MRI (bpMRI) of the prostate is an alternative to multiparametric MRI (mpMRI), with lower cost and increased accessibility. Studies investigating the positive predictive value (PPV) of bpMRI-directed compared with mpMRI-directed targeted biopsy are lacking in the literature. Purpose To compare the PPVs of bpMRI-directed and mpMRI-directed targeted prostate biopsies. Materials and Methods This retrospective cross-sectional study evaluated men who underwent bpMRI-directed or mpMRI-directed transrectal US (TRUS)-guided targeted prostate biopsy at a single institution from January 2015 to December 2022. The PPVs for any prostate cancer (PCa) and clinically significant PCa (International Society of Urological Pathology grade ≥2) were calculated for bpMRI and mpMRI using mixed-effects logistic regression modeling. Results A total of 1538 patients (mean age, 67 years ± 8 [SD]) with 1860 lesions underwent bpMRI-directed (55%, 849 of 1538) or mpMRI-directed (45%, 689 of 1538) prostate biopsy. When adjusted for the number of lesions and Prostate Imaging Reporting and Data System (PI-RADS) score, there was no difference in PPVs for any PCa or clinically significant PCa (P = .61 and .97, respectively) with bpMRI-directed (55% [95% CI: 51, 59] and 34% [95% CI: 30, 38], respectively) or mpMRI-directed (56% [95% CI: 52, 61] and 34% [95% CI: 30, 39], respectively) TRUS-guided targeted biopsy. PPVs for any PCa and clinically significant PCa stratified according to clinical indication were as follows: biopsy-naive men, 64% (95% CI: 59, 69) and 43% (95% CI: 39, 48) for bpMRI, 67% (95% CI: 59, 75) and 51% (95% CI: 43, 59) for mpMRI (P = .65 and .26, respectively); and active surveillance, 59% (95% CI: 49, 69) and 30% (95% CI: 22, 39) for bpMRI, 73% (95% CI: 65, 89) and 38% (95% CI: 31, 47) for mpMRI (P = .04 and .23, respectively). Conclusion There was no evidence of a difference in PPV for clinically significant PCa between bpMRI- and mpMRI-directed TRUS-guided targeted biopsy. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Nicola Schieda
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Yashmin Nisha
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Alexa R Hadziomerovic
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Suman Prabhakar
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Trevor A Flood
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Rodney H Breau
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Trevor A McGrath
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Tim Ramsay
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
| | - Christopher Morash
- From the Department of Radiology (N.S., Y.N., A.R.H., S.P., T.A.M.), Department of Surgery, Division of Urology (N.S., R.H.B., C.M.), and Department of Anatomical Pathology (T.A.F.), The University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Rm C1, Ottawa, ON, Canada K1Y 4E9; and the Department of Epidemiology, The Ottawa Hospital Research Institute, Ottawa, Canada (T.R.)
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16
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Chen CH, Huang HP, Chang KH, Lee MS, Lee CF, Lin CY, Lin YC, Huang WJ, Liao CH, Yu CC, Chung SD, Tsai YC, Wu CC, Ho CH, Hsiao PW, Pu YS. Predicting Clinically Significant Prostate Cancer Using Urine Metabolomics via Liquid Chromatography Mass Spectrometry. World J Mens Health 2024; 42:42.e59. [PMID: 38863374 DOI: 10.5534/wjmh.230344] [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: 11/30/2023] [Revised: 02/18/2024] [Accepted: 03/03/2024] [Indexed: 06/13/2024] Open
Abstract
PURPOSE Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. MATERIALS AND METHODS Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. RESULTS The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88-0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. CONCLUSIONS Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.
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Grants
- MOST 107-2314-B-002-032-MY3 Ministry of Science and Technology, Executive Yuan, Taiwan
- MOST 107-2321-B-002-065 Ministry of Science and Technology, Executive Yuan, Taiwan
- MOST 108-2321-B-002-029 Ministry of Science and Technology, Executive Yuan, Taiwan
- MOST 109-2327-B-002-001 Ministry of Science and Technology, Executive Yuan, Taiwan
- MOHW111-TDUB-221-114002 Ministry of Health and Welfare, Executive Yuan, Taiwan
- MOHW112-TDU-B-222-124002 Ministry of Health and Welfare, Executive Yuan, Taiwan
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Affiliation(s)
- Chung-Hsin Chen
- Department of Urology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Hsiang-Po Huang
- Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Kai-Hsiung Chang
- Institute of Cellular and System Medicine, National Health Research Institutes, Miaoli, Taiwan
| | - Ming-Shyue Lee
- Department of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Cheng-Fan Lee
- Department of Biochemistry and Molecular Biology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Chih-Yu Lin
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan
| | - Yuan Chi Lin
- Department of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - William J Huang
- Department of Urology, Taipei Veterans General Hospital, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Hou Liao
- Division of Urology, Department of Surgery, Cardinal Tien Hospital, New Taipei City, Taiwan
- School of Medicine, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Chih-Chin Yu
- Division of Urology, Department of Surgery, Taipei Tzu Chi Hospital and The Buddhist Tzu Chi Medical Foundation, College of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Shiu-Dong Chung
- Division of Urology, Department of Surgery, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Department of Nursing, College of Healthcare & Management, Asia Eastern University of Science and Technology, New Taipei City, Taiwan
| | - Yao-Chou Tsai
- Division of Urology, Department of Medicine, Taipei Tzu Chi Hospital, New Taipei City, Taiwan
| | - Chia-Chang Wu
- Department of Urology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Urology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
| | - Chen-Hsun Ho
- School of Medicine, College of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Division of Urology, Department of Surgery, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
| | - Pei-Wen Hsiao
- Agricultural Biotechnology Research Center, Academia Sinica, Taipei, Taiwan.
| | - Yeong-Shiau Pu
- Department of Urology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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17
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Miszewski K, Skrobisz K, Miszewska L, Matuszewski M. Interpreting Prostate MRI Reports in the Era of Increasing Prostate MRI Utilization: A Urologist's Perspective. Diagnostics (Basel) 2024; 14:1060. [PMID: 38786358 PMCID: PMC11120165 DOI: 10.3390/diagnostics14101060] [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: 04/17/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
Multi-parametric prostate MRI (mpMRI) is crucial for diagnosing, staging, and assessing treatment response in individuals with prostate cancer. Radiologists, through an accurate and standardized interpretation of mpMRI, stratify patients who may benefit from more invasive treatment or exclude patients who may be harmed by overtreatment. The integration of prostate MRI into the diagnostic pathway is anticipated to generate a substantial surge in the demand for high-quality mpMRI, estimated at approximately two million additional prostate MRI scans annually in Europe. In this review we examine the immediate impact on healthcare, particularly focusing on the workload and evolving roles of radiologists and urologists tasked with the interpretation of these reports and consequential decisions regarding prostate biopsies. We investigate important questions that influence how prostate MRI reports are handled. The discussion aims to provide insights into the collaboration needed for effective reporting.
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Affiliation(s)
- Kevin Miszewski
- Department of Urology, Gdańsk Medical University, Mariana Smoluchowskiego 17 Street, 80-214 Gdańsk, Poland
| | - Katarzyna Skrobisz
- Department of Radiology, Gdańsk Medical University, Mariana Smoluchowskiego 17 Street, 80-214 Gdańsk, Poland
| | - Laura Miszewska
- Student Scientific Association, Gdańsk Medical University, Mariana Smoluchowskiego 17 Street, 80-214 Gdańsk, Poland
| | - Marcin Matuszewski
- Department of Urology, Gdańsk Medical University, Mariana Smoluchowskiego 17 Street, 80-214 Gdańsk, Poland
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18
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Talyshinskii A, Hameed BMZ, Ravinder PP, Naik N, Randhawa P, Shah M, Rai BP, Tokas T, Somani BK. Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management. Cancers (Basel) 2024; 16:1809. [PMID: 38791888 PMCID: PMC11119252 DOI: 10.3390/cancers16101809] [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: 03/11/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.
