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Kang Z, Margolis DJ, Tian Y, Li Q, Wang S, Wang L. Clinical-imaging metrics for the diagnosis of prostate cancer in PI-RADS 3 lesions. Urol Oncol 2024:S1078-1439(24)00525-8. [PMID: 38969546 DOI: 10.1016/j.urolonc.2024.06.014] [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: 05/11/2024] [Revised: 06/06/2024] [Accepted: 06/13/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVE To explore the feasibility and efficacy of clinical-imaging metrics in the diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in prostate imaging-reporting and data system (PI-RADS) category 3 lesions. METHODS A retrospective analysis was conducted on lesions diagnosed as PI-RADS 3. They were categorized into benign, non-csPCa and csPCa groups. Apparent diffusion coefficient (ADC), T2-weighted imaging signal intensity (T2WISI), coefficient of variation of ADC and T2WISI, prostate-specific antigen density (PSAD), ADC density (ADCD), prostate-specific antigen lesion volume density (PSAVD) and ADC lesion volume density (ADCVD) were measured and calculated. Univariate and multivariate analyses were used to identify risk factors associated with PCa and csPCa. Receiver operating characteristic curve (ROC) and decision curves were utilized to assess the efficacy and net benefit of independent risk factors. RESULTS Among 202 patients, 133 had benign prostate disease, 25 non-csPCa and 44 csPCa. Age, PSA and lesion location showed no significant differences (P > 0.05) among the groups. T2WISI and coefficient of variation of ADC (ADCcv) were independent risk factors for PCa in PI-RADS 3 lesions, yielding an area under the curve (AUC) of 0.68. ADC was an independent risk factor for csPCa in PI-RADS 3 lesions, yielding an AUC of 0.65. Decision curve analysis showed net benefit for patients at certain probability thresholds. CONCLUSIONS T2WISI and ADCcv, along with ADC, respectively showed considerable promise in enhancing the diagnosis of PCa and csPCa in PI-RADS 3 lesions.
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Affiliation(s)
- Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Daniel J Margolis
- Department of Radiology, Weill Cornell Medicine/ New York Presbyterian, New York, NY, USA
| | - Ye Tian
- Department of Urology, Capital Medical University Affiliated Beijing Friendship Hospital, Beijing, China
| | - Qiubai Li
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Kwe J, Baunacke M, Boehm K, Platzek I, Thomas C, Borkowetz A. PI-RADS upgrading as the strongest predictor for the presence of clinically significant prostate cancer in patients with initial PI-RADS-3 lesions. World J Urol 2024; 42:84. [PMID: 38363332 PMCID: PMC10873230 DOI: 10.1007/s00345-024-04776-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: 06/30/2023] [Accepted: 01/08/2024] [Indexed: 02/17/2024] Open
Abstract
PURPOSE Unclear lesions on multiparametric magnetic resonance tomography (mpMRI) are challenging for the indication of biopsy in patients with clinical suspicion of prostate cancer (PCa). The aim of this study is the validation of the detection rate of clinically significant PCa (csPCa) in patients with PI-RADS 3 findings and to determine the appropriate follow-up strategy. METHODS In this retrospective single-center study, patients with maximum PI-RADS 3 lesions underwent targeted MRI/ultrasound-fusion biopsy (tPbx) combined with systematic 12-core biopsy (sPbx) and follow-up mpMRI with further control biopsy. We assessed the evolution of MRI findings (PI-RADS, volume of the lesion), clinical parameters and histopathology in follow-up MRI and biopsies. The primary objective is the detection rate of csPCa, defined as ISUP ≥ 2 findings. RESULTS A total of 126 patients (median PSA 6.65 ng/ml; median PSA-density (PSAD) 0.13 ng/ml2) were included. The initial biopsy identified low-risk PCa in 24 cases (19%). During follow-up biopsy, 22.2% of patients showed PI-RADS upgrading (PI-RADS > 3), and 29 patients (23%) exhibited a tumor upgrading. Patients with PI-RADS upgrading had a higher risk of csPCa compared to those without PI-RADS upgrading (42.9% vs. 9.18%, p < 0.05). PI-RADS upgrading was identified as an independent predictor for csPCa in follow-up biopsy (OR 16.20; 95% CI 1.17-224.60; p = 0.038). CONCLUSION Patients with stable PI-RADS 3 findings may not require a follow-up biopsy. Instead, it is advisable to schedule an MRI, considering that PI-RADS upgrading serves as an independent predictor for csPCa.
