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Huang TB, Ding XF. Re: Matthias Boschheidgen, Peter Albers, Heinz-Peter Schlemmer, et al. Multiparametric Magnetic Resonance Imaging in Prostate Cancer Screening at the Age of 45 Years: Results from the First Screening Round of the PROBASE Trial, Eur Urol 2024:85:105-111. Eur Urol 2024; 85:e112. [PMID: 38105141 DOI: 10.1016/j.eururo.2023.11.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 11/09/2023] [Indexed: 12/19/2023]
Affiliation(s)
- Tian-Bao Huang
- Department of Urology, Northern Jiangsu People's Hospital, Affiliated Hospital of Nanjing University Medical School, Yangzhou, China
| | - Xue-Fei Ding
- Department of Urology, Northern Jiangsu People's Hospital, Affiliated Hospital of Nanjing University Medical School, Yangzhou, China.
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Padhani AR, Godtman RA, Schoots IG. Key learning on the promise and limitations of MRI in prostate cancer screening. Eur Radiol 2024:10.1007/s00330-024-10626-6. [PMID: 38311703 DOI: 10.1007/s00330-024-10626-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/14/2024] [Accepted: 01/20/2024] [Indexed: 02/06/2024]
Abstract
MRI retains its ability to reduce the harm of prostate biopsies by decreasing biopsy rates and the detection of indolent cancers in population-based screening studies aiming to find clinically significant prostate cancers. Limitations of low positive predictive values and high reader variability in diagnostic performance require optimisations in patient selection, imaging protocols, interpretation standards, diagnostic thresholds, and biopsy methods. Improvements in diagnostic accuracy could come about through emerging technologies like risk calculators and polygenic risk scores to select men for MRI. Furthermore, artificial intelligence and workflow optimisations focused on streamlining the diagnostic pathway, quality control, and assurance measures will improve MRI variability. CLINICAL RELEVANCE STATEMENT: MRI significantly reduces harm in prostate cancer screening, lowering unnecessary biopsies and minimizing the overdiagnosis of indolent cancers. MRI maintains the effective detection of high-grade cancers, thus improving the overall benefit-to-harm ratio in population-based screenings with or without using serum prostate-specific antigen (PSA) for patient selection. KEY POINTS: • The use of MRI enables the harm reduction benefits seen in individual early cancer detection to be extended to both risk-stratified and non-stratified prostate cancer screening populations. • MRI limitations include a low positive predictive value and imperfect reader variability, which require standardising interpretations, biopsy methods, and integration into a quality diagnostic pathway. • Current evidence is based on one-time point use of MRI in screening; MRI effectiveness in multiple rounds of screening is not well-documented.
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Affiliation(s)
- Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Rickmansworth Road, Northwood, Middlesex, HA6 2RN, UK.
| | - Rebecka A Godtman
- Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy at Goteborg University, Goteborg, Sweden
| | - Ivo G Schoots
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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Boschheidgen M, Albers P, Schlemmer HP, Hellms S, Bonekamp D, Sauter A, Hadaschik B, Krilaviciute A, Radtke JP, Seibold P, Lakes J, Arsov C, Gschwend JE, Herkommer K, Makowski M, Kuczyk MA, Wacker F, Harke N, Debus J, Körber SA, Benner A, Kristiansen G, Giesel FL, Antoch G, Kaaks R, Becker N, Schimmöller L. Multiparametric Magnetic Resonance Imaging in Prostate Cancer Screening at the Age of 45 Years: Results from the First Screening Round of the PROBASE Trial. Eur Urol 2024; 85:105-111. [PMID: 37863727 DOI: 10.1016/j.eururo.2023.09.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/05/2023] [Accepted: 09/28/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) has been suggested as a tool for guiding biopsy recommendations in prostate cancer (PC) screening. OBJECTIVE To determine the performance of multiparametric MRI (mpMRI) in young men at age 45 yr who participated in a PC screening trial (PROBASE) on the basis of baseline prostate-specific antigen (PSA). DESIGN, SETTING, AND PARTICIPANTS Participants with confirmed PSA ≥3 ng/ml were offered mpMRI followed by MRI/transrectal ultrasound fusion biopsy (FBx) with targeted and systematic cores. mpMRI scans from the first screening round for men randomised to an immediate PSA test in PROBASE were evaluated by local readers and then by two reference radiologists (experience >10 000 prostate MRI examinations) blinded to the histopathology. The PROBASE trial is registered as ISRCTN37591328 OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The local and reference Prostate Imaging-Data and Reporting System (PI-RADS) scores were compared, and the sensitivity, negative predictive value (NPV), and accuracy were calculated for both readings for different cutoffs (PI-RADS 3 vs 4). RESULTS AND LIMITATIONS Of 186 participants, 114 underwent mpMRI and FBx. PC was detected in 47 (41%), of whom 33 (29%) had clinically significant PC (csPC; International Society of Urological Pathology grade group ≥2). Interobserver reliability between local and reference PI-RADS scores was moderate (k = 0.41). At a cutoff of PI-RADS 4, reference reading showed better performance for csPC detection (sensitivity 79%, NPV 91%, accuracy of 85%) than local reading (sensitivity 55%, NPV 80%, accuracy 68%). Reference reading did not miss any PC cases for a cutoff of PI-RADS <3. If PI-RADS ≥4 were to be used as a biopsy cutoff, mpMRI would reduce negative biopsies by 68% and avoid detection of nonsignificant PC in 71% of cases. CONCLUSIONS Prostate MRI in a young screening population is difficult to read. The MRI accuracy of for csPC detection is highly dependent on reader experience, and double reading might be advisable. More data are needed before MRI is included in PC screening for men at age 45 yr. PATIENT SUMMARY Measurement of prostate specific antigen (PSA) is an effective screening test for early detection of prostate cancer (PC) and can reduce PC-specific deaths, but it can also lead to unnecessary biopsies and treatment. Magnetic resonance imaging (MRI) after a positive PSA test has been proposed as a way to reduce the number of biopsies, with biopsy only recommended for men with suspicious MRI findings. Our results indicate that MRI accuracy is moderate for men aged 45 years but can be increased by a second reading of the images by expert radiologists. For broad application of MRI in routine screening, double reading may be advisable.
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Affiliation(s)
- Matthias Boschheidgen
- Dusseldorf University, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany
| | - Peter Albers
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany; Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Susanne Hellms
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Sauter
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Boris Hadaschik
- Department of Urology, University of Duisburg-Essen and German Cancer Consortium (dktk), University Hospital Essen, Essen, Germany
| | - Agne Krilaviciute
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Philipp Radtke
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany
| | - Petra Seibold
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jale Lakes
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany
| | - Christian Arsov
- University Dusseldorf, Medical Faculty, Department of Urology, D-40225 Dusseldorf, Germany; Department of Urology and Paediatric Urology, Elisabeth-Krankenhaus Rheydt, Städtische Kliniken Mönchengladbach GmbH, Mönchengladbach, Germany
| | - Jürgen E Gschwend
- Department of Urology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Kathleen Herkommer
- Department of Urology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Marcus Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Markus A Kuczyk
- Department of Urology, Medical University Hannover, Hannover, Germany
| | - Frank Wacker
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Nina Harke
- Department of Urology, Medical University Hannover, Hannover, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany
| | - Stefan A Körber
- Department of Radiation Oncology, Heidelberg University Hospital, Ruprecht Karls University, Heidelberg, Germany
| | - Axel Benner
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Frederik L Giesel
- University Dusseldorf, Medical Faculty, Department of Nuclear Medicine, D-40225 Dusseldorf, Germany
| | - Gerald Antoch
- Dusseldorf University, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany; Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf, Düsseldorf (CIO ABCD), Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nikolaus Becker
- Division of Personalized Early Detection of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lars Schimmöller
- Dusseldorf University, Medical Faculty, Department of Diagnostic and Interventional Radiology, D-40225 Dusseldorf, Germany; Department of Diagnostic, Interventional Radiology and Nuclear Medicine, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.