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Affiliation(s)
- Ali Talyshinskii
- Department of Urology and Andrology, Astana Medical University, Astana 010000, Kazakhstan;
| | | | - Prajwal P. Ravinder
- Department of Urology, Kasturba Medical College, Mangaluru, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Princy Randhawa
- Department of Mechatronics, Manipal University Jaipur, Jaipur 303007, India;
| | - Milap Shah
- Department of Urology, Aarogyam Hospital, Ahmedabad 380014, India;
| | - Bhavan Prasad Rai
- Department of Urology, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK;
| | - Theodoros Tokas
- Department of Urology, Medical School, University General Hospital of Heraklion, University of Crete, 14122 Heraklion, Greece;
| | - Bhaskar K. Somani
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
- Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK
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van Velthoven R, Diamand R, Mozer P, Barry de Longchamp N. Letter to the Editor on "Comparison in Detection Rate of Clinically Significant Prostate Cancer Between Microultrasound-guided Prostate Biopsy (ExactVu) and Multiparametric Resonance Imaging-guided Prostate Biopsy (Koelis System)". Urology 2024:S0090-4295(24)00346-7. [PMID: 38729267 DOI: 10.1016/j.urology.2024.04.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 05/12/2024]
Affiliation(s)
- Roland van Velthoven
- Urology Department, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, Belgium.
| | - Romain Diamand
- Department of Urology, Jules Bordet Institute, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Pierre Mozer
- Service d'Urologie, Groupe Hospitalier Pitié-Salpétrière, Paris, France
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20
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Jin L, Yu Z, Gao F, Li M. T2-weighted imaging-based deep-learning method for noninvasive prostate cancer detection and Gleason grade prediction: a multicenter study. Insights Imaging 2024; 15:111. [PMID: 38713377 PMCID: PMC11076444 DOI: 10.1186/s13244-024-01682-z] [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: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 05/08/2024] Open
Abstract
OBJECTIVES To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach. METHODS Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance. RESULTS For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively. CONCLUSION Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts. CRITICAL RELEVANCE STATEMENT Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making. KEY POINTS Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.
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Affiliation(s)
- Liang Jin
- Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai, 200040, China
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Zhuo Yu
- School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
| | - Feng Gao
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Shanghai, 200040, China.
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21
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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:S1546-1440(24)00423-X. [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] [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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22
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Park SY, Woo S, Park KJ, Westphalen AC. A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice. Abdom Radiol (NY) 2024:10.1007/s00261-024-04273-0. [PMID: 38704782 DOI: 10.1007/s00261-024-04273-0] [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: 11/09/2023] [Revised: 03/02/2024] [Accepted: 03/03/2024] [Indexed: 05/07/2024]
Abstract
Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.
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Affiliation(s)
- Sung Yoon Park
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA.
| | - Sungmin Woo
- Department of Radiology, NYU Langone Health, New York, NY, 10016, USA
| | - Kye Jin Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Antonio C Westphalen
- Department of Radiology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
- Department of Urology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
- Department of Radiation Oncology, University of Washington, 1959 NE Pacific St., 2nd Floor, Seattle, WA, 98195, USA
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23
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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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24
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Seibert TM. Prostate MRI Was Negative-What's Next? Cancer Epidemiol Biomarkers Prev 2024; 33:641-642. [PMID: 38689575 DOI: 10.1158/1055-9965.epi-24-0214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 02/29/2024] [Accepted: 03/06/2024] [Indexed: 05/02/2024] Open
Abstract
The primary benefit of prostate MRI in the modern diagnostic pathway for prostate cancer is that many men with elevated serum PSA can safely avoid an immediate biopsy if the MRI is nonsuspicious. It is less clear, though, how these patients should be followed thereafter. Are they to be followed the same as the general population, or do they warrant more attention because of the risk of a cancer missed on MRI? In this issue, Pylväläinen and colleagues report on incidence of clinically significant prostate cancer (csPCa) and clinically insignificant PCa (ciPCa) among patients who were referred for prostate MRI for clinical suspicion of csPCa in Helsinki but had a nonsuspicious MRI (nMRI). Compared with the general population in Finland, patients who had nMRI were approximately 3.4 times more likely to be diagnosed with csPCa and 8.2 times more likely to be diagnosed with ciPCa. Balancing the competing risks of a missed csPCa versus overdiagnosis in patients after nMRI requires integration of MRI and other risk factors, especially age and PSA density. This integration may be facilitated by multivariable models and quantitative pathology and imaging. See related article by Pylväläinen et al., p. 749.
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Affiliation(s)
- Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California
- Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California
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25
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Nativ O, Shefler A, Bejar J, Peschansky S, Lavi A, Michael C, Nativ O. Performance of standard systematic biopsy versus MRI/TRUS fusion biopsy using the Navigo® system in contemporary cohort. Urol Oncol 2024; 42:159.e1-159.e7. [PMID: 38431487 DOI: 10.1016/j.urolonc.2024.01.026] [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: 11/19/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 03/05/2024]
Abstract
INTRODUCTION The introduction of multi parameter magnetic resonance imaging (mpMRI) of the prostate in combination with MRI/TRUS fusion and systematic biopsy resulted in improved detection of prostate cancer. The aim of the current study was to document the performance of MRI/TRUS fusion biopsy of the prostate using the Navigo™ software in a contemporary cohort of patients from nonreferral centers. MATERIAL AND METHODS We performed a two centers prospective data collection (2014-2020) for men with clinically suspected Pca and patients on active surveillance for low-risk Pca that were referred for TRUS biopsy after performing mpMRI of the prostate with a visible lesion. The primary outcome was detection of clinically significant cancer (csPca) defined as ISUP grade group ≥2. Patients were stratified according to biopsy technique and PI-RADS category. RESULTS The study group included 236 patients of whom 129 (54.9%) were diagnosed with Pca and 82 (34.7%) with csPca (GG ≥ 2) on combined biopsy. The overall detection of csPca was 31% for targeted vs. 25.4% for systematic biopsy with an absolute difference of 5.6% in favor of the fusion technique. No significant difference between the two techniques was observed for detection of benign prostate or GG1 disease. The improved performance of the targeted approach was noted only in patients with PI-RADS 4 and 5 lesions. Of the patients with csPca 10 (12%) were diagnosed only by the systematic biopsy while 20 (24%) were detected only in the fusion biopsy. Systematic biopsy of prostate lobe without MRI lesion detected only 2 cases (∼1%) with high grade disease. CONCLUSIONS Detection of csPca by mpMRI/TRUS fusion biopsy using the 3D Navigo™ system is feasible. The targeted approach outperforms the systematic one, however the later technique also detects high risk disease and should be included in the biopsy procedure. The overall detection rate (34.9%) of clinically significant prostate cancer by both targeted and systematic sampling is relatively low.
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Affiliation(s)
- Omri Nativ
- Department of Urology, Rambam Medical, Haifa, Israel.
| | | | - Jacob Bejar
- Department of Pathology, Bnai Zion Medical Center, Haifa, Israel
| | | | - Arnon Lavi
- Department of Urology, Hemek Medical Center, Afula, Israel
| | - Cohen Michael
- Department of Urology, Hemek Medical Center, Afula, Israel
| | - Ofer Nativ
- Department of Surgery, Elisha Medical Center, Haifa, Israel
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26
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Privé BM, Israël B, Janssen MJR, van der Leest MMG, de Rooij M, van Ipenburg JA, Jonker M, Peters SMB, de Groot M, Zámecnik P, Hoepping A, Bomers JG, Gotthardt M, Sedelaar JPM, Barentsz JO, van Oort IM, Nagarajah J. Multiparametric MRI and 18F-PSMA-1007 PET/CT for the Detection of Clinically Significant Prostate Cancer. Radiology 2024; 311:e231879. [PMID: 38771185 DOI: 10.1148/radiol.231879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Multiparametric MRI (mpMRI) is effective for detecting prostate cancer (PCa); however, there is a high rate of equivocal Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions and false-positive findings. Purpose To investigate whether fluorine 18 (18F) prostate-specific membrane antigen (PSMA) 1007 PET/CT after mpMRI can help detect localized clinically significant PCa (csPCa), particularly for equivocal PI-RADS 3 lesions. Materials and Methods This prospective study included participants with elevated prostate-specific antigen (PSA) levels referred for prostate mpMRI between September 2020 and February 2022. 18F-PSMA-1007 PET/CT was performed within 30 days of mpMRI and before biopsy. PI-RADS category and level of suspicion (LOS) were assessed. PI-RADS 3 or higher lesions at mpMRI and/or LOS 3 or higher lesions at 18F-PSMA-1007 PET/CT underwent targeted biopsies. PI-RADS 2 or lower and LOS 2 or lower lesions were considered nonsuspicious and were monitored during a 1-year follow-up by means of PSA testing. Diagnostic accuracy was assessed, with histologic examination serving as the reference standard. International Society of Urological Pathology (ISUP) grade 2 or higher was considered csPCa. Results Seventy-five participants (median age, 67 years [range, 52-77 years]) were assessed, with PI-RADS 1 or 2, PI-RADS 3, and PI-RADS 4 or 5 groups each including 25 participants. A total of 102 lesions were identified, of which 80 were PI-RADS 3 or higher and/or LOS 3 or higher and therefore underwent targeted biopsy. The per-participant sensitivity for the detection of csPCa was 95% and 91% for mpMRI and 18F-PSMA-1007 PET/CT, respectively, with respective specificities of 45% and 62%. 18F-PSMA-1007 PET/CT was used to correctly differentiate 17 of 26 PI-RADS 3 lesions (65%), with a negative and positive predictive value of 93% and 27%, respectively, for ruling out or detecting csPCa. One additional significant and one insignificant PCa lesion (PI-RADS 1 or 2) were found at 18F-PSMA-1007 PET/CT that otherwise would have remained undetected. Two participants had ISUP 2 tumors without PSMA uptake that were missed at PET/CT. Conclusion 18F-PSMA-1007 PET/CT showed good sensitivity and moderate specificity for the detection of csPCa and ruled this out in 93% of participants with PI-RADS 3 lesions. Clinical trial registration no. NCT04487847 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.