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Affiliation(s)
- Jeremy Kwe
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Martin Baunacke
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Katharina Boehm
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Ivan Platzek
- Department of Radiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Thomas
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany
| | - Angelika Borkowetz
- Department of Urology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307, Dresden, Germany.
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Wang K, Xing Z, Kong Z, Yu Y, Chen Y, Zhao X, Song B, Wang X, Wu P, Wang X, Xue Y. Artificial intelligence as diagnostic aiding tool in cases of Prostate Imaging Reporting and Data System category 3: the results of retrospective multi-center cohort study. Abdom Radiol (NY) 2023; 48:3757-3765. [PMID: 37740046 DOI: 10.1007/s00261-023-03989-9] [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: 03/31/2023] [Revised: 06/12/2023] [Accepted: 06/13/2023] [Indexed: 09/24/2023]
Abstract
PURPOSE To study the effect of artificial intelligence (AI) on the diagnostic performance of radiologists in interpreting prostate mpMRI images of the PI-RADS 3 category. METHODS In this multicenter study, 16 radiologists were invited to interpret prostate mpMRI cases with and without AI. The study included a total of 87 cases initially diagnosed as PI-RADS 3 by radiologists without AI, with 28 cases being clinically significant cancers (csPCa) and 59 cases being non-csPCa. The study compared the diagnostic efficacy between readings without and with AI, the reading time, and confidence levels. RESULTS AI changed the diagnosis in 65 out of 87 cases. Among the 59 non-csPCa cases, 41 were correctly downgraded to PI-RADS 1-2, and 9 were incorrectly upgraded to PI-RADS 4-5. For the 28 csPCa cases, 20 were correctly upgraded to PI-RADS 4-5, and 5 were incorrectly downgraded to PI-RADS 1-2. Radiologists assisted by AI achieved higher diagnostic specificity and accuracy than those without AI [0.695 vs 0.000 and 0.736 vs 0.322, both P < 0.001]. Sensitivity with AI was not significantly different from that without AI [0.821 vs 1.000, P = 1.000]. AI reduced reading time significantly compared to without AI (mean: 351 seconds, P < 0.001). The diagnostic confidence score with AI was significantly higher than that without AI (Cohen Kappa: -0.016). CONCLUSION With the help of AI, there was an improvement in the diagnostic accuracy of PI-RADS category 3 cases by radiologists. There is also an increase in diagnostic efficiency and diagnostic confidence.
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Affiliation(s)
- Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China
| | - Zhangli Xing
- Department of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China
| | - Zixuan Kong
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No. 467, Zhongshan Road, Shahekou District, Dalian, 116023, Liaoning Province, China
| | - Yang Yu
- Department of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District, Chengdu, 610044, Sichuan Province, China
| | - Xiangpeng Zhao
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, No. 467, Zhongshan Road, Shahekou District, Dalian, 116023, Liaoning Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, No. 37, Guoxue Lane, Wuhou District, Chengdu, 610044, Sichuan Province, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., No. 97, Changping Road, Shahe Town, Changping District, Beijing, 102200, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., No. 97, Changping Road, Shahe Town, Changping District, Beijing, 102200, China
| | - Xiaoying Wang
- Peking University First Hospital, No. 8, Xishku Road, Xicheng District, Beijing, 100034, China.
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China.
