Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan
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 an...
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2021-09-01
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doaj-6b2fee26655341f9a2d7bc63e1dc4d7e2021-09-14T04:32:50ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-09-011110.3389/fonc.2021.732027732027Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging ScanJunxiao Liu0Shuanbao Yu1Biao Dong2Guodong Hong3Jin Tao4Yafeng Fan5Zhaowei Zhu6Zhiyu Wang7Xuepei Zhang8Xuepei Zhang9Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaKey Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou, ChinaPurposeThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2021.732027/fullprostate cancermagnetic resonance imagingprostate-specific antigenprostate-specific antigen densitymultivariate model |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junxiao Liu Shuanbao Yu Biao Dong Guodong Hong Jin Tao Yafeng Fan Zhaowei Zhu Zhiyu Wang Xuepei Zhang Xuepei Zhang |
spellingShingle |
Junxiao Liu Shuanbao Yu Biao Dong Guodong Hong Jin Tao Yafeng Fan Zhaowei Zhu Zhiyu Wang Xuepei Zhang Xuepei Zhang Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan Frontiers in Oncology prostate cancer magnetic resonance imaging prostate-specific antigen prostate-specific antigen density multivariate model |
author_facet |
Junxiao Liu Shuanbao Yu Biao Dong Guodong Hong Jin Tao Yafeng Fan Zhaowei Zhu Zhiyu Wang Xuepei Zhang Xuepei Zhang |
author_sort |
Junxiao Liu |
title |
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan |
title_short |
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan |
title_full |
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan |
title_fullStr |
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan |
title_full_unstemmed |
Developing Strategy to Predict the Results of Prostate Multiparametric Magnetic Resonance Imaging and Reduce Unnecessary Multiparametric Magnetic Resonance Imaging Scan |
title_sort |
developing strategy to predict the results of prostate multiparametric magnetic resonance imaging and reduce unnecessary multiparametric magnetic resonance imaging scan |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-09-01 |
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. |
topic |
prostate cancer magnetic resonance imaging prostate-specific antigen prostate-specific antigen density multivariate model |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2021.732027/full |
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