Estimated diagnostic performance of prostate MRI performed with clinical suspicion of prostate cancer

Hirotsugu Nakai; Hiroaki Takahashi; Jordan D LeGout; Akira Kawashima; Adam T Froemming; Derek J Lomas; Mitchell R Humphreys; Chandler Dora; Naoki Takahashi

doi:10.1186/s13244-024-01845-y

Estimated diagnostic performance of prostate MRI performed with clinical suspicion of prostate cancer

Insights Imaging. 2024 Nov 15;15(1):271. doi: 10.1186/s13244-024-01845-y.

Authors

Hirotsugu Nakai¹, Hiroaki Takahashi¹, Jordan D LeGout², Akira Kawashima³, Adam T Froemming¹, Derek J Lomas⁴, Mitchell R Humphreys⁵, Chandler Dora⁶, Naoki Takahashi⁷

Affiliations

¹ Department of Radiology, Mayo Clinic, Rochester, MN, US.
² Department of Radiology, Mayo Clinic, Jacksonville, FL, US.
³ Department of Radiology, Mayo Clinic, Scottsdale, AZ, US.
⁴ Department of Urology, Mayo Clinic, Rochester, MN, US.
⁵ Department of Urology, Mayo Clinic, Scottsdale, AZ, US.
⁶ Department of Urology, Mayo Clinic, Jacksonville, FL, US.
⁷ Department of Radiology, Mayo Clinic, Rochester, MN, US. Takahashi.Naoki@mayo.edu.

Abstract

Purpose: To assess the diagnostic performance of prostate MRI by estimating the proportion of clinically significant prostate cancer (csPCa) in patients without prostate pathology.

Materials and methods: This three-center retrospective study included prostate MRI examinations performed for clinical suspicion of csPCa (Grade group ≥ 2) between 2018 and 2022. Examinations were divided into two groups: pathological diagnosis within 1 year after the MRI (post-MRI pathology) is present and absent. Risk prediction models were developed using the extracted eleven common predictive variables from the patients with post-MRI pathology. Then, the csPCa proportion in the patients without post-MRI pathology was estimated by applying the model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV/NPV) of prostate MRI in diagnosing csPCa were subsequently calculated for patients with and without post-MRI prostate pathology (estimated statistics) with a positive threshold of PI-RADS ≥ 3.

Results: Of 12,191 examinations enrolled (mean age, 65.7 years ± 8.4 [standard deviation]), PI-RADS 1-2 was most frequently assigned (55.4%) with the lowest pathological confirmation rate of 14.0-18.2%. Post-MRI prostate pathology was found in 5670 (46.5%) examinations. The estimated csPCa proportions across facilities were 12.6-15.3%, 18.4-31.4%, 45.7-69.9%, and 75.4-88.3% in PI-RADS scores of 1-2, 3, 4, and 5, respectively. The estimated (observed) performance statistics were as follows: AUC, 0.78-0.81 (0.76-0.79); sensitivity, 76.6-77.3%; specificity, 67.5-78.6%; PPV, 49.8-66.6% (52.0-67.7%); and NPV, 84.4-87.2% (82.4-86.6%).

Conclusion: We proposed a method to estimate the probabilities harboring csPCa for patients who underwent prostate MRI examinations, which allows us to understand the PI-RADS diagnostic performance with several metrics.

Clinical relevance statement: The reported estimated performance metrics are expected to aid in understanding the true diagnostic value of PI-RADS in the entire prostate MRI population performed with clinical suspicion of prostate cancer.

Key points: Calculating performance metrics only from patients who underwent prostate biopsy may be biased due to biopsy selection criteria, especially in PI-RADS 1-2. The estimated area under the receiver operating characteristic curve of PI-RADS in the entire prostate MRI population ranged from 0.78 to 0.81 at three facilities. The estimated statistics are expected to help us understand the true PI-RADS performance and serve as a reference for future studies.

Keywords: Early detection of cancer; Magnetic resonance imaging; Models (statistical); Prostatic neoplasms.