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Cereser L, Evangelista L, Giannarini G, Girometti R. Prostate MRI and PSMA-PET in the Primary Diagnosis of Prostate Cancer. Diagnostics (Basel) 2023; 13:2697. [PMID: 37627956 PMCID: PMC10453091 DOI: 10.3390/diagnostics13162697] [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: 06/15/2023] [Revised: 07/29/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023] Open
Abstract
Over the last years, prostate magnetic resonance imaging (MRI) has gained a key role in the primary diagnosis of clinically significant prostate cancer (csPCa). While a negative MRI can avoid unnecessary prostate biopsies and the overdiagnosis of indolent cancers, a positive examination triggers biopsy samples targeted to suspicious imaging findings, thus increasing the diagnosis of csPCa with a sensitivity and negative predictive value of around 90%. The limitations of MRI, including suboptimal positive predictive values, are fueling debate on how to stratify biopsy decisions and management based on patient risk and how to correctly estimate it with clinical and/or imaging findings. In this setting, "next-generation imaging" imaging based on radiolabeled Prostate-Specific Membrane Antigen (PSMA)-Positron Emission Tomography (PET) is expanding its indications both in the setting of primary staging (intermediate-to-high risk patients) and primary diagnosis (e.g., increasing the sensitivity of MRI or acting as a problem-solving tool for indeterminate MRI cases). This review summarizes the current main evidence on the role of prostate MRI and PSMA-PET as tools for the primary diagnosis of csPCa, and the different possible interaction pathways in this setting.
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Affiliation(s)
- Lorenzo Cereser
- Institute of Radiology, Department of Medicine, University of Udine, 20072 Milan, Italy;
- University Hospital S. Maria della Misericordia, Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Gianluca Giannarini
- Urology Unit, University Hospital S. Maria della Misericordia, Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, University of Udine, 20072 Milan, Italy;
- University Hospital S. Maria della Misericordia, Azienda Sanitaria-Universitaria Friuli Centrale (ASUFC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
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5
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Nicola R, Bittencourt LK. PI-RADS 3 lesions: a critical review and discussion of how to improve management. Abdom Radiol (NY) 2023; 48:2401-2405. [PMID: 37160472 DOI: 10.1007/s00261-023-03929-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/15/2023] [Accepted: 04/18/2023] [Indexed: 05/11/2023]
Abstract
Since the publication of PI-RADS v1 in 2012, the debate regarding the question of how to manage PI-RADS 3 lesions has been mostly unsolved. However, based on our review of the current literature we discuss possible solutions and improvements to the original classification, factors such as PSAD (Prostate Specific Antigen Density), age, and tumor volume, in the decision of whether to proceed with a biopsy or not.
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Affiliation(s)
- Refky Nicola
- Division of Abdominal Radiology, SUNY-Upstate Medical University, 750 East Adams St, Syracuse, NY, 13210, USA.
| | - Leonardo Kayat Bittencourt
- School of Medicine, Abdominal Imaging, Case Western University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
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Gaudiano C, Mottola M, Bianchi L, Corcioni B, Braccischi L, Tomassoni MT, Cattabriga A, Cocozza MA, Giunchi F, Schiavina R, Fanti S, Fiorentino M, Brunocilla E, Mosconi C, Bevilacqua A. An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience. Cancers (Basel) 2023; 15:3438. [PMID: 37444548 DOI: 10.3390/cancers15133438] [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: 05/22/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.