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Affiliation(s)
- Bastiaan M Privé
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Bas Israël
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Marcel J R Janssen
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Marloes M G van der Leest
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Maarten de Rooij
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Jolique A van Ipenburg
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Marianne Jonker
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Steffie M B Peters
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Michel de Groot
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Patrik Zámecnik
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Alexander Hoepping
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Joyce G Bomers
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Martin Gotthardt
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - J P Michiel Sedelaar
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Jelle O Barentsz
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - Inge M van Oort
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
| | - James Nagarajah
- From the Department of Medical Imaging and Nuclear Medicine (B.M.P., B.I., M.J.R.J., M.M.G.v.d.L., M.d.R., S.M.B.P., M.d.G., P.Z., J.G.B., M.G., J.O.B., J.N.), Department of Urology (B.I., J.P.M.S., I.M.v.O.), and Department of Radiation Oncology (B.I.), Radboud University Medical Center, Radboud Institute for Health Sciences, PO Box 9101, 6500 HB Nijmegen, the Netherlands; Department of Radiation Oncology, Erasmus Medical Center, Cancer Institute, Rotterdam, the Netherlands (B.M.P.); Department of Pathology (J.A.v.I.) and Department of Health Evidence, Biostatistics Section (M.J.), Radboud University Medical Center, Nijmegen, the Netherlands; and Department of Medicinal Chemistry, ABX Advanced Biochemical Compounds, Radeberg, Germany (A.H.)
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Lin Y, Yilmaz EC, Belue MJ, Harmon SA, Tetreault J, Phelps TE, Merriman KM, Hazen L, Garcia C, Yang D, Xu Z, Lay NS, Toubaji A, Merino MJ, Xu D, Law YM, Gurram S, Wood BJ, Choyke PL, Pinto PA, Turkbey B, Atzen S. Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI. Radiology 2024; 311:e230750. [PMID: 38713024 PMCID: PMC11140533 DOI: 10.1148/radiol.230750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 01/24/2024] [Accepted: 03/18/2024] [Indexed: 05/08/2024]
Abstract
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
- Yue Lin
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Enis C. Yilmaz
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Mason J. Belue
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Stephanie A. Harmon
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Jesse Tetreault
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Tim E. Phelps
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Katie M. Merriman
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Lindsey Hazen
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Charisse Garcia
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Dong Yang
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Ziyue Xu
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Nathan S. Lay
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Antoun Toubaji
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Maria J. Merino
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Daguang Xu
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Yan Mee Law
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Sandeep Gurram
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Bradford J. Wood
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Peter L. Choyke
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Peter A. Pinto
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Baris Turkbey
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
| | - Sarah Atzen
- From the Molecular Imaging Branch (Y.L., E.C.Y., M.J.B., S.A.H.,
T.E.P., K.M.M., N.S.L., P.L.C., B.T.), Center for Interventional Oncology (L.H.,
C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology
Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health,
10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; NVIDIA, Santa
Clara, Calif (J.T., D.Y., Z.X., D.X.); Department of Radiology, Clinical Center,
National Institutes of Health, Bethesda, Md (L.H., C.G., B.J.W.); and Department
of Radiology, Singapore General Hospital, Singapore (Y.M.L.)
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28
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Bleker J, Roest C, Yakar D, Huisman H, Kwee TC. The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI. J Magn Reson Imaging 2024; 59:1800-1806. [PMID: 37572098 DOI: 10.1002/jmri.28935] [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: 05/26/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models. PURPOSE To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting. STUDY TYPE Retrospective. POPULATION Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148). FIELD STRENGTH/SEQUENCE 1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps. ASSESSMENT A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models. STATISTICAL TESTS Area under the curve (AUC), DeLong test. The significance level used was 0.05. RESULTS The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67). DATA CONCLUSION Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation). EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jeroen Bleker
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Christian Roest
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Derya Yakar
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek Hospital (NCI-AVL), Amsterdam, The Netherlands
| | - Henkjan Huisman
- Department of Medical Imaging, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, Departments of Radiology, Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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Nakai H, Takahashi H, Adamo DA, LeGout JD, Kawashima A, Thomas JV, Froemming AT, Kuanar S, Lomas DJ, Humphreys MR, Dora C, Takahashi N. Decreased prostate MRI cancer detection rate due to moderate to severe susceptibility artifacts from hip prosthesis. Eur Radiol 2024; 34:3387-3399. [PMID: 37889268 DOI: 10.1007/s00330-023-10345-4] [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: 07/18/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 10/28/2023]
Abstract
OBJECTIVES To evaluate the impact of susceptibility artifacts from hip prosthesis on cancer detection rate (CDR) in prostate MRI. MATERIALS AND METHODS This three-center retrospective study included prostate MRI studies for patients without known prostate cancer between 2017 and 2021. Exams with hip prosthesis were searched on MRI reports. The degree of susceptibility artifact on diffusion-weighted images was retrospectively categorized into mild, moderate, and severe (> 66%, 33-66%, and < 33% of the prostate volume are evaluable) by blind reviewers. CDR was defined as the number of exams with Gleason score ≥7 detected by MRI (PI-RADS ≥3) divided by the total number of exams. For each artifact grade, control exams without hip prosthesis were matched (1:6 match), and CDR was compared. The degree of CDR reduction was evaluated with ratio, and influential factors were evaluated by expanding the equation. RESULTS Hip arthroplasty was present in 548 (4.8%) of the 11,319 MRI exams. CDR of the cases and matched control exams for each artifact grade were as follows: mild (n = 238), 0.27 vs 0.25, CDR ratio = 1.09 [95% CI: 0.87-1.37]; moderate (n = 143), 0.18 vs 0.27, CDR ratio = 0.67 [95% CI: 0.46-0.96]; severe (n = 167), 0.22 vs 0.28, CDR ratio = 0.80 [95% CI: 0.59-1.08]. When moderate and severe artifact grades were combined, CDR ratio was 0.74 [95% CI: 0.58-0.93]. CDR reduction was mostly attributed to the increased frequency of PI-RADS 1-2. CONCLUSION With moderate to severe susceptibility artifacts from hip prosthesis, CDR was decreased to 74% compared to the matched control. CLINICAL RELEVANCE STATEMENT Moderate to severe susceptibility artifacts from hip prosthesis may cause a non-negligible CDR reduction in prostate MRI. Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 was assigned. KEY POINTS • We proposed cancer detection rate as a diagnostic performance metric in prostate MRI. • With moderate to severe susceptibility artifacts secondary to hip arthroplasty, cancer detection rate decreased to 74% compared to the matched control. • Expanding indications for systematic prostate biopsy may be considered when PI-RADS 1-2 is assigned.
