Dataset Information

The role of quantitative MRI-based prostate zonal parameters in predicting clinically significant prostate cancer A U.S. cohort.

ABSTRACT:

Introduction

We aimed to investigate the clinical utility of quantitative prostatic zonal measurements on multiparametric magnetic resonance imaging (mpMRI) for the predication of clinically significant prostate cancer (csPCa).

Methods

A retrospective, single-institution study included 144 men who underwent mpMRI from 2015-2017. Prostate zone parameters were measured on mpMRI. Correlation and multivariable analysis evaluated the relationship between prostate zone parameters and the presence of csPCa.

Results

The mean age was 66.9±7.8 years old. The median (interquartile range [IQR]) prostate volume and prostate-specific antigen (PSA) were 51.6 ml (37.1-74.5) and 6.1 ng/ ml (4.5-8.2), respectively. Men with csPCa had significantly smaller total prostate volume (TPV), transitional zone volume (TZV), and transitional zone thickness (TZT), and larger transitional zone density (TZD) compared to those without PCa; however, on multivariate variable analysis, only TZD maintained significance. TZD had a comparable area under the curve to PSA density (PSAD) and PSA (0.74 vs. 0.73 vs. 0.60, respectively). In a subgroup analysis of men with PCa, PSAD and TZD were significantly higher in men with Gleason grade group (GG) ≥2 compared to those with GG <2 (p=0.002); however, this significance is not maintained on logistic regression in predicting GG.

Conclusions

Quantitative features of prostate zones on MRI may aid in identifying better predictors of csPCa. Zonal-based PSA density (TZD) may be a useful marker in identifying csPCa. Further exploration is needed to understand the clinical application of larger TZV in men with csPCa compared to those with insignificant disease.

SUBMITTER: Sze C

PROVIDER: S-EPMC10382215 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Publications

The role of quantitative MRI-based prostate zonal parameters in predicting clinically significant prostate cancer A U.S. cohort.

Sze Christina C Brant Aaron A Johnson Jeffery P JP Singh Zorawar Z DeMeo Gina G Moryousef Joseph J Zorn Kevin C KC Bhojani Naeem N Elterman Dean S DS Margolis Daniel D Chughtai Bilal B

Canadian Urological Association journal = Journal de l'Association des urologues du Canada 20230701 7

<h4>Introduction</h4>We aimed to investigate the clinical utility of quantitative prostatic zonal measurements on multiparametric magnetic resonance imaging (mpMRI) for the predication of clinically significant prostate cancer (csPCa).<h4>Methods</h4>A retrospective, single-institution study included 144 men who underwent mpMRI from 2015-2017. Prostate zone parameters were measured on mpMRI. Correlation and multivariable analysis evaluated the relationship between prostate zone parameters and th ...[more]

PMID: 37068148

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Project description:OBJECTIVE. The purpose of this study was to compare in a multireader manner the diagnostic accuracies of 3-T multiparametric MRI interpretation and serial prostate-specific antigen (PSA) measurement in predicting the presence of residual clinically significant prostate cancer after focal laser ablation. MATERIALS AND METHODS. Eighteen men had undergone focal laser ablation for low- or intermediate-risk prostate cancer as part of two National Cancer Institute-funded phase 1 clinical trials. Multiparametric MRI was performed immediately after and 6 and 12 months after focal laser ablation. Serial PSA measurements after focal laser ablation were recorded, and MRI-ultrasound fusion biopsy was performed 6 and 12 months after ablation and served as the reference standard. Multiparametric MRI was performed at 3 T with pelvic phased-array coils. T2-weighted, DW, and dynamic contrast-enhanced MR images were retrospectively assessed by two blinded radiologists using a 3-point Likert scale (0-2). Inter-reader agreement was assessed with the Cohen kappa statistic. The diagnostic accuracies of multiparametric MRI and PSA measurement were compared. RESULTS. Residual clinically significant prostate cancer was identified in 11 of 18 (61%) men. Logistic regression analysis of serial PSA measurements yielded a correct classification rate of 61.1% (p > 0.05). Using a multiparametric MRI threshold score of 4 or greater, both radiologists made correct classifications for 16 of 18 men (89%) at 6 months and 15 of 17 men (88%) at 12 months. Interreader agreement was substantial to excellent for T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI and improved uniformly from 6 to 12 months. Logistic regression analysis of the retrospectively reviewed multiparametric MR images yielded AUCs greater than 0.90 for each radiologist 6 and 12 months after focal laser ablation (p < 0.001). CONCLUSION. Multiparametric MRI 6 and 12 months after focal laser ablation significantly outperformed serial PSA measurements for predicting the presence of residual clinically significant prostate cancer.

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Project description:PurposeTo establish machine learning(ML) models for the diagnosis of clinically significant prostate cancer (csPC) using multiparameter magnetic resonance imaging (mpMRI), texture analysis (TA), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative analysis and clinical parameters and to evaluate the stability of these models in internal and temporal validation.MethodsThe dataset of 194 men was split into training (n = 135) and internal validation (n = 59) cohorts, and a temporal dataset (n = 58) was used for evaluation. The lesions with Gleason score ≥ 7 were defined as csPC. Logistic regression (LR), stepwise regression (SR), classical decision tree (cDT), conditional inference tree (CIT), random forest (RF) and support vector machine (SVM) models were established by combining mpMRI-TA, DCE-MRI and clinical parameters and validated by internal and temporal validation using the receiver operating characteristic (ROC) curve and Delong's method.ResultsEight variables were determined as important predictors for csPC, with the first three related to texture features derived from the apparent diffusion coefficient (ADC) mapping. RF, LR and SR models yielded larger and more stable area under the ROC curve values (AUCs) than other models. In the temporal validation, the sensitivity was lower than that of the internal validation (p < 0.05). There were no significant differences in specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and AUC (p > 0.05).ConclusionsEach machine learning model in this study has good classification ability for csPC. Compared with internal validation, the sensitivity of each machine learning model in temporal validation was reduced, but the specificity, accuracy, PPV, NPV and AUCs remained stable at a good level. The RF, LR and SR models have better classification performance in the imaging-based diagnosis of csPC, and ADC texture-related parameters are of the highest importance.

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Dataset Information

The role of quantitative MRI-based prostate zonal parameters in predicting clinically significant prostate cancer A U.S. cohort.

Introduction

Methods

Results

Conclusions

Publications

The role of quantitative MRI-based prostate zonal parameters in predicting clinically significant prostate cancer A U.S. cohort.

Similar Datasets

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