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Affiliation(s)
- Caterina Gaudiano
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Margherita Mottola
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Lorenzo Bianchi
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Beniamino Corcioni
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Lorenzo Braccischi
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | | | - Arrigo Cattabriga
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Francesca Giunchi
- Department of Pathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Riccardo Schiavina
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Stefano Fanti
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Department of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Michelangelo Fiorentino
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Eugenio Brunocilla
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
- Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, 40138 Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, 40126 Bologna, Italy
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7
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Girometti R, Giannarini G, De Martino M, Caregnato E, Cereser L, Soligo M, Rozze D, Pizzolitto S, Isola M, Zuiani C. Multivariable stratification of PI-RADS version 2.1 categories for the risk of false-positive target biopsy: Impact on prostate biopsy decisions. Eur J Radiol 2023; 165:110897. [PMID: 37300933 DOI: 10.1016/j.ejrad.2023.110897] [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: 03/27/2023] [Revised: 04/30/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE To identify clinical and multiparametric magnetic resonance imaging (mpMRI) factors predicting false positive target biopsy (FP-TB) of prostate imaging reporting and data system version 2.1 (PI-RADSv2.1) ≥ 3 findings. METHOD We retrospectively included 221 men with and without previous negative prostate biopsy who underwent 3.0 T/1.5 T mpMRI for suspicious clinically significant prostate cancer (csPCa) between April 2019-July 2021. A study coordinator revised mpMRI reports provided by one of two radiologists (experience of > 1500/>500 mpMRI examinations, respectively) and matched them with the results of transperineal systematic biopsy plus fusion target biopsy (TB) of PI-RADSv2.1 ≥ 3 lesions or PI-RADSv2.1 ≤ 2 men with higher clinical risk. A multivariable model was built to identify features predicting FP-TB of index lesions, defined as the absence of csPCa (International Society of Urogenital Pathology [ISUP] ≥ 2). The model was internally validated with the bootstrap technique, receiving operating characteristics (ROC) analysis, and decision analysis. RESULTS Features significantly associated with FP-TB were age < 65 years (odds ratio [OR] 2.77), prostate-specific antigen density (PSAD) < 0.15 ng/mL/mL (OR 2.45), PI-RADS 4/5 category vs. category 3 (OR 0.15/0.07), and multifocality (OR 0.46), with a 0.815 area under the curve (AUC) in assessing FP-TB. When adjusting PI-RADSv2.1 categorization for the model, mpMRI showed 87.5% sensitivity and 79.9% specificity for csPCa, with a greater net benefit in triggering biopsy compared to unadjusted categorization or adjustment for PSAD only at decision analysis, from threshold probability ≥ 15%. CONCLUSION Adjusting PI-RADSv2.1 categories for a multivariable risk of FP-TB is potentially more effective in triggering TB of index lesions than unadjusted PI-RADS categorization or adjustment for PSAD alone.
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Affiliation(s)
- Rossano Girometti
- Institute of Radiology, Department of Medicine (DAME), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria ella Misericordia, 15, 33100 Udine, Italy.
| | - Gianluca Giannarini
- Urology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Maria De Martino
- Division of Medical Statistics, Department of Medicine (DAME), University of Udine, Udine, Italy, pl.le Kolbe, 4, 33100 Udine, Italy
| | - Elena Caregnato
- Institute of Radiology, Department of Medicine (DAME), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria ella Misericordia, 15, 33100 Udine, Italy
| | - Lorenzo Cereser
- Institute of Radiology, Department of Medicine (DAME), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria ella Misericordia, 15, 33100 Udine, Italy
| | - Matteo Soligo
- Urology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Davide Rozze
- Pathology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Stefano Pizzolitto
- Pathology Unit, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15, 33100 Udine, Italy
| | - Miriam Isola
- Division of Medical Statistics, Department of Medicine (DAME), University of Udine, Udine, Italy, pl.le Kolbe, 4, 33100 Udine, Italy
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine (DAME), University of Udine, University Hospital S. Maria della Misericordia - Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria ella Misericordia, 15, 33100 Udine, Italy
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8