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Affiliation(s)
| | | | - Daniel A Adamo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - John V Thomas
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Shiba Kuanar
- Department of Radiology, 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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30
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Zhang C, Tu X, Dai J, Zhang Z, Shen C, Wu Q, Liu Z, Lin T, Qiu S, Yang L, Yang L, Zhang M, Cai D, Bao Y, Zeng H, Wei Q. Utilization trend of prebiopsy multiparametric magnetic resonance imaging and its impact on prostate cancer detection: Real-world insights from a high-volume center in southwest China. Prostate 2024; 84:539-548. [PMID: 38173301 DOI: 10.1002/pros.24669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/24/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Data on the utilization and effects of prebiopsy prostate multiparametric magnetic resonance imaging (mpMRI) to support its routine use in real-world setting are still scarce. OBJECTIVE To evaluate the change of clinical practice of prebiopsy mpMRI over time, and assess its diagnostic accuracy. DESIGN, SETTING, AND PARTICIPANTS We retrospectively analyzed data from 6168 patients who underwent primary prostate biopsy (PBx) between January 2011 and December 2021 and had prostate-specific antigen (PSA) values ranging from 3 to 100 ng/mL. INTERVENTION Prebiopsy MRI at the time of PBx. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS We performed general linear regression and to elucidate trends in the annual use of prebiopsy mpMRI and conducted multivariable logistic regression to evaluate the potential benefits of incorporating prebiopsy mpMRI for prostate cancer (PCa) detection. RESULTS AND LIMITATIONS The utilization of prebiopsy mpMRI significantly increased from 9.2% in 2011 to 75.0% in 2021 (p < 0.001). In addition, prebiopsy mpMRI significantly reduced negative PBx by 8.6% while improving the detection of clinically significant PCa (csPCa) by 7.0%. Regression analysis showed that the utilization of prebiopsy mpMRI was significantly associated with a 48% (95% confidence interval [CI]: 1.19-1.84) and 36% (95% CI: 1.12-1.66) increased PCa detection rate in the PSA 3-10 ng/mL and 10-20 ng/mL groups, respectively; and a 34% increased csPCa detection rate in the PSA 10-20 ng/mL group (95% CI: 1.09-1.64). The retrospective design and the single center cohort constituted the limitations of this study. CONCLUSIONS Our study demonstrated a notable rise in the utilization of prebiopsy mpMRI in the past decade. The adoption of this imaging technique was significantly associated with an increased probability of detecting prostate cancer. PATIENT SUMMARY From 2011 to 2021, we demonstrated a steady increase in the utilization of prebiopsy mpMRI among biopsy-naïve men. We also confirmed the positive impact of prebiopsy mpMRI utilization on the detection of prostate cancer.
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Affiliation(s)
- Chichen Zhang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiang Tu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Jindong Dai
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Zilong Zhang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Chenlan Shen
- Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyou Wu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenhua Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Tianhai Lin
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Shi Qiu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
- Department of Molecular Oncology, Institute of Oncology Research (IOR), Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
| | - Lu Yang
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Ling Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Mengni Zhang
- Department of Pathology and Laboratory of Pathology, State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
| | - Diming Cai
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Yige Bao
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiang Wei
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
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31
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Spinner JW, Purysko AS, Westphalen AC. Enhancing prostate MRI expertise: educational strategies for radiologists. Abdom Radiol (NY) 2024:10.1007/s00261-024-04325-5. [PMID: 38684548 DOI: 10.1007/s00261-024-04325-5] [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: 11/03/2023] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 05/02/2024]
Abstract
The adoption of multiparametric MRI (mpMRI) and the Prostate Imaging Reporting and Data System has significantly changed prostate cancer diagnosis and management. These advancements, alongside novel biomarkers and updated International Society of Uropathology grade groups, have improved cancer detection and prognostication. Despite this progress, varying levels of expertise in mpMRI among radiologists have resulted in inconsistent assessments, potentially leading to unnecessary procedures and diminished confidence in the modality. This review assesses the educational landscape for prostate MRI, highlighting available resources for radiologists at all professional stages. It emphasizes the need for targeted educational strategies to bridge knowledge gaps and improve patient care outcomes in prostate cancer management.
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Affiliation(s)
- Jesse W Spinner
- Department of Radiology, School of Medicine, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA, 98195, USA
| | - Andrei S Purysko
- Section of Abdominal Imaging Section and Nuclear Radiology Department, Cleveland Clinic Imaging Institute, 9500 Euclid Ave, Mail Code JB-322, Cleveland, OH, 44195, USA
| | - Antonio C Westphalen
- Departments of Radiology, Urology, and Radiation Oncology, School of Medicine, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA, 98195, USA.
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32
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Caglic I, Sushentsev N, Syer T, Lee KL, Barrett T. Biparametric MRI in prostate cancer during active surveillance: is it safe? Eur Radiol 2024:10.1007/s00330-024-10770-z. [PMID: 38656709 DOI: 10.1007/s00330-024-10770-z] [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: 02/02/2024] [Revised: 03/13/2024] [Accepted: 03/22/2024] [Indexed: 04/26/2024]
Abstract
Active surveillance (AS) is the preferred option for patients presenting with low-intermediate-risk prostate cancer. MRI now plays a crucial role for baseline assessment and ongoing monitoring of AS. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations aid radiological assessment of progression; however, current guidelines do not advise on MRI protocols nor on frequency. Biparametric (bp) imaging without contrast administration offers advantages such as reduced costs and increased throughput, with similar outcomes to multiparametric (mp) MRI shown in the biopsy naïve setting. In AS follow-up, the paradigm shifts from MRI lesion detection to assessment of progression, and patients have the further safety net of continuing clinical surveillance. As such, bpMRI may be appropriate in clinically stable patients on routine AS follow-up pathways; however, there is currently limited published evidence for this approach. It should be noted that mpMRI may be mandated in certain patients and potentially offers additional advantages, including improving image quality, new lesion detection, and staging accuracy. Recently developed AI solutions have enabled higher quality and faster scanning protocols, which may help mitigate against disadvantages of bpMRI. In this article, we explore the current role of MRI in AS and address the need for contrast-enhanced sequences. CLINICAL RELEVANCE STATEMENT: Active surveillance is the preferred plan for patients with lower-risk prostate cancer, and MRI plays a crucial role in patient selection and monitoring; however, current guidelines do not currently recommend how or when to perform MRI in follow-up. KEY POINTS: Noncontrast biparametric MRI has reduced costs and increased throughput and may be appropriate for monitoring stable patients. Multiparametric MRI may be mandated in certain patients, and contrast potentially offers additional advantages. AI solutions enable higher quality, faster scanning protocols, and could mitigate the disadvantages of biparametric imaging.
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Affiliation(s)
- Iztok Caglic
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Nikita Sushentsev
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Tom Syer
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
| | - Kang-Lung Lee
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tristan Barrett
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom.
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom.
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33
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Maki JH, Patel NU, Ulrich EJ, Dhaouadi J, Jones RW. Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review. Curr Probl Diagn Radiol 2024:S0363-0188(24)00072-0. [PMID: 38658286 DOI: 10.1067/j.cpradiol.2024.04.002] [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: 01/12/2024] [Revised: 03/14/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prostate cancer. Despite its success, prostate MRI continues to suffer from poor inter-reader variability and a low positive predictive value. The recent emergence of Artificial Intelligence (AI) to potentially improve diagnostic performance shows great potential. Understanding and interpreting the AI landscape as well as ever-increasing research literature, however, is difficult. This is in part due to widely varying study design and reporting techniques. This paper aims to address this need by first outlining the different types of AI used for the detection and diagnosis of prostate cancer, next deciphering how data collection methods, statistical analysis metrics (such as ROC and FROC analysis) and end points/outcomes (lesion detection vs. case diagnosis) affect the performance and limit the ability to compare between studies. Finally, this work explores the need for appropriately enriched investigational datasets and proper ground truth, and provides guidance on how to best conduct AI prostate MRI studies. Published in parallel, a clinical study applying this suggested study design was applied to review and report a multiple-reader multiple-case clinical study of 150 bi-parametric prostate MRI studies across nine readers, measuring physician performance both with and without the use of a recently FDA cleared Artificial Intelligence software.1.
