Geoffrey A Sonn1, Richard E Fan2, Pejman Ghanouni3, Nancy N Wang2, James D Brooks2, Andreas M Loening3, Bruce L Daniel3, Katherine J To'o4, Alan E Thong2, John T Leppert5. 1. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: gsonn@stanford.edu. 2. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA. 3. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. 4. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs, Palo Alto Health Care System, Palo Alto, CA. 5. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA; Veterans Affairs, Palo Alto Health Care System, Palo Alto, CA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Abstract
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) interpreted by experts is a powerful tool for diagnosing prostate cancer. However, the generalizability of published results across radiologists of varying expertise has not been verified. OBJECTIVE: To assess variability in mpMRI reporting and diagnostic accuracy across radiologists of varying experience in routine clinical care. DESIGN, SETTING, AND PARTICIPANTS: Men who underwent mpMRI and MR-fusion biopsy between 2014-2016. Each MRI scan was read by one of nine radiologists using the Prostate Imaging Reporting and Data System (PIRADS) and was not re-read before biopsy. Biopsy histopathology was the reference standard. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes were the PIRADS score distribution and diagnostic accuracy across nine radiologists. We evaluated the association between age, prostate-specific antigen, PIRADS score, and radiologist in predicting clinically significant cancer (Gleason ≥7) using multivariable logistic regression. We conducted sensitivity analyses for case volume and changes in accuracy over time. RESULTS AND LIMITATIONS: We analyzed data for 409 subjects with 503 MRI lesions. While the number of lesions (mean 1.2 lesions/patient) did not differ across radiologists, substantial variation existed in PIRADS distribution and cancer yield. The significant cancer detection rate was 3-27% for PIRADS 3 lesions, 23-65% for PIRADS 4, and 40-80% for PIRADS 5 across radiologists. Some 13-60% of men with a PIRADS score of <3 on MRI harbored clinically significant cancer. The area under the receiver operating characteristic curve varied from 0.69 to 0.81 for detection of clinically significant cancer. PIRADS score (p<0.0001) and radiologist (p=0.042) were independently associated with cancer in multivariable analysis. Neither individual radiologist volume nor study period impacted the results. MRI scans were not retrospectively re-read by all radiologists, precluding measurement of inter-observer agreement. CONCLUSIONS: We observed considerable variability in PIRADS score assignment and significant cancer yield across radiologists. We advise internal evaluation of mpMRI accuracy before widespread adoption. PATIENT SUMMARY: We evaluated the interpretation of multiparametric magnetic resonance imaging of the prostate in routine clinical care. Diagnostic accuracy depends on the Prostate Imaging Reporting and Data System score and the radiologist.
BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) interpreted by experts is a powerful tool for diagnosing prostate cancer. However, the generalizability of published results across radiologists of varying expertise has not been verified. OBJECTIVE: To assess variability in mpMRI reporting and diagnostic accuracy across radiologists of varying experience in routine clinical care. DESIGN, SETTING, AND PARTICIPANTS: Men who underwent mpMRI and MR-fusion biopsy between 2014-2016. Each MRI scan was read by one of nine radiologists using the Prostate Imaging Reporting and Data System (PIRADS) and was not re-read before biopsy. Biopsy histopathology was the reference standard. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes were the PIRADS score distribution and diagnostic accuracy across nine radiologists. We evaluated the association between age, prostate-specific antigen, PIRADS score, and radiologist in predicting clinically significant cancer (Gleason ≥7) using multivariable logistic regression. We conducted sensitivity analyses for case volume and changes in accuracy over time. RESULTS AND LIMITATIONS: We analyzed data for 409 subjects with 503 MRI lesions. While the number of lesions (mean 1.2 lesions/patient) did not differ across radiologists, substantial variation existed in PIRADS distribution and cancer yield. The significant cancer detection rate was 3-27% for PIRADS 3 lesions, 23-65% for PIRADS 4, and 40-80% for PIRADS 5 across radiologists. Some 13-60% of men with a PIRADS score of <3 on MRI harbored clinically significant cancer. The area under the receiver operating characteristic curve varied from 0.69 to 0.81 for detection of clinically significant cancer. PIRADS score (p<0.0001) and radiologist (p=0.042) were independently associated with cancer in multivariable analysis. Neither individual radiologist volume nor study period impacted the results. MRI scans were not retrospectively re-read by all radiologists, precluding measurement of inter-observer agreement. CONCLUSIONS: We observed considerable variability in PIRADS score assignment and significant cancer yield across radiologists. We advise internal evaluation of mpMRI accuracy before widespread adoption. PATIENT SUMMARY: We evaluated the interpretation of multiparametric magnetic resonance imaging of the prostate in routine clinical care. Diagnostic accuracy depends on the Prostate Imaging Reporting and Data System score and the radiologist.
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