PI-RADS Version 2: A Pictorial Update

Purysko, Andrei S.; Rosenkrantz, Andrew B.; Barentsz, Jelle O.; Weinreb, Jeffrey C.; Macura, Katarzyna J.

doi:10.1148/rg.2016150234

Cited by 89 publications

(96 citation statements)

References 42 publications

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“…For each patient, radiologists manually segmented the whole prostate gland, the PZ, and the central gland (TZ + central zone), first on axial T 2 W and then on axial ADC map images. The T 2 W images were segmented first to provide a better delineation of the zonal anatomy for later segmentation on ADC map images where the zonal anatomy is subjectively less well depicted than on T 2 W images . In addition to segmentation of the PZ, radiologists also divided and labeled the segmentations into the PZ base, middle, and apical regions, as described previously .…”

Section: Methodsmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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Background Accurate detection and localization of prostate cancer (PCa) in men undergoing prostate MRI is a fundamental step for future targeted prostate biopsies and treatment planning. Fully automated localization of peripheral zone (PZ) PCa using the apparent diffusion coefficient (ADC) map might be clinically useful. Purpose/Hypothesis To describe automated localization of PCa in the PZ on ADC map MR images using an ensemble U‐Net‐based model. Study Type Retrospective, case–control. Population In all, 226 patients (154 and 72 patients with and without clinically significant PZ PCa, respectively), training, and testing was performed using dataset images of 146 and 80 patients, respectively. Field Strength 3T, ADC maps. Sequence ADC map. Assessment The ground truth was established by manual delineation of the prostate and prostate PZ tumors on ADC maps by dedicated radiologists using MRI‐radical prostatectomy maps as a reference standard. Statistical Tests: Performance of the ensemble model was evaluated using Dice similarity coefficient (DSC), sensitivity, and specificity metrics on a per‐slice basis. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were employed as well. The paired t‐test was used to test the differences between the performances of constituent networks of the ensemble model. Results Our developed algorithm yielded DSC, sensitivity, and specificity of 86.72% ± 9.93%, 85.76% ± 23.33%, and 76.44% ± 23.70%, respectively (mean ± standard deviation) on 80 test cases consisting of 41 and 39 instances from patients with and without clinically significant tumors including 660 extracted 2D slices. AUC was reported as 0.779. Data Conclusion An ensemble U‐Net‐based approach can accurately detect and segment PCa in the PZ from ADC map MR prostate images. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1223–1234.

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Section: Methodsmentioning

confidence: 99%

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Zabihollahy

Ukwatta

Krishna

et al. 2019

Magnetic Resonance Imaging

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“…Bu sebeplerle TZ'deki anormal bulguların değerlendirilmesi PIRADS v2'ye göre esas olarak T2A görüntüler kullanılmaktadır. Kitle boyutunun ölçümü için de esas olarak yine T2A görüntülerin kullanılması önerilmiştir [5]. PIRADS v2'de TZ bulgularının T2A görüntü-lere göre sınıflandırılması aşağıdaki gibidir [5]: • PIRADS 1: TZ'de homojen, orta derecede artmış sinyal intensitesi.…”

