A predictive model based on biparametric magnetic resonance imaging and clinical parameters for improved risk assessment and selection of biopsy-naïve men for prostate biopsies

Boesen, Lars; Thomsen, Frederik Birkebæk; Nørgaard, Nis; Løgager, Vibeke; Balslev, I.; Bisbjerg, Rasmus; Thomsen, Henrik S.; Jakobsen, Henrik

doi:10.1038/s41391-019-0149-y

Cited by 24 publications

(31 citation statements)

References 33 publications

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“…In a study by Lee et al, an internally validated nomogram/model using MRI performed without intravenous contrast agent was able to accurately predict PCa and SPCa in transperineal prostate biopsy. Recently, Boesen et al developed and internally validated a nomogram for prediction of prostate biopsy outcome based on bpMRI, which consisted of a T 2 w and a single DWI acquisition . The current study goes a few steps further since 1) all men underwent a rapid biparametric MRI (IMPROD bpMRI), which consists of three separate DWI acquisitions; 2) the nomograms/model were developed using prospectively collected dataset in the single institutional trial (IMPROD trial) and externally validated in a multiinstitutional trial (MULTI‐IMPROD trial).…”

Section: Discussionmentioning

confidence: 95%

“…Multiparametric prostate MRI combined with systematic biopsy and targeted biopsy was shown to improve cancer detection rates of clinically significant PCa (SPCa) compared with performing systematic biopsy in all men with elevated PSA. Furthermore, in some studies the use of PSA density (PSAD) was shown to further improve the diagnostic performance of multiparametric prostate MRI …”

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confidence: 99%

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Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI‐Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI‐IMPROD Trials)

Perez

Jambor

Kauko

et al. 2019

Magnetic Resonance Imaging

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Background: Multiparametric MRI of the prostate has been shown to improve the risk stratification of men with an elevated prostate-specific antigen (PSA). However, long acquisition time, high cost, and inter-center/reader variability of a routine prostate multiparametric MRI limit its wider adoption. Purpose: To develop and validate nomograms based on unique rapid biparametric MRI (bpMRI) qualitative and quantitative derived variables for prediction of clinically significant cancer (SPCa). Study Type: Retrospective analyses of single (IMPROD, NCT01864135) and multiinstitution trials (MULTI-IMPROD, NCT02241122). Population: 161 and 338 prospectively enrolled men who completed the IMPROD and MULTI-IMPROD trials, respectively. Field Strength/Sequence: IMPROD bpMRI: 3T/1.5T, T 2 -weighted imaging, three separate diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm 2 ; 2) b values 0, 1500 s/mm 2 ; 3) values 0, 2000 s/mm 2 . Assessment: The primary endpoint of the combined trial analysis was the diagnostic accuracy of the combination of IMPROD bpMRI and clinical variables for detection of SPCa.View this article online at wileyonlinelibrary.com.

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Section: Discussionmentioning

confidence: 95%

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confidence: 99%

Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI‐Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI‐IMPROD Trials)

Perez

Jambor

Kauko

et al. 2019

Magnetic Resonance Imaging

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“…This is consistent with other similar studies. [20][21][22][23] Meanwhile, the nomogram exhibited an effective reclassification improvement in both the DC and VC, compared to model1. This suggested that the combination of the mpMRI-based PI-RADS score and other independent clinical factors is superior to mpMRI or clinical factors alone for discriminating csPCa.…”

Section: Discussionmentioning

confidence: 94%

“…25 DRE did not incorporate in the nomogram, although previous studies have proven DRE was also a significant predictor of csPCa. 10,11,20,23,24 Because we wanted the nomogram to be as simple and objective as possible, and DRE is usually performed by doctors with different experiences, which may affect the accuracy of the nomogram if incorporated. Some novel biomarkers, such as HOXC6 and DLX1 mRNA expression levels, were incorporated into the csPCa prediction model and achieved excellent performance with an AUC of 0.90 (95% CI, 0.85-0.95).…”

Section: Discussionmentioning

confidence: 99%

<p>A PI-RADS-Based New Nomogram for Predicting Clinically Significant Prostate Cancer: A Cohort Study</p>

Zhang

Zhu

Zhao

et al. 2020

CMAR

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To develop and validate a PI-RADS-based nomogram for predicting the probability of clinically significant prostate cancer (csPCa) at initial prostate biopsy. Patients and Methods: From February 2015 to October 2018, 573 consecutive patients made up the development cohort (DC), and another 253 patients were included as an independent validation cohort (VC). Univariate and multivariate analysis were used for determining the dependent clinical risk factors for csPCa. Prediction model1 was constructed by integrating independent clinical risk factors. Then added the PI-RADS score to model1 to develop the prediction model2 and present it in the form of a nomogram. The performance of the nomogram was assessed by receiver operating characteristic curve, net reclassification improvement analysis, calibration curve, and decision curve. Results: All clinical candidate factors were significantly different between csPCa and non-csPCa in both the DC and VC. Age, PSA density (PSAD), and free-to-total PSA ratio (f/t) were ultimately determined as dependent clinical risk factors for csPCa and integrated into prediction model1. Then, prediction model2 was developed and presented in a nomogram. In the DC, the nomogram (AUC=0.894) was superior to model1, PI-RADS score, or other clinical factors alone in detecting csPCa. Similar result (AUC=0.891) was obtained in the VC. NRI analysis showed that the nomogram improved the classification of patients significantly compared with model1. Furthermore, the nomogram showed favorable calibration and great clinical usefulness. Conclusion:This study developed and validated a nomogram that integrates PI-RADS score with other independent clinical risk factors to facilitate prebiopsy individualized prediction in high-risk patients with csPCa.

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“…7,10−12 However, mp-MRI is expensive and time-consuming, and the high prevalence of PCa mp-MRI poses a significant financial burden on society. 13,14 Interestingly, biparametric MRI (bp-MRI), consisting of T2-weighted imaging and diffusion-weighted imaging without dynamic contrastenhanced imaging, has shown comparable efficacy in detecting clinically significant PCa by mp-MRI. 13,15 It could be speculated that bp-MRI may also have potential value in predicting adverse pathological features in PCa patients.…”

Section: Introductionmentioning

confidence: 99%

<p>Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer</p>

Liu¹,

Tang²,

Ding³

et al. 2020

CMAR

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Objective: To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies. Patients and Methods: We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostatespecific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator. Results: A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit. Conclusion: We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.

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A predictive model based on biparametric magnetic resonance imaging and clinical parameters for improved risk assessment and selection of biopsy-naïve men for prostate biopsies

Cited by 24 publications

References 33 publications

Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI‐Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI‐IMPROD Trials)

Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI‐Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI‐IMPROD Trials)

<p>A PI-RADS-Based New Nomogram for Predicting Clinically Significant Prostate Cancer: A Cohort Study</p>

<p>Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer</p>

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