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July 1, 2024Bulletin of Urooncology0 citationsOpen Access

Correlation of Multiparametric Prostate MRI with Prostate Biopsy and Radical Prostatectomy Histopathology

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HŞHakan ŞığvaSGSadık GörürFGFatih Gökalp

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Abstract

Objective: Prostate cancer (PCa) is the most common cancer responsible for cancer deaths in men after lung cancer.In this study, we aimed to obtain information about the Gleason score of PCa by prostate image reporting and data system (PIRADS) scoring of multiparametric magnetic resonance imaging (mpMRI) by comparing mpMRI results with the histopathology of prostate biopsy and radical prostatectomy specimen.Materials and Methods: A total of 214 patients who applied to the outpatient clinic of Hatay Mustafa Kemal University Faculty of Medicine, Department of Urology between January 2019 and April 2021 with elevated prostate-specific antigen (PSA) levels were included in the study.All patients underwent mpMRI before the biopsy procedure.PIRADS scoring was performed by the same radiologist.Prostate biopsy was systematically performed by experienced urologists as 12 quadrant biopsies.Results: When the mpMRI results of the patients are evaluated; the most common patterns are seen as PIRADS 2 and PIRADS 4, followed by PIRADS 3 lesions, followed by PIRADS 5 lesions, and PIRADS 1 lesions, which were the least frequent.When the analysis was applied to predict PCa over the pyrans value, the receiver operating characteristics analysis result for the diagnosis of the disease showed statistically significant levels of area under the curve (0.860; p<0.001), with a sensitivity of 81% and a sensitivity of 3 and above PIRADS 3 and above.It can predict cancer with 75 specificity.In the correlation analysis, there was a low but significant correlation between PIRADS and PSA value (r=0.252;p<0.001). Conclusion:We found that patients presenting with elevated PSA levels and mpMRI had a high power in detecting PCa.We also found a strong relationship between ISUP rating and PIRADS.As a result, it is thought that the pathology of the patients can be predicted using mpMRI.

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Şığva et al. (2024) studied this question.

synapsesocial.com/papers/68e62195b6db6435875b35eahttps://doi.org/10.4274/uob.galenos.2024.2024.1.2
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