Section: Pirads V2unclassified

Prostat Bezi Tranzisyonel Zon Tumorlerinde Manyetik Rezonans Goruntulemenin Rolu

Karaosmanoğlu¹,

Karçaaltıncaba²

2018

Trd Sem

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 Multiparametrik prostat MRG'nin tranzisyonel zon tümörlerinin saptanmasındaki rolü  Tranzisyonel zon tümörlerinin görüntü-lenmesinde; T2 ve difüzyon ağırlıklı gö-rüntülerde prostat kanserini telkin eden MR bulgularının bilinmesi  Multiparametrik prostat MRG'nin tranzisyonel zonda benign prostatik hiperplazi nodülleri ile bu kesim yerleşimli kanserlerin ayırımındaki kısıtlılıklarının öğrenilme-si GİRİŞProstat kanseri erkek popülasyonun en sık görülen kanseri olup aynı zamanda da yine bu popülasyonda üçüncü en sık ölüm nedenidir. Prostat kanserinin klinik seyri hastalar arasında oldukça farklılık göstermekte olup bazı olgularda benign seyrederken bir kısım olgularda da biyolojik olarak oldukça agresif seyredebilmektedir.Prostatın manyetik rezonans görüntüleme (MRG) ile görüntülenmeye başlanması prostat bezindeki tümör odaklarının daha yüksek kesinlikle saptanabilmesine izin vermekte ve ayrıca da hedef biyopsi yapılmasına izin vererek yanlış negatif biyopsilerin sayısının azalmasına büyük oranda katkı yapabilmektedir. Prostat MR görüntülemesinin, diğer tümör biyomarkerlarının da kullanıma girmesiyle, prostat kanserlerinin selektif tanısı ve yüksek riskli hastaların seçiliminin sağlanması noktasında prostat kanseri alanında paradigmanın kaymasına yavaş da olsa yol açtığı artık hemen herkes tarafından kabul edilen bir husustur.Prostat MRG, okült kanser odaklarının tespiti, hedef biyopsi metodu ile daha iyi risk sınıf-laması imkanı vermesi ve non-invaziv bir şe-kilde risk değerlendirilmesine yardımcı olması gibi sebeplerle her geçen gün daha sık olarak kullanılmakta olan bir yöntemdir [1]. Özellik-le multiparametrik prostat MRG (mp-PMRG) tekniğinin yaygın olarak kullanıma girmesi ve bu konuda biriken yoğun tecrübe, bu alanda çok daha büyük gelişmelerin olacağının ve mp-PMRG'nin klinik değerlendirme ve risk sınıflamasında çok daha yoğun bir şekilde kullanılacağının habercisidir. Mp-PMRG ile anatomik, biyolojik ve metabolik olmak üzere çok farklı parametrelerden gelen bilgiler bir araya toplanmakta ve bu şekilde prostat kanseri olan hastaların tanı, tedavi yanıtı ve tedavi sonrası takip gibi pek çok farklı dönemlerinde hasta-

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“…According to current imaging recommendations, multi-parametric prostate MRI (mpMRI) is based on assessment comprising T2 weighted (T2w), dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). According to the PI-RADS V2 classification [6,7], DCE only plays a minor role, while T2w imaging and DWI provide the major criteria for the diagnosis of a clinically significant prostate tumor.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Exponential ADC (eADC) and Computed DWI (cDWI) for the Detection of Prostate Cancer

Sprinkart

Marx

Träber

et al. 2018

Fortschr Röntgenstr

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ZUSAMMENFASSUNG Ziel Direkter Vergleich verschiedener Methoden zur besserenErkennung und Differenzierbarkeit von Prostatakrebs in der diffusionsgewichteten Bildgebung (DWI); Vergleich der Ergebnisse mit originalen DWI-Bildern und konventionellen Apparent Diffusion Coefficient (ADC)-Karten. This document was downloaded for personal use only. Unauthorized distribution is strictly prohibited. dataset exponential ADC maps (eADC), computed DWI images (cDWI), and additionally eADC maps for computed b-values of 2000 and 3000 s/mm² were generated (c_eADC). For each of 123 lesions, the contrast (CR) and contrast-to-noise ratio (CNR) were determined. Differences in the CR and CNR of malignant lesions (n = 83) between the different image types and group differences between benign (n = 40), lowrisk (n = 53) and high-risk (n = 30) lesions were assessed by repeated measures ANOVA and one-way ANOVA with post-hoc tests. The ability to differentiate between benign and malignant and between low-risk and high-risk lesions was assessed by receiver operating characteristic (ROC) curve analyses. Material und MethodenResults The CR and CNR were higher for computed DWI and related c_eADC at b = 3000 s/mm² and 2000 s/mm² compared to original DWI, conventional ADC and standard eADC. For differentiation of benign and malignant lesions, conventional ADC and CR of conventional ADC were best suited. For discrimination of low-risk from high-risk lesions, the CR of c_eADC was best suited followed by the CR of cDWI. Fortschr Röntgenstr 2018; 190: 758-766

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PI-RADS Version 2: A Pictorial Update

Cited by 89 publications

References 42 publications

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

Prostat Bezi Tranzisyonel Zon Tumorlerinde Manyetik Rezonans Goruntulemenin Rolu

Evaluation of Exponential ADC (eADC) and Computed DWI (cDWI) for the Detection of Prostate Cancer

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