Ileana Montoya Perez1,2,3, Ivan Jambor1,3,4, Tommi Kauko5, Janne Verho1,3, Otto Ettala6, Ugo Falagario7,8, Harri Merisaari1,2,3, Aida Kiviniemi1,3, Pekka Taimen9, Kari T Syvänen6, Juha Knaapila6, Marjo Seppänen10, Antti Rannikko11, Jarno Riikonen12, Markku Kallajoki9, Tuomas Mirtti13, Tarja Lamminen6, Jani Saunavaara14, Tapio Pahikkala2, Peter J Boström6, Hannu J Aronen1,3. 1. Department of Diagnostic Radiology, University of Turku, Turku, Finland. 2. Department of Future Technologies, University of Turku, Turku, Finland. 3. Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland. 4. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 5. Auria Clinical Informatics, Turku University Hospital, Turku, Finland. 6. Department of Urology, University of Turku and Turku University Hospital, Turku, Finland. 7. Department of Urology, University of Foggia, Foggia, Italy. 8. Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 9. Institute of Biomedicine, University of Turku and Department of Pathology, Turku University Hospital, Turku, Finland. 10. Department of Surgery, Satakunta Central Hospital, Pori, Finland. 11. Department of Urology, Helsinki University and Helsinki University Hospital, Helsinki, Finland. 12. Department of Urology, Tampere University Hospital and University of Tampere, Tampere, Finland. 13. Department of Pathology, University of Helsinki, Helsinki, Finland. 14. Department of Medical Physics, Turku University Hospital, Turku, Finland.
Abstract
BACKGROUND: Multiparametric MRI of the prostate has been shown to improve the risk stratification of men with an elevated prostate-specific antigen (PSA). However, long acquisition time, high cost, and inter-center/reader variability of a routine prostate multiparametric MRI limit its wider adoption. PURPOSE: To develop and validate nomograms based on unique rapid biparametric MRI (bpMRI) qualitative and quantitative derived variables for prediction of clinically significant cancer (SPCa). STUDY TYPE: Retrospective analyses of single (IMPROD, NCT01864135) and multiinstitution trials (MULTI-IMPROD, NCT02241122). POPULATION: 161 and 338 prospectively enrolled men who completed the IMPROD and MULTI-IMPROD trials, respectively. FIELD STRENGTH/SEQUENCE: IMPROD bpMRI: 3T/1.5T, T2 -weighted imaging, three separate diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b values 0, 1500 s/mm2 ; 3) values 0, 2000 s/mm2 . ASSESSMENT: The primary endpoint of the combined trial analysis was the diagnostic accuracy of the combination of IMPROD bpMRI and clinical variables for detection of SPCa. STATISTICAL TESTS: Logistic regression models were developed using IMPROD trial data and validated using MULTI-IMPROD trial data. The model's performance was expressed as the area under the curve (AUC) values for the detection of SPCa, defined as ISUP Gleason Grade Group ≥2. RESULTS: A model incorporating clinical variables had an AUC (95% confidence interval) of 0.83 (0.77-0.89) and 0.80 (0.75-0.85) in the development and validation cohorts, respectively. The corresponding values for a model using IMPROD bpMRI findings were 0.93 (0.89-0.97), and 0.88 (0.84-0.92), respectively. Further addition of the quantitative DWI-based score did not improve AUC values (P < 0.05). DATA CONCLUSION: A prediction model using qualitative IMPROD bpMRI findings demonstrated high accuracy for predicting SPCa in men with an elevated PSA. Online risk calculator: http://petiv.utu.fi/multiimprod/ Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1556-1567.
BACKGROUND: Multiparametric MRI of the prostate has been shown to improve the risk stratification of men with an elevated prostate-specific antigen (PSA). However, long acquisition time, high cost, and inter-center/reader variability of a routine prostate multiparametric MRI limit its wider adoption. PURPOSE: To develop and validate nomograms based on unique rapid biparametric MRI (bpMRI) qualitative and quantitative derived variables for prediction of clinically significant cancer (SPCa). STUDY TYPE: Retrospective analyses of single (IMPROD, NCT01864135) and multiinstitution trials (MULTI-IMPROD, NCT02241122). POPULATION: 161 and 338 prospectively enrolled men who completed the IMPROD and MULTI-IMPROD trials, respectively. FIELD STRENGTH/SEQUENCE: IMPROD bpMRI: 3T/1.5T, T2 -weighted imaging, three separate diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b values 0, 1500 s/mm2 ; 3) values 0, 2000 s/mm2 . ASSESSMENT: The primary endpoint of the combined trial analysis was the diagnostic accuracy of the combination of IMPROD bpMRI and clinical variables for detection of SPCa. STATISTICAL TESTS: Logistic regression models were developed using IMPROD trial data and validated using MULTI-IMPROD trial data. The model's performance was expressed as the area under the curve (AUC) values for the detection of SPCa, defined as ISUP Gleason Grade Group ≥2. RESULTS: A model incorporating clinical variables had an AUC (95% confidence interval) of 0.83 (0.77-0.89) and 0.80 (0.75-0.85) in the development and validation cohorts, respectively. The corresponding values for a model using IMPROD bpMRI findings were 0.93 (0.89-0.97), and 0.88 (0.84-0.92), respectively. Further addition of the quantitative DWI-based score did not improve AUC values (P < 0.05). DATA CONCLUSION: A prediction model using qualitative IMPROD bpMRI findings demonstrated high accuracy for predicting SPCa in men with an elevated PSA. Online risk calculator: http://petiv.utu.fi/multiimprod/ Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1556-1567.
Authors: Ugo Giovanni Falagario; Oscar Selvaggio; Francesca Sanguedolce; Paola Milillo; Maria Chiara Sighinolfi; Salvatore Mariano Bruno; Marco Recchia; Carlo Bettocchi; Gian Maria Busetto; Luca Macarini; Bernardo Rocco; Luigi Cormio; Giuseppe Carrieri Journal: Diagnostics (Basel) Date: 2022-01-21
Authors: Ugo Giovanni Falagario; Giovanni Silecchia; Salvatore Mariano Bruno; Michele Di Nauta; Mario Auciello; Francesca Sanguedolce; Paola Milillo; Luca Macarini; Oscar Selvaggio; Giuseppe Carrieri; Luigi Cormio Journal: Front Oncol Date: 2021-01-08 Impact factor: 6.244
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