Ashkan Mortezavi1, Thorgerdur Palsdottir2, Martin Eklund2, Venkatesh Chellappa2, Sarath Kumar Murugan2, Karim Saba3, Donna P Ankerst4, Erik S Haug5, Tobias Nordström6. 1. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; University Hospital Zurich, Department of Urology, Zurich, Switzerland. Electronic address: Ashkan.Mortezavi@ki.se. 2. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 3. Department of Urology, Cantonal Hospital Grisons, Chur, Switzerland. 4. Department of Mathematics and Life Sciences, Technical University of Munich, Garching, Munich, Germany. 5. Section of Urology, Vestfold Hospital Trust, Tønsberg, Norway; Institute of Cancer Genomics and Informatics, Oslo University Hospital, Oslo, Norway. 6. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden.
Abstract
BACKGROUND: A new generation of risk calculators (RCs) for prostate cancer (PCa) incorporating magnetic resonance imaging (MRI) data have been introduced. However, these have not been validated externally, and their clinical benefit compared with alternative approaches remains unclear. OBJECTIVE: To assess previously published PCa RCs incorporating MRI data, and compare their performance with traditional RCs (European Randomized Study of Screening for Prostate Cancer [ERSPC] 3/4 and Prostate Biopsy Collaborative Group [PBCG]) and the blood-based Stockholm3 test. DESIGN, SETTING, AND PARTICIPANTS: RCs were tested in a prospective multicenter cohort including 532 men aged 45-74 yr participating in the Stockholm3-MRI study between 2016 and 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The probabilities of detection of clinically significant PCa (csPCa) defined as Gleason score ≥3 + 4 were calculated for each patient. For each RC and the Stockholm3 test, discrimination was assessed by area under the curve (AUC), calibration by numerical and graphical summaries, and clinical usefulness by decision curve analysis (DCA). RESULTS AND LIMITATIONS: The discriminative ability of MRI RCs 1-4 for the detection of csPCa was superior (AUC 0.81-0.87) to the traditional RCs (AUC 0.76-0.80). The observed prevalence of csPCa in the cohort was 37%, but calibration-in-the-large predictions varied from 14% to 63% across models. DCA identified only one model including MRI data as clinically useful at a threshold probability of 10%. The Stockholm3 test achieved equivalent performance for discrimination (AUC 0.86) and DCA, but was underpredicting the actual risk. CONCLUSIONS: Although MRI RCs discriminated csPCa better than traditional RCs, their predicted probabilities were variable in accuracy, and DCA identified only one model as clinically useful. PATIENT SUMMARY: Novel risk calculators (RCs) incorporating imaging improved the ability to discriminate clinically significant prostate cancer compared with traditional tools. However, all but one predicted divergent compared with actual risks, suggesting that regional modifications be implemented before usage. The Stockholm3 test achieved performance comparable with the best MRI RC without utilization of imaging.
BACKGROUND: A new generation of risk calculators (RCs) for prostate cancer (PCa) incorporating magnetic resonance imaging (MRI) data have been introduced. However, these have not been validated externally, and their clinical benefit compared with alternative approaches remains unclear. OBJECTIVE: To assess previously published PCa RCs incorporating MRI data, and compare their performance with traditional RCs (European Randomized Study of Screening for Prostate Cancer [ERSPC] 3/4 and Prostate Biopsy Collaborative Group [PBCG]) and the blood-based Stockholm3 test. DESIGN, SETTING, AND PARTICIPANTS: RCs were tested in a prospective multicenter cohort including 532 men aged 45-74 yr participating in the Stockholm3-MRI study between 2016 and 2017. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The probabilities of detection of clinically significant PCa (csPCa) defined as Gleason score ≥3 + 4 were calculated for each patient. For each RC and the Stockholm3 test, discrimination was assessed by area under the curve (AUC), calibration by numerical and graphical summaries, and clinical usefulness by decision curve analysis (DCA). RESULTS AND LIMITATIONS: The discriminative ability of MRI RCs 1-4 for the detection of csPCa was superior (AUC 0.81-0.87) to the traditional RCs (AUC 0.76-0.80). The observed prevalence of csPCa in the cohort was 37%, but calibration-in-the-large predictions varied from 14% to 63% across models. DCA identified only one model including MRI data as clinically useful at a threshold probability of 10%. The Stockholm3 test achieved equivalent performance for discrimination (AUC 0.86) and DCA, but was underpredicting the actual risk. CONCLUSIONS: Although MRI RCs discriminated csPCa better than traditional RCs, their predicted probabilities were variable in accuracy, and DCA identified only one model as clinically useful. PATIENT SUMMARY: Novel risk calculators (RCs) incorporating imaging improved the ability to discriminate clinically significant prostate cancer compared with traditional tools. However, all but one predicted divergent compared with actual risks, suggesting that regional modifications be implemented before usage. The Stockholm3 test achieved performance comparable with the best MRI RC without utilization of imaging.
Authors: Massimo Lazzeri; Vittorio Fasulo; Giovanni Lughezzani; Alessio Benetti; Giulia Soldà; Rosanna Asselta; Ilaria De Simone; Marco Paciotti; Pier Paolo Avolio; Roberto Contieri; Cesare Saitta; Alberto Saita; Rodolfo Hurle; Giorgio Guazzoni; Nicolò Maria Buffi; Paolo Casale Journal: Front Oncol Date: 2022-09-06 Impact factor: 5.738
Authors: Jan Chandra Engel; Thorgerdur Palsdottir; Donna Ankerst; Sebastiaan Remmers; Ashkan Mortezavi; Venkatesh Chellappa; Lars Egevad; Henrik Grönberg; Martin Eklund; Tobias Nordström Journal: Eur Urol Open Sci Date: 2022-05-19
Authors: Matthias Neumair; Michael W Kattan; Stephen J Freedland; Alexander Haese; Lourdes Guerrios-Rivera; Amanda M De Hoedt; Michael A Liss; Robin J Leach; Stephen A Boorjian; Matthew R Cooperberg; Cedric Poyet; Karim Saba; Kathleen Herkommer; Valentin H Meissner; Andrew J Vickers; Donna P Ankerst Journal: BMC Med Res Methodol Date: 2022-07-21 Impact factor: 4.612