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Cheng X, Chen Y, Xu J, Cai D, Liu Z, Zeng H, Yao J, Song B. Development and validation of a predictive model based on clinical and MpMRI findings to reduce additional systematic prostate biopsy. Insights Imaging 2024; 15:3. [PMID: 38185753 PMCID: PMC10772021 DOI: 10.1186/s13244-023-01544-0] [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: 05/03/2023] [Accepted: 10/21/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES To develop and validate a predictive model based on clinical features and multiparametric magnetic resonance imaging (mpMRI) to reduce unnecessary systematic biopsies (SBs) in biopsy-naïve patients with suspected prostate cancer (PCa). METHODS A total of 274 patients who underwent combined cognitive MRI-targeted biopsy (MRTB) with SB were retrospectively enrolled and temporally split into development (n = 201) and validation (n = 73) cohorts. Multivariable logistic regression analyses were used to determine independent predictors of clinically significant PCa (csPCa) on cognitive MRTB, and the clinical, MRI, and combined models were established respectively. Area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses were assessed. RESULTS Prostate imaging data and reporting system (PI-RADS) score, index lesion (IL) on the peripheral zone, age, and prostate-specific antigen density (PSAD) were independent predictors and included in the combined model. The combined model achieved the best discrimination (AUC 0.88) as compared to both the MRI model incorporated by PI-RADS score, IL level, and zone (AUC 0.86) and the clinical model incorporated by age and PSAD (AUC 0.70). The combined model also showed good calibration and enabled great net benefit. Applying the combined model as a reference for performing MRTB alone with a cutoff of 60% would reduce 43.8% of additional SB, while missing 2.9% csPCa. CONCLUSIONS The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients. CRITICAL RELEVANCE STATEMENT The combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naïve patients. KEY POINTS • Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa. • Risk models were used to predict the probability of detecting csPCa on cognitive MRTB. • The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.
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Affiliation(s)
- Xueqing Cheng
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, Sichuan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, Sichuan, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Jinshun Xu
- Department of Ultrasound, Sichuan Cancer Hospital, Chengdu, Sichuan, China
| | - Diming Cai
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Zhenhua Liu
- Department of Urology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hao Zeng
- Department of Urology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Jin Yao
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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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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Qi X, Wang K, Feng B, Sun X, Yang J, Hu Z, Zhang M, Lv C, Jin L, Zhou L, Wang Z, Yao J. Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer. Front Oncol 2023; 13:1157949. [PMID: 37260984 PMCID: PMC10227569 DOI: 10.3389/fonc.2023.1157949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 05/04/2023] [Indexed: 06/02/2023] Open
Abstract
Objective To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance. Materials and methods We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set. B-Ultrasound videos (BUS), mpMRI T2 sequence (T2), and ADC sequence (ADC) were obtained from all patients. We extracted 851 features of each patient in the BUS, T2, and ADC groups and used a t-test, the Mann-Whitney U test, and LASSO regression to screen the features. Support vector machine (SVM), random forest (RF), adaptive boosting (ADB), and gradient boosting machine (GBM) models were used to establish radiomics models. In addition, we fused the features screened via LASSO regression from three groups as new features and rebuilt ML models. The performance of the ML models in diagnosing PCa in the BUS, T2, ADC, and fusion groups was compared using the area under the ROC curve (AUC), sensitivity, specificity, and accuracy. Results In the test cohort, the AUC of each model in the ADC group was higher than that of in the.BUS and T2 groups. Among the models, the RF model had the best diagnostic performance, with an AUC of 0.85, sensitivity of 0.78 (0.61-0.89), specificity of 0.84 (0.69-0.94), and accuracy of 0.83 (0.66-0.93). The SVM model in both the BUS and T2 groups performed best. Based on the features screened in the BUS, T2, and ADC groups fused to construct the models, the SVM model was found to perform best, with an AUC of 0.87, sensitivity of 0.73 (0.56-0.86), specificity of 0.79 (0.63-0.90), and accuracy of 0.77 (0.59-0.89). The difference in the results was statistically significant (p<0.05). Conclusion The ML prediction models had a good diagnostic ability for PCa. Among them, the SVM model in the fusion group showed the best performance in diagnosing PCa. These prediction models can help radiologists make better diagnoses.