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Affiliation(s)
- Jeffrey H Maki
- University of Colorado Anschutz Medical Center, Department of Radiology, 12401 E 17th Ave (MS L954), Aurora, Colorado, USA.
| | - Nayana U Patel
- University of New Mexico Department of Radiology, Albuquerque, NM, USA
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34
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Pellegrino F, Stabile A, Sorce G, Mazzone E, Cannoletta D, Cirulli GO, Quarta L, Leni R, Robesti D, Brembilla G, Gandaglia G, De Cobelli F, Montorsi F, Briganti A. Variability of mpMRI diagnostic performance according to the upfront individual patient risk of having clinically significant prostate cancer. Prostate 2024; 84:473-478. [PMID: 38149793 DOI: 10.1002/pros.24665] [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: 07/30/2023] [Revised: 11/30/2023] [Accepted: 12/15/2023] [Indexed: 12/28/2023]
Abstract
BACKGROUND To assess the variation of multiparametric magnetic resonance imaging (mpMRI) positive predictive value (PPV) according to each patient's risk of clinically significant prostate cancer (csPCa) based exclusively on clinical factors. METHODS We evaluated 999 patients with positive mpMRI (PI-RADS ≥ 3) receiving targeted (TBx) plus systematic prostate biopsy. We built a multivariable logistic regression analysis (MVA) using clinical risk factors to calculate the individual patients' risk of harboring csPCa at TBx. A second MVA tested the association between individual patients' clinical risk and mpMRI PPV accounting for the PI-RADS score. Finally, we plotted the PPV of each PI-RADS score by the individual patient pretest probability of csPCa using a LOWESS approach. RESULTS Overall, TBx found csPCa in 21%, 51%, and 80% of patients with PI-RADS 3, 4, and 5 lesions, respectively. At MVA, age, PSA, digital rectal examination (DRE), and prostate volume were significantly associated with the risk of csPCa at biopsy. DRE yielded the highest odds ratio (OR: 2.88; p < 0.001). The individual patient's clinical risk was significantly associated with mpMRI PPV (OR: 2.49; p < 0.001) using MVA. Plotting the mpMRI PPV according to the predicted clinical risks, we observed that for patients with clinical risk close to 0 versus patients with risk higher than 90%, the mpMRI PPV of PI-RADS 3, 4, and 5 ranged from 0% to 75%, from 0% to 96%, and from 45% to 100%, respectively. CONCLUSION mpMRI PPV varies according to the individual pretest patient's risk based on clinical factors. These findings should be considered in the decision-making process for patients with suspect MRI findings referred for a prostate biopsy. Moreover, our data support the need for further studies to create an individualized risk prediction tool.
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Affiliation(s)
- Francesco Pellegrino
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Armando Stabile
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Gabriele Sorce
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elio Mazzone
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Donato Cannoletta
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giuseppe Ottone Cirulli
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Leonardo Quarta
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Riccardo Leni
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Daniele Robesti
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Giorgio Gandaglia
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco Montorsi
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Alberto Briganti
- Division of Oncology/Unit of Urology, Soldera Prostate Cancer Lab, URI, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
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Björnebo L, Discacciati A, Falagario U, Vigneswaran HT, Jäderling F, Grönberg H, Eklund M, Nordström T, Lantz A. Biomarker vs MRI-Enhanced Strategies for Prostate Cancer Screening: The STHLM3-MRI Randomized Clinical Trial. JAMA Netw Open 2024; 7:e247131. [PMID: 38648061 PMCID: PMC11036143 DOI: 10.1001/jamanetworkopen.2024.7131] [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: 12/21/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024] Open
Abstract
Importance Prostate cancer guidelines often recommend obtaining magnetic resonance imaging (MRI) before a biopsy, yet MRI access is limited. To date, no randomized clinical trial has compared the use of novel biomarkers for risk estimation vs MRI-based diagnostic approaches for prostate cancer screening. Objective To evaluate biomarker-based risk estimation (Stockholm3 risk scores or prostate-specific antigen [PSA] levels) with systematic biopsies vs an MRI-enhanced strategy (PSA levels and MRI with systematic and targeted biopsy) for the detection of clinically significant prostate cancer in a screening setting. Design, Setting, and Participants This open-label randomized clinical trial conducted in Stockholm, Sweden, between April 4, 2018, and December 10, 2020, recruited men aged 50 to 74 years with no history of prostate cancer. Participants underwent blood sampling for PSA and Stockholm3 tests to estimate their risk of clinically significant prostate cancer (Gleason score ≥3 + 4). After the blood tests were performed, participants were randomly assigned in a 2:3 ratio to receive a Stockholm3 test with systematic biopsy (biomarker group) or a PSA test followed by MRI with systematic and targeted biopsy (MRI-enhanced group). Data were analyzed from September 1 to November 5, 2023. Interventions In the biomarker group, men with a Stockholm3 risk score of 0.15 or higher underwent systematic biopsies. In the MRI-enhanced group, men with a PSA level of 3 ng/mL or higher had an MRI and those with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3 or higher (range: 1-5, with higher scores indicating a higher likelihood of clinically significant prostate cancer) underwent targeted and systematic biopsies. Main Outcomes and Measures Primary outcome was detection of clinically significant prostate cancer (Gleason score ≥3 + 4). Secondary outcomes included detection of clinically insignificant cancer (Gleason score ≤6) and the number of biopsy procedures performed. Results Of 12 743 male participants (median [IQR] age, 61 [55-67] years), 5134 were assigned to the biomarker group and 7609 to the MRI-enhanced group. In the biomarker group, 8.0% of men (413) had Stockholm3 risk scores of 0.15 or higher and were referred for systematic biopsies. In the MRI-enhanced group, 12.2% of men (929) had a PSA level of 3 ng/mL or higher and were referred for MRI with biopsies if they had a PI-RADS score of 3 or higher. Detection rates of clinically significant prostate cancer were comparable between the 2 groups: 2.3% in the biomarker group and 2.5% in the MRI-enhanced group (relative proportion, 0.92; 95% CI, 0.73-1.15). More biopsies were performed in the biomarker group than in the MRI-enhanced group (326 of 5134 [6.3%] vs 338 of 7609 [4.4%]; relative proportion, 1.43 [95% CI, 1.23-1.66]), and more indolent prostate cancers were detected (61 [1.2%] vs 41 [0.5%]; relative proportion, 2.21 [95% CI, 1.49-3.27]). Conclusions and Relevance Findings of this randomized clinical trial indicate that combining a Stockholm3 test with systematic biopsies is comparable with MRI-based screening with PSA levels and systematic and targeted biopsies for detection of clinically significant prostate cancer, but this approach resulted in more biopsies as well as detection of a greater number of indolent cancers. In regions where access to MRI is lacking, the Stockholm3 test can aid in selecting patients for systematic prostate biopsy. Trial Registration ClinicalTrials.gov Identifier: NCT03377881.
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Affiliation(s)
- Lars Björnebo
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Andrea Discacciati
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ugo Falagario
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Urology and Kidney Transplantation, University of Foggia, Foggia, Italy
| | - Hari T. Vigneswaran
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Jäderling
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Diagnostic Radiology, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Nordström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Sciences, Danderyd Hospital, Danderyd, Sweden
| | - Anna Lantz
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
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36
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Li L, Shiradkar R, Gottlieb N, Buzzy C, Hiremath A, Viswanathan VS, MacLennan GT, Omil Lima D, Gupta K, Shen DL, Tirumani SH, Magi-Galluzzi C, Purysko A, Madabhushi A. Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology. Med Phys 2024; 51:2549-2562. [PMID: 37742344 PMCID: PMC10960735 DOI: 10.1002/mp.16753] [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: 04/07/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration. PURPOSE This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration. METHODS In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM. RESULTS Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment. CONCLUSIONS This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.
