Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?

Rouvière, Olivier; Papillard, M.; Girouin, N.; Boutier, R.; Rabilloud, Muriel; Riche, Benjamin; Mège‐Lechevallier, Florence; Colombel, Marc; Gelet, A.

doi:10.1007/s00330-011-2343-8

Cited by 30 publications

(10 citation statements)

References 27 publications

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“…Therefore, histologic interpretations from biopsy specimens are used to determine the ground truth in some studies [14, 62, 122, 137, 138]. In vivo biopsy can only label the pathology of the core inside the prostate.…”

Section: Validationmentioning

confidence: 99%

Computer-aided Detection of Prostate Cancer with MRI

et al. 2016

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One in six men will develop prostate cancer in his life time. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multi-parametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and MR spectroscopy imaging. Due to the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In order to improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

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Section: Validationmentioning

confidence: 99%

Computer-aided Detection of Prostate Cancer with MRI

et al. 2016

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“…Indeed, characterization of focal lesions seen on prostate mp-MRI remains difficult because of the overlap between the appearance of cancers and benign conditions and because the different pulse sequences that compose mp-MRI may yield discordant results [2,3]. As a consequence, the good results published by specialized institutions may not be reproduced by less-experienced radiologists [4].…”

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confidence: 96%

Characterization of prostate cancer using T2 mapping at 3T: A multi-scanner study

Dinh

Souchon

Melodelima

et al. 2015

Diagnostic and Interventional Imaging

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“…This strongly suggests that shape is not a good predictor of csPCa, even if the PIRADS v2 and other scoring systems [15, 17] use it to characterize focal lesions in PZ. The poor diagnostic value of shape had already been found in another study [14] and may be due to the fact that it remains a very subjective feature.…”

Section: Discussionmentioning

confidence: 87%

“…However, because there are no descriptions of specific criteria to be used in the scoring process, the Likert score relies heavily on the reader’s experience. Therefore, some research groups tried to set up more objective scoring systems to improve inter-reader agreement [14–17]. …”

Section: Introductionmentioning

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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Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?

Cited by 30 publications

References 27 publications

Computer-aided Detection of Prostate Cancer with MRI

Computer-aided Detection of Prostate Cancer with MRI

Characterization of prostate cancer using T2 mapping at 3T: A multi-scanner study

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

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