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Affiliation(s)
- Xiaoyang Qi
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Bojian Feng
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Xingbo Sun
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jie Yang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Cheng Lv
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Liyuan Jin
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Lingyan Zhou
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
| | - Zhengping Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China
| | - Jincao Yao
- Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, Zhejiang, China
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Li L, Gu L, Kang B, Yang J, Wu Y, Liu H, Lai S, Wu X, Jiang J. Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions. Front Oncol 2022; 12:934108. [PMID: 35865467 PMCID: PMC9295912 DOI: 10.3389/fonc.2022.934108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/07/2022] [Indexed: 12/24/2022] Open
Abstract
ObjectiveTo compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.MethodsA total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.ResultsIn the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.ConclusionThe results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.
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Affiliation(s)
- Linghao Li
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Lili Gu
- Department of Pain, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Bin Kang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jiaojiao Yang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Ying Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Hao Liu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Shasha Lai
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Xueting Wu
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
| | - Jian Jiang
- Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang, China
- *Correspondence: Jian Jiang,
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Majchrzak N, Cieśliński P, Milecki T, Twardosz K, Głyda M, Karmelita-Katulska K. Analysis of the usefulness of magnetic resonance imaging and clinical parameters in the detection of prostate cancer in the first systematic biopsy combined with targeted cognitive biopsy. Cent European J Urol 2021; 74:321-326. [PMID: 34729220 PMCID: PMC8552931 DOI: 10.5173/ceju.2021.3.r2.0111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/06/2021] [Accepted: 06/09/2021] [Indexed: 11/22/2022] Open
Abstract
Introduction The study aimed to assess the suitability of multiparametric magnetic resonance prostate imaging (mpMRI) in combination with clinical parameters [prostate-specific antigen (PSA), digital rectal examination (DRE)] in the identification of men at risk of the presence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa, Gleason Score ≥3+4) in the cognitive fusion with systematic prostate biopsy. Material and methods We retrospectively evaluated a population of 215 biopsy – naive patients with a clinical suspicion of prostate cancer. The results of mpMRI, DRE, PSA and biopsy were analyzed. MpMRI of the prostate according to the Prostate Imaging Reporting and Data System (PI-RADS) v.2.0 scheme preceded cognitive fusion and systematic transrectal prostate biopsy. Uni- and multivariable logistic regression analysis (MVA) was used to identify the variables determining the risk of detecting PCa overall and csPCa. Results In MVA, it was established that the combination of variables such as PSA level [odds ratio (OR) 1.195; p = 0.002], PI-RADS ≥3 (OR 7.7; p = 0.002), prostate volume (OR 0.98; p = 0.017) significantly determines the probability of PCa detection in biopsy, while for csPCa it is PSA level (OR 1.14; p = 0.004), DRE (+) (OR 5.75; p <0.001), PI-RADS ≥4 (OR 6.5; p <0.001). Analysis of mpMRI diagnostic value for PI-RADS ≥4 revealed better sensitivity (88.9% vs 82.6%) and better negative predictive value (NPV) (94.5% vs 82.4%) for detection of csPCa than for PCa overall. Conclusions MpMRI results combining with DRE and PSA parameters help to identify men at high – or low risk of csPCa detection in the first – time biopsy.
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Affiliation(s)
- Natalia Majchrzak
- Transplantology, General Surgery and Urology Department, Poznań District Hospital, Poznań, Poland
| | - Piotr Cieśliński
- Transplantology, General Surgery and Urology Department, Poznań District Hospital, Poznań, Poland
| | - Tomasz Milecki
- Department and Clinic of Urology and Oncological Urology, Poznań University of Medical Sciences, Poznań, Poland
| | - Krzysztof Twardosz
- Transplantology, General Surgery and Urology Department, Poznań District Hospital, Poznań, Poland
| | - Maciej Głyda
- Transplantology, General Surgery and Urology Department, Poznań District Hospital, Poznań, Poland.,Hepatobiliary and General Surgery Department, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, Bydgoszcz, Poland
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9
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Mazzone E, Gandaglia G, Ploussard G, Marra G, Valerio M, Campi R, Mari A, Minervini A, Serni S, Moschini M, Marquis A, Beauval JB, van den Bergh R, Rahota RG, Soeterik T, Roumiguiè M, Afferi L, Zhuang J, Tuo H, Mattei A, Gontero P, Cucchiara V, Stabile A, Fossati N, Montorsi F, Briganti A. Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups. Eur Urol 2021; 81:193-203. [PMID: 34399996 DOI: 10.1016/j.eururo.2021.07.027] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 07/29/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone. OBJECTIVE We aimed at developing and validating a novel classification integrating clinical and radiological parameters. DESIGN, SETTING, AND PARTICIPANTS A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel's concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR. RESULTS AND LIMITATIONS Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively). CONCLUSIONS We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools. PATIENT SUMMARY Our novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.