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Affiliation(s)
- Lin Li
- Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Rakesh Shiradkar
- Wallace H Coulter Department of Biomedical Engineering at Emory University and Georgia Institute of Technology
| | - Noah Gottlieb
- Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Christina Buzzy
- Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Amogh Hiremath
- Deptartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Vidya Sankar Viswanathan
- Wallace H Coulter Department of Biomedical Engineering at Emory University and Georgia Institute of Technology
| | - Gregory T. MacLennan
- Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Danly Omil Lima
- Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Karishma Gupta
- Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Daniel Lee Shen
- Department of Pathology and Urology, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | | | - Andrei Purysko
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anant Madabhushi
- Wallace H Coulter Department of Biomedical Engineering at Emory University and Georgia Institute of Technology
- Atlanta Veterans Administration Medical Center
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37
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Davenport MS. Efforts to Optimize Performance Assessment at Prostate MRI. J Am Coll Radiol 2024; 21:409-410. [PMID: 37813230 DOI: 10.1016/j.jacr.2023.08.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/03/2023] [Indexed: 10/11/2023]
Affiliation(s)
- Matthew S Davenport
- Vice Chair and Service Chief, Department of Radiology and Department of Urology, Michigan Medicine, Ann Arbor, Michigan.
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Nakai H, Nagayama H, Takahashi H, Froemming AT, Kawashima A, Bolan CW, Adamo DA, Carter RE, Fazzio RT, Tsuji S, Lomas DJ, Mynderse LA, Humphreys MR, Dora C, Takahashi N. Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer. J Am Coll Radiol 2024; 21:387-397. [PMID: 37838189 DOI: 10.1016/j.jacr.2023.07.030] [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: 05/18/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 10/16/2023]
Abstract
PURPOSE The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa). METHODS This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa. RESULTS A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line. CONCLUSIONS The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.
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Affiliation(s)
| | - Hiroki Nagayama
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; Department of Radiology, Nagasaki University School of Medicine, Nagasaki, Japan
| | | | - Adam T Froemming
- Division Chair of Abdominal Imaging, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Candice W Bolan
- Chief, Department of Radiology, Mayo Clinic, Jacksonville, Florida
| | - Daniel A Adamo
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Rickey E Carter
- Vice Chair, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Robert T Fazzio
- Division Chair of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Derek J Lomas
- Department of Urology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Chandler Dora
- Department of Urology, Mayo Clinic, Jacksonville, Florida
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Chang KP, Hsieh PF, Lin WC, Chang H. Growth Patterns of Prostate Cancers Undetected by Prostate 3T Multiparametric MRI. In Vivo 2024; 38:833-841. [PMID: 38418107 PMCID: PMC10905436 DOI: 10.21873/invivo.13508] [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: 11/13/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 03/01/2024]
Abstract
BACKGROUND/AIM The multiparametric magnetic resonance imaging (mpMRI)-ultrasound (US) fusion targeted biopsy (TB) is a useful diagnostic device for men with suspected prostate cancer (PC) and can increase the detection rate for clinically significant PCs (csPC). However, few studies have shown pathological findings of undetectable csPCs on the prostate mpMRI. PATIENTS AND METHODS This study investigated the growth patterns of csPC undetected in prostate mpMRI. The study enrolled 248 patients with suspected PCs and ≥PI-RADS 2 lesions, who then underwent mpMRI-US fusion TB and nearly prostate-mapping systematic biopsies (SB). A total 248 biopsies included 404 regions of interest in TB and 2976 mapping-regions in SB. RESULTS The detection rates of csPC, defined as PC grade group (GG) ≥2, were 42% in TB and 44% in SB, and the highest detection rate was 50%, using both TB and SB. Approximately 79% of PI-RADS 3/4/5 with any PC showed csPC. A total 201 PI-RADS 3/4/5 lesions showed benign prostatic hyperplasia, lymphocytic prostatitis, or fibromuscular stroma only in the core tissues. Notably, 22 csPCs detected in SB but undetected in prostate mpMRI preferentially showed a pattern of mixed well-formed and fused PC glands. The other patterns including cribriform glands and poorly formed glands with intracytoplasmic vacuoles were also seen. Approximately 85% of the 22 csPCs showed tumor volume less than 50% of core tissues. CONCLUSION Changes in prostatic stroma amounts, inflammation severity, tumor volume and growth patterns of PC glands affected the detectability of prostate mpMRI.
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Affiliation(s)
- Kai-Po Chang
- School of Medicine, China Medical University, Taichung, Taiwan, R.O.C
- Department of Pathology, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Po-Fan Hsieh
- School of Medicine, China Medical University, Taichung, Taiwan, R.O.C
- Department of Urology, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Wei-Ching Lin
- School of Medicine, China Medical University, Taichung, Taiwan, R.O.C
- Department of Radiology, China Medical University Hospital, Taichung, Taiwan, R.O.C
| | - Han Chang
- School of Medicine, China Medical University, Taichung, Taiwan, R.O.C.;
- Department of Pathology, China Medical University Hospital, Taichung, Taiwan, R.O.C
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40
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Nagayama H, Nakai H, Takahashi H, Froemming AT, Kawashima A, Bolan CW, Adamo DA, Carter RE, Fazzio RT, Tsuji S, Lomas DJ, Mynderse LA, Humphreys MR, Dora C, Takahashi N. Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI Performed for Clinical Suspicion of Prostate Cancer. J Am Coll Radiol 2024; 21:398-408. [PMID: 37820833 DOI: 10.1016/j.jacr.2023.07.031] [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: 05/18/2023] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 10/13/2023]
Abstract
PURPOSE To report cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI performed for clinical suspicion of prostate cancer (PCa). MATERIALS AND METHODS This retrospective single-institution, three-center study included patients who underwent MRI for clinical suspicion of PCa between 2017 and 2021. Patients with known PCa were excluded. Patient-level Prostate Imaging-Reporting and Data System (PI-RADS) score was extracted from the radiology report. AIR was defined as number of abnormal MRI (PI-RADS score 3-5) / total number of MRIs. CDR was defined as number of clinically significant PCa (csPCa: Gleason score ≥7) detected at abnormal MRI / total number of MRI. AIR, CDR, and CDR adjusted for pathology confirmation rate were calculated for each of three centers and pre-MRI biopsy status (biopsy-naive and previous negative biopsy). RESULTS A total of 9,686 examinations (8,643 unique patients) were included. AIR, CDR, and CDR adjusted for pathology confirmation rate were 45.4%, 23.8%, and 27.6% for center I; 47.2%, 20.0%, and 22.8% for center II; and 42.3%, 27.2%, and 30.1% for center III, respectively. Pathology confirmation rate ranged from 81.6% to 88.0% across three centers. AIR and CDR for biopsy-naive patients were 45.5% to 52.6% and 24.2% to 33.5% across three centers, respectively, and those for previous negative biopsy were 27.2% to 39.8% and 11.7% to 14.2% across three centers, respectively. CONCLUSION We reported CDR and AIR in prostate MRI for clinical suspicion of PCa. CDR needs to be adjusted for pathology confirmation rate and pre-MRI biopsy status for interfacility comparison.
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Affiliation(s)
- Hiroki Nagayama
- Department of Radiology, Mayo Clinic, Rochester, Minnesota; Department of Radiology, Nagasaki University School of Medicine, Nagasaki, Japan
| | | | | | - Adam T Froemming
- Division Chair of the Abdominal Imaging in Minnesota, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Candice W Bolan
- Chief, Department of Radiology, Mayo Clinic, Jacksonville, Florida
| | - Daniel A Adamo
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Rickey E Carter
- Vice Chair, Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, Florida
| | - Robert T Fazzio
- Division Chair of the Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | | | - Derek J Lomas
- Department of Urology, Mayo Clinic, Rochester, Minnesota
| | | | | | - Chandler Dora
- Department of Urology, Mayo Clinic, Jacksonville, Florida
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Patel HD, Halgrimson WR, Sweigert SE, Shea SM, Turk TMT, Quek ML, Gorbonos A, Flanigan RC, Goldberg A, Gupta GN. Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy. BJUI COMPASS 2024; 5:304-312. [PMID: 38371209 PMCID: PMC10869647 DOI: 10.1002/bco2.294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 09/04/2023] [Indexed: 02/20/2024] Open
Abstract
Objectives The aim of this study is to evaluate the impact of radiologist and urologist variability on detection of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) with magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) fusion prostate biopsies. Patients and methods The Prospective Loyola University MRI (PLUM) Prostate Biopsy Cohort (January 2015 to December 2020) was used to identify men receiving their first MRI and MRI/TRUS fusion biopsy for suspected PCa. Clinical, MRI and biopsy data were stratified by radiologist and urologist to evaluate variation in Prostate Imaging-Reporting and Data System (PI-RADS) grading, lesion number and cancer detection. Multivariable logistic regression (MVR) models and area under the curve (AUC) comparisons assessed the relative impact of individual radiologists and urologists. Results A total of 865 patients (469 biopsy-naïve) were included across 5 urologists and 10 radiologists. Radiologists varied with grading 15.4% to 44.8% of patients with MRI lesions as PI-RADS 3. PCa detection varied significantly by radiologist, from 34.5% to 66.7% (p = 0.003) for PCa and 17.2% to 50% (p = 0.001) for csPCa. Urologists' PCa diagnosis rates varied between 29.2% and 55.8% (p = 0.013) and between 24.6% and 39.8% (p = 0.36) for csPCa. After adjustment for case-mix on MVR, a fourfold to fivefold difference in PCa detection was observed between the highest-performing and lowest-performing radiologist (OR 0.22, 95%CI 0.10-0.47, p < 0.001). MVR demonstrated improved AUC for any PCa and csPCa detection when controlling for radiologist variation (p = 0.017 and p = 0.038), but controlling for urologist was not significant (p = 0.22 and p = 0.086). Any PCa detection (OR 1.64, 95%CI 1.06-2.55, p = 0.03) and csPCa detection (OR 1.57, 95%CI 1.00-2.48, p = 0.05) improved over time (2018-2020 vs. 2015-2017). Conclusions Variability among radiologists in PI-RADS grading is a key area for quality improvement significantly impacting the detection of PCa and csPCa. Variability for performance of MRI-TRUS fusion prostate biopsies exists by urologist but with less impact on overall detection of csPCa.