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Yang J, Tang Y, Zhou C, Zhou M, Li J, Hu S. The use of 68 Ga-PSMA PET/CT to stratify patients with PI-RADS 3 lesions according to clinically significant prostate cancer risk. Prostate 2023; 83:430-439. [PMID: 36544382 DOI: 10.1002/pros.24475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 11/13/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Prostate imaging reporting and data system (PI-RADS) category 3 lesions represent a "gray zone," having an equivocal risk of presenting as clinically significant prostate cancer (csPCa). 68 Ga-labelled prostate-specific membrane antigen (68 Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has been identified as a diagnostic tool that can help to predict cases of primary PCa. We aimed to explore diagnostic value of 68 Ga-PSMA PET/CT for csPCa in PI-RADS 3 lesions to aid in decision-making and avoid unnecessary biopsies. METHODS A total of 78 men with PI-RADS 3 lesions who underwent both 68 Ga-PSMA PET/CT and transrectal ultrasound/magnetic resonance imaging (MRI) fusion-guided biopsy were enrolled. Images were analyzed by respective physicians who were blinded to the pathological results. Receiver operating characteristic (ROC) curve analysis and decision curve analysis were used to evaluate the diagnostic performance of univariate and multivariate analyses. RESULTS A total of 26/78 men had pathologically confirmed csPCa. A lower ADCT/ADCCLP (0.65 vs. 0.71, p = 0.018), smaller prostate volume (25.27 vs. 42.79 ml, p < 0.001), lower free prostate-specific antigen/total prostate-specific antigen (0.11 vs. 0.16, p < 0.001), higher PSA level (13.45 vs. 7.90 ng/ml, p = 0.001), higher PSA density (0.40 vs. 0.16 ng/ml2 , p < 0.001), higher SUVmax (9.80 vs. 4.40, p < 0.001) and SUVT/BGp (2.41 vs. 1.00, p < 0.001) were associated with csPCa. ROC analysis illustrated the improvement in SUVmax and SUVT/BGp compared with all independent and combined clinical features as well as multiparametric magnetic resonance imaging (mpMRI) features for csPCa detection. The net benefits of SUVmax and SUVT/BGp were superior to those of other features, respectively. With cutoff values of 5.0 for SUVmax and 1.4 for SUVT/BGp, the diagnostic sensitivity and specificity for csPCa were 96.2%, 100% and 80.8%, 84.6%, respectively. CONCLUSION 68 Ga-PSMA PET/CT is potentially capable of stratifying men with PI-RADS 3 lesions according to the presence of csPCa and has better performance than the model established based on clinical and mpMRI features.
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Affiliation(s)
- Jinhui Yang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Chuanchi Zhou
- Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Ming Zhou
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jian Li
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders (XIANGYA), Xiangya Hospital, Central South University, Changsha, Hunan, China
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9
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Natural History of Patients with Prostate MRI Likert 1-3 and Development of RosCaP: a Multivariate Risk Score for Clinically Significant Cancer. Clin Genitourin Cancer 2023; 21:162-170. [PMID: 35970760 DOI: 10.1016/j.clgc.2022.07.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/21/2022] [Accepted: 07/25/2022] [Indexed: 02/01/2023]
Abstract
INTRODUCTION Clinically significant prostate cancer (csCaP) with Gleason ≥3 + 4 is found in 10% negative prebiopsy multiparametric (mp) MRI cases and varies widely for equivocal mpMRI cases. The objective of this study was to investigate long-term outcomes of patients with negative and equivocal mpMRIs and to develop a predictive score for csCaP risk stratification in this group. PATIENTS AND METHODS Patients who underwent an upfront mpMRI between May 2015 and March 2018 with an MRI score Likert 1 to 3 were included in the study. Patients had either a CaP diagnosis at MRI-targeted biopsy or were not diagnosed and attended follow-up in the community. Outcomes were analysed through the Kaplan-Meier estimator and Cox Model. Regression coefficients of significant variables were used to develop a Risk of significant Cancer of the Prostate score (RosCaP). RESULTS At first assessment 281/469 patients had mpMRI only and 188/469 mpMRI and biopsy, 26 csCaP were found at biopsy, including 10/26 in Likert 3 patients. 12/371 patients discharged without CaP after first assessment were diagnosed with csCaP during a median of 34.2 months' follow-up, 11/12 diagnosis occurred in patients omitting initial biopsy. csCaP diagnosis-free survival was 95.7% in the MRI group and 99.1% in the biopsy group. From these outcomes, a continuous RosCaP score was developed: RosCaP = 0.083 x Age - 0.202 x (1/PSA Density) + 0.786 (if Likert 3), and 4 risk classes were proposed. Limitations include retrospective design and absence of external validation. CONCLUSION Age, PSA Density and MRI Likert score were significantly associated to the risk of csCaP and utilised to devise the novel RosCap predictive score focused to support risk assessment in patients with negative or equivocal mpMRI results.