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Affiliation(s)
- Elio Mazzone
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Giorgio Gandaglia
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Guillame Ploussard
- La Croix du Sud Hospital, Quint Fonsegrives, France; Institut Universitaire du Cancer-Toulouse, Oncopole, Toulouse, France
| | - Giancarlo Marra
- Department of Urology, Città della Salute e della Scienza, University of Turin, Turin, Italy
| | - Massimo Valerio
- Urology Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Riccardo Campi
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Andrea Mari
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Andrea Minervini
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Sergio Serni
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy; Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Marco Moschini
- Klinik Für Urologie, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Alessandro Marquis
- Department of Urology, Città della Salute e della Scienza, University of Turin, Turin, Italy
| | - Jean Baptiste Beauval
- Department of Urology and Renal Transplantation, Toulouse University Hospital, Toulouse, France
| | | | - Razvan-George Rahota
- La Croix du Sud Hospital, Quint Fonsegrives, France; Institut Universitaire du Cancer-Toulouse, Oncopole, Toulouse, France
| | - Timo Soeterik
- Department of Urology, University Medical Centre Utrecht, Utrecht, The Netherlands; Department of Urology, St. Antonius Hospital, Santeon-group, The Netherlands
| | - Mathieu Roumiguiè
- Department of Urology and Renal Transplantation, Toulouse University Hospital, Toulouse, France
| | - Luca Afferi
- Klinik Für Urologie, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Junlong Zhuang
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, Jiangsu, People's Republic of China
| | - Hongqian Tuo
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, Jiangsu, People's Republic of China
| | - Agostino Mattei
- Klinik Für Urologie, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Paolo Gontero
- Department of Urology, Città della Salute e della Scienza, University of Turin, Turin, Italy
| | - Vito Cucchiara
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Armando Stabile
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Nicola Fossati
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Francesco Montorsi
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Alberto Briganti
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy
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10
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Bura V, Caglic I, Snoj Z, Sushentsev N, Berghe AS, Priest AN, Barrett T. MRI features of the normal prostatic peripheral zone: the relationship between age and signal heterogeneity on T2WI, DWI, and DCE sequences. Eur Radiol 2021; 31:4908-4917. [PMID: 33398421 PMCID: PMC8213603 DOI: 10.1007/s00330-020-07545-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/30/2020] [Accepted: 11/18/2020] [Indexed: 12/24/2022]
Abstract
Objectives To assess the multiparametric MRI (mpMRI) appearances of normal peripheral zone (PZ) across age groups in a biopsy-naïve population, where prostate cancer (PCa) was subsequently excluded, and propose a scoring system for background PZ changes. Methods This retrospective study included 175 consecutive biopsy-naïve patients (40–74 years) referred with a suspicion of PCa, but with subsequent negative investigations. Patients were grouped by age into categories ≤ 54, 55–59, 60–64, and ≥ 65 years. MpMRI sequences (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC], and dynamic contrast-enhanced imaging [DCE]) were independently evaluated by two uro-radiologists on a proposed 4-point grading scale for background change on each sequence, wherein score 1 mirrored PIRADS-1 change and score 4 represented diffuse background change. Peripheral zone T2WI signal intensity and ADC values were also analyzed for trends relating to age. Results There was a negative correlation between age and assigned background PZ scores for each mpMRI sequence: T2WI: r = − 0.52, DWI: r = − 0.49, DCE: r = − 0.45, p < 0.001. Patients aged ≤ 54 years had mean scores of 3.0 (T2WI), 2.7 (DWI), and 3.1 (DCE), whilst patients ≥ 65 years had significantly lower mean scores of 1.7, 1.4, and 1.9, respectively. There was moderate inter-reader agreement for all scores (range κ = 0.43–0.58). Statistically significant positive correlations were found for age versus normalized T2WI signal intensity (r = 0.2, p = 0.009) and age versus ADC values (r = 0.33, p = 0.001). Conclusion The normal PZ in younger patients (≤ 54 years) demonstrates significantly lower T2WI signal intensity, lower ADC values, and diffuse enhancement on DCE, which may hinder diagnostic interpretation in these patients. The proposed standardized PZ background scoring system may help convey the potential for diagnostic uncertainty to clinicians. Key Points • Significant, positive correlations were found between increasing age and higher normalized T2-weighted signal intensity and mean ADC values of the prostatic peripheral zone. • Younger men exhibit lower T2-weighted imaging signal intensity, lower ADC values, and diffuse enhancement on dynamic contrast-enhanced imaging, which may hinder MRI interpretation. • A scoring system is proposed which aims towards a standardized assessment of the normal background PZ. This may help convey the potential for diagnostic uncertainty to clinicians. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07545-7.
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Affiliation(s)
- Vlad Bura
- Department of Radiology, County Clinical Emergency Hospital, Cluj-Napoca, Cluj, Romania
| | - Iztok Caglic
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Box 218, Hills Road, Cambridge, CB2 0QQ, UK
| | - Ziga Snoj
- Radiology Institute, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Box 218, Hills Road, Cambridge, CB2 0QQ, UK
| | - Alexandra S Berghe
- Department of Radiology, County Clinical Emergency Hospital, Cluj-Napoca, Cluj, Romania.,Department of Medical Informatics and Biostatistics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Andrew N Priest
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Box 218, Hills Road, Cambridge, CB2 0QQ, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Box 218, Hills Road, Cambridge, CB2 0QQ, UK.
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11
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Mazzone E, Stabile A, Pellegrino F, Basile G, Cignoli D, Cirulli GO, Sorce G, Barletta F, Scuderi S, Bravi CA, Cucchiara V, Fossati N, Gandaglia G, Montorsi F, Briganti A. Positive Predictive Value of Prostate Imaging Reporting and Data System Version 2 for the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis. Eur Urol Oncol 2020; 4:697-713. [PMID: 33358543 DOI: 10.1016/j.euo.2020.12.004] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/26/2020] [Accepted: 12/08/2020] [Indexed: 11/18/2022]
Abstract
CONTEXT The variability of the positive predictive value (PPV) represents a significant factor affecting the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI). OBJECTIVE To analyze published studies reporting mpMRI PPV and the reasons behind the variability of clinically significant prostate cancer (csPCa) detection rates on targeted biopsies (TBx) according to Prostate Imaging Reporting and Data System (PI-RADS) version 2 categories. EVIDENCE ACQUISITION A search of PubMed, Cochrane library's Central, EMBASE, MEDLINE, and Scopus databases, from January 2015 to June 2020, was conducted. The primary and secondary outcomes were to evaluate the PPV of PI-RADS version 2 in detecting csPCa and any prostate cancer (PCa), respectively. Individual authors' definitions for csPCa and PI-RADS thresholds for positive mpMRI were accepted. Detection rates, used as a surrogate of PPV, were pooled using random-effect models. Preplanned subgroup analyses tested PPV after stratification for PI-RADS scores, previous biopsy status, TBx technique, and number of sampled cores. PPV variation over cancer prevalence was evaluated. EVIDENCE SYNTHESIS Fifty-six studies, with a total of 16 537 participants, were included in the quantitative synthesis. The PPV of suspicious mpMRI for csPCa was 40% (95% confidence interval 36-43%), with large heterogeneity between studies (I2 94%, p < 0.01). PPV increased according to PCa prevalence. In subgroup analyses, PPVs for csPCa were 13%, 40%, and 69% for, respectively, PI-RADS 3, 4, and 5 (p < 0.001). TBx missed 6%, 6%, and 5% of csPCa in PI-RADS 3, 4, and 5 lesions, respectively. In biopsy-naïve and prior negative biopsy groups, PPVs for csPCa were 42% and 32%, respectively (p = 0.005). Study design, TBx technique, and number of sampled cores did not affect PPV. CONCLUSIONS Our meta-analysis underlines that the PPV of mpMRI is strongly dependent on the disease prevalence, and that the main factors affecting PPV are PI-RADS version 2 scores and prior biopsy status. A substantially low PPV for PI-RADS 3 lesions was reported, while it was still suboptimal in PI-RADS 4 and 5 lesions. Lastly, even if the added value of a systematic biopsy for csPCa is relatively low, this rate can improve patient risk assessment and staging. PATIENT SUMMARY Targeted biopsy of Prostate Imaging Reporting and Data System 3 lesions should be considered carefully in light of additional individual risk assessment corroborating the presence of clinically significant prostate cancer. On the contrary, the positive predictive value of highly suspicious lesions is not high enough to omit systematic prostate sampling.