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Affiliation(s)
- Hiten D. Patel
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
- Department of Urology, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | | | - Sarah E. Sweigert
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - Steven M. Shea
- Department of RadiologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - Thomas M. T. Turk
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - Marcus L. Quek
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - Alex Gorbonos
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
| | | | - Ari Goldberg
- Department of RadiologyLoyola University Medical CenterMaywoodIllinoisUSA
| | - Gopal N. Gupta
- Department of UrologyLoyola University Medical CenterMaywoodIllinoisUSA
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Feng X, Chen X, Peng P, Zhou H, Hong Y, Zhu C, Lu L, Xie S, Zhang S, Long L. Values of multiparametric and biparametric MRI in diagnosing clinically significant prostate cancer: a multivariate analysis. BMC Urol 2024; 24:40. [PMID: 38365673 PMCID: PMC10870467 DOI: 10.1186/s12894-024-01411-0] [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: 03/22/2023] [Accepted: 01/16/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND To investigate the value of semi-quantitative and quantitative parameters (PI-RADS score, T2WI score, ADC, Ktrans, and Kep) based on multiparametric MRI (mpMRI) or biparametric MRI (bpMRI) combined with prostate specific antigen density (PSAD) in detecting clinically significant prostate cancer (csPCa). METHODS A total of 561 patients (276 with csPCa; 285 with non-csPCa) with biopsy-confirmed prostate diseases who underwent preoperative mpMRI were included. Prostate volume was measured for calculation of PSAD. Prostate index lesions were scored on a five-point scale on T2WI images (T2WI score) and mpMRI images (PI-RADS score) according to the PI-RADS v2.1 scoring standard. DWI and DCE-MRI images were processed to measure the quantitative parameters of the index lesion, including ADC, Kep, and Ktrans values. The predictors of csPCa were screened by logistics regression analysis. Predictive models of bpMRI and mpMRI were established. ROC curves were used to evaluate the efficacy of parameters and the model in diagnosing csPCa. RESULTS The independent diagnostic accuracy of PSA density, PI-RADS score, T2WI score, ADCrec, Ktrans, and Kep for csPCa were 80.2%, 89.5%, 88.3%, 84.6%, 58.5% and 61.6%, respectively. The diagnostic accuracy of bpMRI T2WI score and ADC value combined with PSAD was higher than that of PI-RADS score. The combination of mpMRI PI‑RADS score, ADC value with PSAD had the highest diagnostic accuracy. CONCLUSIONS PI-RADS score according to the PI-RADS v2.1 scoring standard was the most accurate independent diagnostic index. The predictive value of bpMRI model for csPCa was slightly lower than that of mpMRI model, but higher than that of PI-RADS score.
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Affiliation(s)
- Xiao Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Xin Chen
- Department of Radiology, Jiangjin Hospital, Chongqing University, No.725, Jiangzhou Avenue, Dingshan Street, Chongqing, 402260, China
| | - Peng Peng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - He Zhou
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Yi Hong
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Chunxia Zhu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Libing Lu
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Siyu Xie
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Sijun Zhang
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China
| | - Liling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, P.R. China.
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Deivasigamani S, Kotamarti S, Gupta RT, Polascik TJ. Re: Low Cancer Yield in PI-RADS 3 Upgraded to 4 by Dynamic Contrast-enhanced MRI: Is It Time To Reconsider Scoring Categorization? Eur Urol 2024; 85:180-181. [PMID: 37743198 DOI: 10.1016/j.eururo.2023.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023]
Affiliation(s)
| | | | - Rajan T Gupta
- Department of Urology, Duke Cancer Institute, Durham, NC, USA; Department of Radiology, Duke Cancer Institute, Durham, NC, USA
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Barrett T, Lee KL, Illerstam F, Thomsen HS, Jhaveri KS, Løgager V. Interactive training workshop to improve prostate mpMRI knowledge: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship. Insights Imaging 2024; 15:27. [PMID: 38270689 PMCID: PMC10810764 DOI: 10.1186/s13244-023-01574-8] [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: 07/22/2023] [Accepted: 11/05/2023] [Indexed: 01/26/2024] Open
Abstract
PURPOSE Prostate MRI is established for the investigation of patients presenting with suspected early prostate cancer. Outcomes are dependent on both image quality and interpretation. This study assessed the impact of an educational intervention on participants' theoretical knowledge of the technique. METHODS Eighty-one clinicians from two centers with varying experience in prostate MRI participated. Baseline knowledge was assessed with 10 written and image-based multiple-choice questions (MCQs) prior to a course including didactic lectures and hands-on interactive workshops on prostate MRI interpretation. Post-course, participants completed a second 10-question MCQ test, matched by format, themes, and difficulty, to assess for any improvement in knowledge and performance. Results were assessed using the Wilcoxon rank sum test, and the Wilcoxon signed-rank test for paired data. RESULTS Thirty-nine participants, including 25/49 (51.0%) and 14/32 (43.8%) at each center completed both assessments, with their results used for subsequent evaluation. Overall, there was a significant improvement from pre- (4.92 ± 2.41) to post-course scores (6.77 ± 1.46), p < 0.001 and at both Copenhagen (5.92 ± 2.25 to 7.36 ± 1.25) and Toronto (3.14 ± 1.51 to 5.71 ± 1.20); p = 0.005 and p = 0.002, respectively. Participants with no prostate MRI experience showed the greatest improvement (3.77 ± 1.97 to 6.18 ± 1.5, p < 0.001), followed by intermediate level (< 500 MRIs reported) experience (6.18 ± 1.99 to 7.46 ± 1.13, p = 0.058), then advanced (> 500 MRIs reported) experience (6.83 ± 2.48 to 7.67 ± 0.82, p = 0.339). CONCLUSIONS A dedicated prostate MRI teaching course combining didactic lectures and hands-on workshops significantly improved short-term theoretical knowledge of the technique for clinicians with differing levels of experience. CRITICAL RELEVANCE STATEMENT A dedicated teaching course significantly improved theoretical knowledge of the technique particularly for clinicians with less reporting experience and a lower baseline knowledge. The multiple-choice questions format mapped improved performance and may be considered as part of future MRI certification initiatives. KEY POINTS • Prostate MRI knowledge is important for image interpretation and optimizing acquisition sequences. • A dedicated teaching course significantly improved theoretical knowledge of the technique. • Improved performance was more apparent in clinicians with less reporting experience and a lower baseline knowledge.