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Ferraro DA, Hötker AM, Becker AS, Mebert I, Laudicella R, Baltensperger A, Rupp NJ, Rueschoff JH, Müller J, Mortezavi A, Sapienza MT, Eberli D, Donati OF, Burger IA. 68Ga-PSMA-11 PET/MRI versus multiparametric MRI in men referred for prostate biopsy: primary tumour localization and interreader agreement. Eur J Hybrid Imaging 2022; 6:14. [PMID: 35843966 PMCID: PMC9288941 DOI: 10.1186/s41824-022-00135-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background Magnetic resonance imaging (MRI) is recommended by the European Urology Association guidelines as the standard modality for imaging-guided biopsy. Recently positron emission tomography with prostate-specific membrane antigen (PSMA PET) has shown promising results as a tool for this purpose. The aim of this study was to compare the accuracy of positron emission tomography with prostate-specific membrane antigen/magnetic resonance imaging (PET/MRI) using the gallium-labeled prostate-specific membrane antigen (68Ga-PSMA-11) and multiparametric MRI (mpMRI) for pre-biopsy tumour localization and interreader agreement for visual and semiquantitative analysis. Semiquantitative parameters included apparent diffusion coefficient (ADC) and maximum lesion diameter for mpMRI and standardized uptake value (SUVmax) and PSMA-positive volume (PSMAvol) for PSMA PET/MRI. Results Sensitivity and specificity were 61.4% and 92.9% for mpMRI and 66.7% and 92.9% for PSMA PET/MRI for reader one, respectively. RPE was available in 23 patients and 41 of 47 quadrants with discrepant findings. Based on RPE results, the specificity for both imaging modalities increased to 98% and 99%, and the sensitivity improved to 63.9% and 72.1% for mpMRI and PSMA PET/MRI, respectively. Both modalities yielded a substantial interreader agreement for primary tumour localization (mpMRI kappa = 0.65 (0.52–0.79), PSMA PET/MRI kappa = 0.73 (0.61–0.84)). ICC for SUVmax, PSMAvol and lesion diameter were almost perfect (≥ 0.90) while for ADC it was only moderate (ICC = 0.54 (0.04–0.78)). ADC and lesion diameter did not correlate significantly with Gleason score (ρ = 0.26 and ρ = 0.16) while SUVmax and PSMAvol did (ρ = − 0.474 and ρ = − 0.468). Conclusions PSMA PET/MRI has similar accuracy and reliability to mpMRI regarding primary prostate cancer (PCa) localization. In our cohort, semiquantitative parameters from PSMA PET/MRI correlated with tumour grade and were more reliable than the ones from mpMRI. Supplementary Information The online version contains supplementary material available at 10.1186/s41824-022-00135-4.
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Wei X, Xu J, Zhong S, Zou J, Cheng Z, Ding Z, Zhou X. Diagnostic value of combining PI-RADS v2.1 with PSAD in clinically significant prostate cancer. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3574-3582. [PMID: 35788882 DOI: 10.1007/s00261-022-03592-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To investigate the diagnostic value of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CsPCa). We also aimed to combine PI-RADS v2.1 with prostate-specific antigen (PSA) derivatives to improve the predictive value of CsPCa. METHODS We retrospectively collected relevant data who underwent standard MRI examinations of the prostate and subjected to a prostate biopsy at Shenzhen People's hospital from November 2014 to November 2019. Included 125 cases of CsPCa and 383 cases of non-CsPCa. All cases were scored using the PI-RADS v2.1. The clinical data collected included age, PSA, free PSA/total PSA, prostate volume and PSA density (PSAD). A univariate analysis was performed to identify statistically significant indicators. Logistic regression was used to analyze the predictive value of the multi-parameter combination on CsPCa. RESULTS Except age, the difference in all of indicators between the CsPCa group and non-CsPCa group was statistically significant. The PI-RADS score and PSAD value had the highest diagnostic value. Logistic regression analysis revealed that the PI-RADS score and PSAD value were independent predictors of CsPCa, with a regression model AUC of 0.935. CsPCa detection rates were low when the PI-RADS score ≤ 2 or the PI-RADS score = 3 and the PSAD value ≤ 0.33 ng/ml/ml. CONCLUSION Combining the PI-RADS score and PSAD value improved the predictive performance of CsPCa. Patients with a PI-RADS score ≤ 2 or a PI-RADS score = 3 and a PSAD value ≤ 0.33 ng/ml/ml can avoid an unnecessary biopsy.