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Affiliation(s)
- Elio Mazzone
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy.
| | - Armando Stabile
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco Pellegrino
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Giuseppe Basile
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Daniele Cignoli
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Giuseppe Ottone Cirulli
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Gabriele Sorce
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco Barletta
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Simone Scuderi
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Carlo Andrea Bravi
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Vito Cucchiara
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Nicola Fossati
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Giorgio Gandaglia
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco Montorsi
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Alberto Briganti
- Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
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12
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Sun J, Yu G, Yang Y, Qiao L, Xu B, Ding C, Liu Y, Yu S. Evaluation of prostate cancer based on MALDI-TOF MS fingerprinting of nanoparticle-treated serum proteins/peptides. Talanta 2020; 220:121331. [PMID: 32928383 DOI: 10.1016/j.talanta.2020.121331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/18/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
The serum MALDI-TOF MS spectrum includes signals for serum proteins and peptides between 1000 and 12,000 Da in size, presenting a fingerprint-like pattern. However, whole serum MALDI-TOF MS signals are complex and prejudiced for data analysis. Pre-treatment with specific nanomaterials can simplify the mass spectrum while retaining the characteristics of the fingerprint pattern. In the present study, we used hydrophilic interaction chromatography nanoparticles (HICNPs) to enrich proteins and peptides in serum from a large number prostate cancer samples and controls. After pre-treatment with HICNPs, the serum MALDI-TOF MS signals for samples were simpler, with more analysable fingerprint-like patterns. Principal component analysis and partial least squares discriminant analysis of the samples demonstrated a significant difference in the MALDI-TOF signals between prostate cancer and controls, with an analytical accuracy of 77%, approaching that of methods based on prostate-specific antigen. Due to the low cost and high flux, MALDI-TOF MS fingerprinting can be used in large-scale evaluation of various cancers, including prostate cancer.
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Affiliation(s)
- Jiaojiao Sun
- Department of Chemistry, Fudan University, 2205 Songhu Rd., Shanghai, 200438, China; Zhejiang Provincial Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University Ningbo, 818 Fenghua Rd., Ningbo, Zhejiang, 315211, China
| | - Guopeng Yu
- Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, 639 Zhizhaoju Rd., Shanghai, 200011, China
| | - Yi Yang
- Department of Chemistry, Fudan University, 2205 Songhu Rd., Shanghai, 200438, China
| | - Liang Qiao
- Department of Chemistry, Fudan University, 2205 Songhu Rd., Shanghai, 200438, China
| | - Bin Xu
- Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, 639 Zhizhaoju Rd., Shanghai, 200011, China
| | - Chuanfan Ding
- Zhejiang Provincial Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University Ningbo, 818 Fenghua Rd., Ningbo, Zhejiang, 315211, China.
| | - Yushan Liu
- Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, 639 Zhizhaoju Rd., Shanghai, 200011, China.
| | - Shaoning Yu
- Department of Chemistry, Fudan University, 2205 Songhu Rd., Shanghai, 200438, China; Zhejiang Provincial Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University Ningbo, 818 Fenghua Rd., Ningbo, Zhejiang, 315211, China.
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