How Accurate Is Multiparametric MR Imaging in Evaluation of Prostate Cancer Volume?

Bratan, Flavie; Melodelima, Christelle; Souchon, Rémi; A, Hoang Dinh; Mège‐Lechevallier, Florence; Crouzet, Sébastien; Colombel, Marc; Gelet, A.; Rouvière, Olivier

doi:10.1148/radiol.14140524

Cited by 77 publications

(63 citation statements)

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“…The current standard for prostate MRI dictates a multiparametric approach with combines these anatomic sequences with functional techniques such as diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI. While the benefits of this multiparametric approach for PCA detection and staging have been widely reported [22–26], the complexity of the imaging interpretation has also increased. Until recently prostate MRI was interpreted using loosely defined criteria through qualitative “Likert” suspicion scores, however guidelines aiming to standardize prostate MRI acquisition and interpretation have now been published, including the Prostate Imaging Reporting and data system (PI-RADS), with an updated PI-RADS version 2 recently released [27–29].…”

Section: Introductionmentioning

confidence: 99%

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer

et al. 2017

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Purpose To assess the performance of the updated Prostate Imaging Reporting and Data System (PI-RADSv2) and the Apparent Diffusion Coefficient (ADC) for predicting confirmatory biopsy results in patients considered for active surveillance of prostate cancer (PCA). Methods IRB-approved, retrospective study of 371-consecutive men with clinically low-risk PCA (initial biopsy Gleason score≤6, prostate-specific-antigen<10ng/ml, clinical stage≤T2a) who underwent 3T-prostate MRI before confirmatory biopsy. Two independent radiologists recorded the PI-RADSv2 scores and measured the corresponding ADC values in each patient. A composite score was generated to assess the performance of combining PI-RADSv2+ADC. Results PCA was upgraded on confirmatory biopsy in 107/371 (29%) patients. Inter-reader agreement was substantial (PI-RADSv2: k=0.73; 95%CI [0.66–0.80]; ADC: r=0.74; 95%CI [0.69–0.79]). Accuracies, sensitivities, specificities, positive, negative predicted values of PI-RADSv2 were 85%, 89%, 83%, 68%, 95% and 78%, 82%, 76%, 58%, 91% for ADC. PI-RADSv2 accuracy was significantly higher than that of ADC for predicting biopsy upgrade (p=0.014). The combined PI-RADSv2+ADC composite score did not perform better than PI-RADSv2 alone. Obviating biopsy in patients with PI-RADSv2 score ≤3 would have missed Gleason Score upgrade in 12/232 (5%) of patients. Conclusion PI-RADSv2 was superior to ADC measurements for predicting PCA upgrading on confirmatory biopsy.

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Section: Introductionmentioning

confidence: 99%

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer

et al. 2017

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“…All patients gave written informed consent. The database was used for other studies evaluating prostate cancer detection rates [3] and accuracy of tumor volume estimation [31] at multiparametric MR imaging, existing MR scoring systems [11] and diagnostic accuracy of quantitative MR parameters [32–34]. These studies do not overlap with the present one that aims at understanding the relative weight of non-quantitative sequence-specific findings in the characterization of aggressive prostate cancers, in order to improve current MR scoring systems.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

Rouvière¹,

Dagonneau²,

Bratan³

et al. 2017

PLoS ONE

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PurposeTo assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).Materials and methodsWe used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C),ResultsFor csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without.When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased.For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757–0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760–0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774–0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806–0.877, p = 0.11], 0.841 [95%CI:0.799–0.876, p = 0.52]) and 0.849 [95%CI:0.811–0.884, p = 0.49], respectively.For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731–0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746–0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781–0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791–0.868, p = 0.13], 0.790 [95%CI:0.742–0.841, p = 0.12]) and 0.808 [95%CI:0.764–0.845, p = 0.006]), respectively.ConclusionCombination of simple variables can match the Likert score’s results. The optimal combination depends on the definition of csPCa.

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“…Thus far, few studies have focused on tumor volume estimation (343536). In 2002, the value of MR spectroscopic imaging in measuring tumor volume in nodules > 0.5 cm3 was assessed and researchers showed that it was positively correlated with histopathologic tumor volume with a Pearson correlation coefficient of 0.59 (37).…”

Section: Mri Detection Of Clinically Significant Diseasementioning

confidence: 99%

Multiparametric MRI in the Detection of Clinically Significant Prostate Cancer

Fütterer

2017

Korean J Radiol

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Prostate cancer is the most common cancer among men aged 50 years and older in developed countries and the third leading cause of cancer-related death in men. Multiparametric prostate MR imaging is currently the most accurate imaging modality to detect, localize, and stage prostate cancer. The role of multi-parametric MR imaging in the detection of clinically significant prostate cancer are discussed. In addition, insights are provided in imaging techniques, protocol, and interpretation.

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How Accurate Is Multiparametric MR Imaging in Evaluation of Prostate Cancer Volume?

Abstract: Vh can be estimated by using Vmax in aggressive tumors or in tumors with high Likert scores.

Cited by 77 publications

References 55 publications

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer

Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers

Multiparametric MRI in the Detection of Clinically Significant Prostate Cancer

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