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Affiliation(s)
- Xiaoting Wei
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, NO.3025, Shennan Middle Road, Shenzhen, 518036, China
| | - Jianmin Xu
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Shuyuan Zhong
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Jinsen Zou
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Zhiqiang Cheng
- Department of Pathology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Zhiguang Ding
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, China
| | - Xuhui Zhou
- Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, NO.3025, Shennan Middle Road, Shenzhen, 518036, China.
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Zhou Z, Liang Z, Zuo Y, Zhou Y, Yan W, Wu X, Ji Z, Li H, Hu M, Ma L. Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region. Prostate 2022; 82:556-565. [PMID: 35098557 DOI: 10.1002/pros.24302] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/23/2021] [Accepted: 12/30/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted. PATIENTS AND METHODS A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ2 test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions. RESULTS A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones. CONCLUSION We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.
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Affiliation(s)
- Zhien Zhou
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhen Liang
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuzhi Zuo
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yi Zhou
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xingcheng Wu
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Zhigang Ji
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Hanzhong Li
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Mengyao Hu
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Lin Ma
- Department of Urology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
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Ferro M, de Cobelli O, Musi G, del Giudice F, Carrieri G, Busetto GM, Falagario UG, Sciarra A, Maggi M, Crocetto F, Barone B, Caputo VF, Marchioni M, Lucarelli G, Imbimbo C, Mistretta FA, Luzzago S, Vartolomei MD, Cormio L, Autorino R, Tătaru OS. Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol 2022; 14:17562872221109020. [PMID: 35814914 PMCID: PMC9260602 DOI: 10.1177/17562872221109020] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 05/30/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy, via Ripamonti 435 Milano, Italy
| | - Ottavio de Cobelli
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Gennaro Musi
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy; Department of Oncology and Hematology-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco del Giudice
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Carrieri
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, Foggia, Italy
| | | | - Alessandro Sciarra
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Martina Maggi
- Department of Urology, Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Biagio Barone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Vincenzo Francesco Caputo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio, University of Chieti, Chieti, Italy; Urology Unit, ‘SS. Annunziata’ Hospital, Chieti, Italy
- Department of Urology, ASL Abruzzo 2, Chieti, Italy
| | - Giuseppe Lucarelli
- Department of Emergency and Organ Transplantation, Urology, Andrology and Kidney Transplantation Unit, University of Bari, Bari, Italy
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples ‘Federico II’, Naples, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Stefano Luzzago
- Department of Urology, European Institute of Oncology, IRCCS, Milan, Italy
- Università degli Studi di Milano, Milan, Italy
| | - Mihai Dorin Vartolomei
- Department of Cell and Molecular Biology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
- Department of Urology, Medical University of Vienna, Vienna, Austria
| | - Luigi Cormio
- Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
- Urology Unit, Bonomo Teaching Hospital, Foggia, Italy
| | | | - Octavian Sabin Tătaru
- Institution Organizing University Doctoral Studies, I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mures, Târgu Mures, Romania
