Which measurement method should be used for prostate volume for PI-RADS? A comparison of ellipsoid and segmentation methods

Colvin, Robert B.; Walker, David; Hafron, Jason; Seifman, Brian; Nandalur, S.R.; Gangwish, David; Nandalur, Kiran R.

doi:10.1016/j.clinimag.2021.09.003

Cited by 11 publications

(11 citation statements)

References 23 publications

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“… 35 While differences were minimal in absolute terms, the PI-RADSv2.0 method was advocated as a more reliable tool to calculate PSAD. Another study on 397 patients 36 showed that the PI-RADS v2.1-derived volume did not affect the accuracy of PSAD for ISUP ≥2 cancers compared to the segmentation-derived volume, although the accuracy for ISUP ≥4 cancers was greater when PSAD was calculated with the latter method. Overall, although our findings are reasonably generalizable in a PI-RADS v.2.1-centered context, they should be confirmed in a setting using segmentation-derived volumes as the surrogate standard of reference.…”

Section: Discussionmentioning

confidence: 97%

Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI

Girometti

Giannarini

Panebianco

et al. 2021

The British Journal of Radiology

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Objectives: To compare the effect of different PSA density (PSAD) thresholds on the accuracy for clinically significant prostate cancer (csPCa) of the Prostate Imaging Reporting And Data System v.2.1 (PI-RADSv2.1). Methods: We retrospectively included 123 biopsy-naïve men who underwent multiparametric magnetic resonance imaging (mpMRI) and transperineal mpMRI-targeted and systematic prostate biopsy between April 2019 and October 2020. mpMRI, obtained on a 3.0T magnet with a PI-RADSv2.1-compliant protocol, was read by two radiologists (>1500/>500 mpMRI examinations). csPCa was defined as International Society of Urogenital Pathology grading group ≥2. Receiver operating characteristic analysis was used to calculate per-index lesion sensitivity, specificity, and area under the curve (AUC) of PI-RADSv.2.1 categories after adjusting for PSAD ≥0.10,≥0.15, and ≥0.20 ng/mL ml−1. Per-adjusted category cancer detection rate (CDR) was calculated, and decision analysis performed to compare PSAD-adjusted PI-RADSv.2.1 categories as a biopsy trigger. Results: csPCa prevalence was 43.9%. PSAD-adjustment increased the CDR of PI-RADSv2.1 category 4. Sensitivity/specificity/AUC were 92.6%/53.6%/0.82 for unadjusted PI-RADS, and 85.2%/72.4%/0.84, 62.9%/85.5%/0.83, and 92.4%/53.6%/0.82 when adjusting PI-RADS categories for a 0.10, 0.15, and 0.20 ng/ml ml−1 PSAD threshold, respectively. Triggering biopsy for PI-RADS four lesions and PSAD ≥0.10 ng/mL ml−1 was the strategy with greatest net benefit at 30 and 40% risk probability (0.307 and 0.271, respectively). Conclusions: PI-RADSv2.1 category four with PSAD ≥0.10 ng/mL ml−1 was the biopsy-triggering cut-off with the highest net benefit in the range of expected prevalence for csPCa. Advances in knowledge: 0.10 ng/mL ml−1 is the PSAD threshold with higher clinical utility in stratifying the risk for prostate cancer of PI-RADSv.2.1 categories.

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Section: Discussionmentioning

confidence: 97%

Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI

Girometti

Giannarini

Panebianco

et al. 2021

The British Journal of Radiology

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“…We believed that both PSAD and PZ ratio are dependent on the correct measurement of PV and TZV. Colvin et al (13) reported the current PIRADS V2.1 recommendation of either traditional ellipsoid or segmentation volume measurements as viable methods to assess prostate volume. The predictive capability of PSAD for csPCa was not significantly different between the two measurements.…”

Section: Discussionmentioning

confidence: 99%

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

et al. 2022

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PurposeThe aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).Patients and MethodsThe demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.ResultsThe AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.ConclusionsThis study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.

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“…Lesions on MRI were assigned a Prostate Imaging Reporting and Data System (PI-RADS) score of 1 to 5 based on the PI-RADS version 2.1 guideline [ 31 ]. The prostate volume was calculated using the traditional prolate ellipse formula, where volume = (width × height × length) × π/6 [ 32 ]. All prostate dimensions were measured on MRI scans.…”

Section: Methodsmentioning

confidence: 99%

MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population

et al. 2022

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In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65–5.86] vs. 3.54 [2.49–4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05–35.95] vs. 27.90 [21.43–33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80–5.88] vs. 4.52 [3.80–5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs . 0.819, p < 0.001) and csPCa (0.827 vs . 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.

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Which measurement method should be used for prostate volume for PI-RADS? A comparison of ellipsoid and segmentation methods

Cited by 11 publications

References 23 publications

Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI

Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population

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