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Affiliation(s)
- Tristan Barrett
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK.
| | - Kang-Lung Lee
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | - Henrik S Thomsen
- Department of Radiology, Herlev Gentofte University Hospital, Herlev, Denmark
| | - Kartik S Jhaveri
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Ave, 3-957, Toronto, ON, M5G 2M9, Canada
| | - Vibeke Løgager
- Department of Radiology, Herlev Gentofte University Hospital, Herlev, Denmark
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Rajendran I, Lee KL, Thavaraja L, Barrett T. Risk stratification of prostate cancer with MRI and prostate-specific antigen density-based tool for personalized decision making. Br J Radiol 2024; 97:113-119. [PMID: 38263825 PMCID: PMC11027333 DOI: 10.1093/bjr/tqad027] [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: 07/04/2023] [Revised: 09/22/2023] [Accepted: 10/03/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES MRI is now established for initial prostate cancer diagnosis; however, there is no standardized pathway to avoid unnecessary biopsy in low-risk patients. Our study aimed to test previously proposed MRI-focussed and risk-adapted biopsy decision models on a real-world dataset. METHODS Single-centre retrospective study performed on 2055 biopsy naïve patients undergoing MRI. Diagnostic pathways included "biopsy all", "MRI-focussed" and two risk-based MRI-directed pathways. Risk thresholds were based on prostate-specific antigen (PSA) density as low (<0.10 ng mL-2), intermediate (0.10-0.15 ng mL-2), high (0.15-0.20 ng mL-2), or very high-risk (>0.20 ng mL-2). The outcome measures included rates of biopsy avoidance, detection of clinically significant prostate cancer (csPCa), missed csPCa, and overdiagnosis of insignificant prostate cancer (iPCa). RESULTS Overall cancer rate was 39.9% (819/2055), with csPCa (Grade-Group ≥2) detection of 30.3% (623/2055). In men with a negative MRI (Prostate Imaging-Reporting and Data System, PI-RADS 1-2), the risk of cancer was 1.2%, 2.6%, 9.0%, and 12.9% in the low, intermediate, high, and very high groups, respectively; for PI-RADS score 3 lesions, the rates were 10.5%, 14.3%, 25.0%, and 33.3%, respectively. MRI-guided pathway and risk-based pathway with a low threshold missed only 1.6% csPCa with a biopsy-avoidance rate of 54.4%, and the risk-based pathway with a higher threshold avoided 62.9% (1292/2055) of biopsies with 2.9% (61/2055) missed csPCa detection. Decision curve analysis found that the "risk-based low threshold" pathway has the highest net benefit for probability thresholds between 3.6% and 13.9%. CONCLUSION Combined MRI and PSA-density risk-based pathways can be a helpful decision-making tool enabling high csPCa detection rates with the benefit of biopsy avoidance and reduced iPCa detection. ADVANCES IN KNOWLEDGE This real-world dataset from a large UK-based cohort confirms that combining MRI scoring with PSA density for risk stratification enables safe biopsy avoidance and limits the over-diagnosis of insignificant cancers.
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Affiliation(s)
- Ishwariya Rajendran
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Kang-Lung Lee
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge CB2 0QQ, United Kingdom
- Department of Radiology, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Liness Thavaraja
- School of Medicine, Addenbrooke’s Hospital, Cambridge CB2 0SP, United Kingdom
| | - Tristan Barrett
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge CB2 0QQ, United Kingdom
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Yilmaz EC, Lin Y, Belue MJ, Harmon SA, Phelps TE, Merriman KM, Hazen LA, Garcia C, Johnson L, Lay NS, Toubaji A, Merino MJ, Patel KR, Parnes HL, Law YM, Wood BJ, Gurram S, Choyke PL, Pinto PA, Turkbey B. PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy. AJR Am J Roentgenol 2024; 222:e2329964. [PMID: 37729551 DOI: 10.2214/ajr.23.29964] [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] [Indexed: 09/22/2023]
Abstract
BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.
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Affiliation(s)
- Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Yue Lin
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Stephanie A Harmon
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Tim E Phelps
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Katie M Merriman
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Lindsey A Hazen
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Charisse Garcia
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Latrice Johnson
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Nathan S Lay
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Antoun Toubaji
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD
| | - Maria J Merino
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Howard L Parnes
- Division of Cancer Prevention, National Cancer Institute, NIH, Bethesda, MD
| | - Yan Mee Law
- Department of Radiology, Singapore General Hospital, Singapore
| | - Bradford J Wood
- Center for Interventional Oncology, National Cancer Institute, NIH, Bethesda, MD
- Department of Radiology, Clinical Center, NIH, Bethesda, MD
| | - Sandeep Gurram
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, NIH, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892
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Englman C, Barrett T, Moore CM, Giganti F. Active Surveillance for Prostate Cancer: Expanding the Role of MR Imaging and the Use of PRECISE Criteria. Radiol Clin North Am 2024; 62:69-92. [PMID: 37973246 DOI: 10.1016/j.rcl.2023.06.009] [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: 11/19/2023]
Abstract
Multiparametric magnetic resonance (MR) imaging has had an expanding role in active surveillance (AS) for prostate cancer. It can improve the accuracy of prostate biopsies, assist in patient selection, and help monitor cancer progression. The PRECISE recommendations standardize reporting of serial MR imaging scans during AS. We summarize the evidence on MR imaging-led AS and provide a clinical primer to help report using the PRECISE criteria. Some limitations to both serial imaging and the PRECISE recommendations must be considered as we move toward a more individualized risk-stratified approach to AS.
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Affiliation(s)
- Cameron Englman
- Department of Radiology, University College London Hospital NHS Foundation Trust, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK; Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Box 218, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK; Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Box 218, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 0QQ, UK
| | - Caroline M Moore
- Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK; Department of Urology, University College London Hospital NHS Foundation Trust, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK; Division of Surgery & Interventional Science, University College London, 3rd Floor, Charles Bell House, 43-45 Foley Street, London, W1W7TY, UK.
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48
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Castellucci P, Mei R, Farolfi A, Nanni C, Fanti S. Potential Clinical Applications of Dedicated Prostate Positron Emission Tomography. PET Clin 2024; 19:119-124. [PMID: 37777381 DOI: 10.1016/j.cpet.2023.09.003] [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: 10/02/2023]
Abstract
The diagnosis of prostate cancer (PCa) is usually based on transrectal or transperineal biopsies (from 12 to 24 samples) in most cases after the performance of a dedicated MRI and/or transrectal ultrasound. A small-dedicated PET scanner could improve spatial resolution and increase sensitivity, allowing a precise detection and location of the PCa foci, thus allowing an image-guided biopsy. In this short review, we will focus our attention on the potential application of a dedicated prostate PET scanner and on the prototype that has been already assembled for this purpose.
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Affiliation(s)
- Paolo Castellucci
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
| | - Riccardo Mei
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Andrea Farolfi
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Cristina Nanni
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Stefano Fanti
- Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
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49
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Lawrence EM. Editorial Comment: Similar Upgrade and Downgrade Rates Between PI-RADS Version 2.0 and Version 2.1. AJR Am J Roentgenol 2024; 222:e2330325. [PMID: 37818961 DOI: 10.2214/ajr.23.30325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Affiliation(s)
- Edward M Lawrence
- University of Wisconsin School of Medicine and Public Health, William S. Middleton VA Hospital, Madison, WI,
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50
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Lin Y, Johnson LA, Fennessy FM, Turkbey B. Prostate Cancer Local Staging with Magnetic Resonance Imaging. Radiol Clin North Am 2024; 62:93-108. [PMID: 37973247 PMCID: PMC10656475 DOI: 10.1016/j.rcl.2023.06.010] [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: 11/19/2023]
Abstract
Accurate determination of the local stage of prostate cancer is crucial for treatment planning and prognosis. The primary objective of local staging is to distinguish between organ-confined and locally advanced disease, with the latter carrying a worse clinical prognosis. The presence of locally advanced disease features of prostate cancer, such as extra-prostatic extension, seminal vesicle invasion, and positive surgical margin, can impact the choice of treatment. Over the past decade, multiparametric MRI (mpMRI) has become the preferred imaging modality for the local staging of prostate cancer and has been shown to provide accurate information on the location and extent of disease. It has demonstrated superior performance compared to staging based on traditional clinical nomograms. Despite being a relatively new technique, mpMRI has garnered considerable attention and ongoing investigations. Therefore, in this review, we will discuss the current use of mpMRI on prostate cancer local staging.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA
| | - Latrice A Johnson
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA
| | - Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Drive, MSC 1182, Building 10, Room B3B85, Bethesda, MD 20892, USA.
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