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Abdominal Radiology

Erschienen in:

30.09.2023 | Kidneys, Ureters, Bladder, Retroperitoneum

Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI

verfasst von: Lihua Xiang, Suping Ma, Yongqiang Xu, Lei Jiang, Hao Guo, Hongyan Liu, Yunyun Liu

Erschienen in: Abdominal Radiology | Ausgabe 12/2023

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Abstract

Purpose

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4–5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI.

Materials and methods

The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated.

Results

A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694–0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844–0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%.

Conclusion

The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4–5.

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Titel: Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI
verfasst von: Lihua Xiang
Suping Ma
Yongqiang Xu
Lei Jiang
Hao Guo
Hongyan Liu
Yunyun Liu
Publikationsdatum: 30.09.2023
Verlag: Springer US
Erschienen in: Abdominal Radiology / Ausgabe 12/2023
Print ISSN: 2366-004X
Elektronische ISSN: 2366-0058
DOI: https://doi.org/10.1007/s00261-023-04011-y

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Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI