Dataset Information

Quantifying the Effect of Location Matching on Accuracy of Multiparametric Magnetic Resonance Imaging Prior to Prostate Biopsy-A Multicentre Study.

ABSTRACT:

Background

Multiparametric magnetic resonance imaging (mpMRI) has shown promise to improve detection of prostate cancer over conventional methods. However, most studies do not describe whether the location of mpMRI lesions match that of cancer found at biopsy, which may lead to an overestimation of accuracy.

Objective

To quantitate the effect of mapping locations of mpMRI lesions to locations of positive biopsy cores on the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI.

Design setting and participant

We retrospectively identified patients having mpMRI of the prostate preceding prostate biopsy at three centres from 2013 to 2016. Men with targetable lesions on imaging underwent directed biopsy in addition to systematic biopsy. We correlated locations of positive mpMRI lesions with those of positive biopsy cores, defining a match when both were in the same sector of the prostate. We defined positive mpMRI as Prostate Imaging Reporting and Data System (PI-RADS) score ≥4 and significant cancer at biopsy as grade group ≥2.

Outcome measurements and statistical analysis

Sensitivity, specificity, PPV, and NPV were calculated with and without location matching.

Results and limitations

Of 446 patients, 247 (55.4%) had positive mpMRI and 232 (52.0%) had significant cancer at biopsy. Sensitivity and NPV for detecting significant cancer with location matching (both 63.4%) were decreased compared with those without location matching (77.6% and 73.9%, respectively). Of the 85 significant cancers not detected by mpMRI, most were of grade group 2 (64.7%, 55/85).

Conclusions

We report a 10-15% decrease in sensitivity and NPV when location matching was used to detect significant prostate cancer by mpMRI. False negative mpMRI remains an issue, highlighting the continued need for biopsy and for improving the standards around imaging quality and reporting.

Patient summary

The true accuracy of multiparametric magnetic resonance imaging (mpMRI) must be determined to interpret results and better counsel patients. We mapped the location of positive mpMRI lesions to where cancer was found at biopsy and found, when compared with matching to cancer anywhere in the prostate, that the accuracy of mpMRI decreased by 10-15%.

SUBMITTER: Gavin DJ

PROVIDER: S-EPMC8317842 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.MethodsWe retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa.ResultsUnivariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.ConclusionOur multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.