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Girometti R, Giannarini G, Panebianco V, Maresca S, Cereser L, De Martino M, Pizzolitto S, Pecoraro M, Ficarra V, Zuiani C, Valotto C. Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI. Br J Radiol 2021; 95:20210886. [PMID: 34762506 PMCID: PMC8978227 DOI: 10.1259/bjr.20210886] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To compare the effect of different PSA density (PSAD) thresholds on the accuracy for clinically significant prostate cancer (csPCa) of the Prostate Imaging Reporting And Data System v.2.1 (PI-RADSv2.1). METHODS We retrospectively included 123 biopsy-naïve men who underwent multiparametric magnetic resonance imaging (mpMRI) and transperineal mpMRI-targeted and systematic prostate biopsy between April 2019 and October 2020. mpMRI, obtained on a 3.0T magnet with a PI-RADSv2.1-compliant protocol, was read by two radiologists (>1500/>500 mpMRI examinations). csPCa was defined as International Society of Urogenital Pathology grading group ≥2. Receiver operating characteristic analysis was used to calculate per-index lesion sensitivity, specificity, and area under the curve (AUC) of PI-RADSv.2.1 categories after adjusting for PSAD ≥0.10,≥0.15, and ≥0.20 ng/mL ml-1. Per-adjusted category cancer detection rate (CDR) was calculated, and decision analysis performed to compare PSAD-adjusted PI-RADSv.2.1 categories as a biopsy trigger. RESULTS csPCa prevalence was 43.9%. PSAD-adjustment increased the CDR of PI-RADSv2.1 category 4. Sensitivity/specificity/AUC were 92.6%/53.6%/0.82 for unadjusted PI-RADS, and 85.2%/72.4%/0.84, 62.9%/85.5%/0.83, and 92.4%/53.6%/0.82 when adjusting PI-RADS categories for a 0.10, 0.15, and 0.20 ng/ml ml-1 PSAD threshold, respectively. Triggering biopsy for PI-RADS four lesions and PSAD ≥0.10 ng/mL ml-1 was the strategy with greatest net benefit at 30 and 40% risk probability (0.307 and 0.271, respectively). CONCLUSIONS PI-RADSv2.1 category four with PSAD ≥0.10 ng/mL ml-1 was the biopsy-triggering cut-off with the highest net benefit in the range of expected prevalence for csPCa. ADVANCES IN KNOWLEDGE 0.10 ng/mL ml-1 is the PSAD threshold with higher clinical utility in stratifying the risk for prostate cancer of PI-RADSv.2.1 categories.
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Affiliation(s)
- Rossano Girometti
- Department of Medicine, Institute of Radiology, University of Udine, Santa Maria dellaMisericordia University Hospital, Udine, Italy
| | - Gianluca Giannarini
- Urology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Silvio Maresca
- Department of Medicine, Institute of Radiology, University of Udine, Santa Maria dellaMisericordia University Hospital, Udine, Italy
| | - Lorenzo Cereser
- Department of Medicine, Institute of Radiology, University of Udine, Santa Maria dellaMisericordia University Hospital, Udine, Italy
| | - Maria De Martino
- Division of Medical Statistic, Department of Medicine, University of Udine, Udine, Italy
| | - Stefano Pizzolitto
- Pathology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy
| | - Vincenzo Ficarra
- Department of Human and Paediatric Pathology "Gaetano Barresi", Urologic Section,University of Messina, Messina, Italy
| | - Chiara Zuiani
- Department of Medicine, Institute of Radiology, University of Udine, Santa Maria dellaMisericordia University Hospital, Udine, Italy
| | - Claudio Valotto
- Urology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy
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Scialpi M, Scialpi P, Martorana E, Torre R, Improta A, Aisa MC, D’Andrea A, Di Blasi A. Simplified PI-RADS (S-PI-RADS) for biparametric MRI to detect and manage prostate cancer: What urologists need to know. Turk J Urol 2021; 47:175-182. [PMID: 35929870 PMCID: PMC8260088 DOI: 10.5152/tud.2021.21004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 04/09/2021] [Indexed: 09/14/2023]
Abstract
Biparametric magnetic resonance imaging (bpMRI) of the prostate has emerged as an alternative to multiparametric MRI (mpMRI) for the detection of clinically significant prostate cancer (csPCa). However, while the Prostate Imaging Reporting and Data System (PI-RADS) is widely known for mpMRI, a proper PI-RADS for bpMRI has not yet been adopted. In this review, we report the current status and the future directions of bpMRI, and propose a simplified PI-RADS (S-PI-RADS) that could help radiologists and urologists in the detection and management of PCa.
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Affiliation(s)
- Michele Scialpi
- Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | - Pietro Scialpi
- Division of Urology, Portogruaro Hospital, Venice, Italy
| | | | - Riccardo Torre
- Division of Radiology, Ospedale Santa Maria, Terni, Italy
| | - Antonio Improta
- Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | - Maria Cristina Aisa
- Division of Obstetrics and Gynaecology, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | | | - Aldo Di Blasi
- Division of Radiology, Tivoli Hospital, Tivoli, Italy
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Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers (Basel) 2021; 13:cancers13030552. [PMID: 33535569 PMCID: PMC7867056 DOI: 10.3390/cancers13030552] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted. Abstract The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.
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