The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer

Thompson, James; Leeuwen, Pim J. van; Moses, Daniel; Shnier, Ron; Brenner, Phillip; Delprado, Warick; Pulbrook, Marley; Böhm, Maret; Haynes, Anne-Marie; Hayen, Andrew; Stricker, Phillip D.

doi:10.1016/j.juro.2015.10.140

Cited by 132 publications

(121 citation statements)

References 27 publications

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“…The median number of taken cores in our cohort was 18 in comPbx, 12 in sysPbx and 6 in fusPbx. Other study groups perform fusPbx in combination with volumebased systematic template biopsy, which results in a median number of 20-30 systematic cores, while the detection rate of all and significant PCa was comparable to our detection rates in sysPbx [24][25][26].…”

Section: Discussionsupporting

confidence: 81%

Evaluation of Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy in Patients with Low-Risk Prostate Cancer Under Active Surveillance Undergoing Surveillance Biopsy

Borkowetz¹,

Renner²,

Platzek³

et al. 2018

Urol Int

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Introduction: Targeted biopsy of tumour-suspicious lesions detected in multiparametric magnetic resonance imaging (mpMRI) plays an increasing role in the active surveillance (AS) of patients with low-risk prostate cancer (PCa). The aim of this study was to compare MRI/ultrasound-fusion biopsy (fusPbx) with systematic biopsy (sysPbx) in patients undergoing biopsy for AS. Methods: Patients undergoing mpMRI and transperineal fusPbx combined with transrectal sysPbx (comPbx) as surveillance biopsy were investigated. The detection of Gleason score upgrading and reclassification according to Prostate Cancer Research International Active Surveillance criteria were evaluated. Results: Eighty-three patients were enrolled. PCa upgrading was detected in 39% by fusPbx and in 37% by sysPbx (p = 1.0). The percentage of patients who were reclassified in fusPbx and sysPbx (p = 0.45) were 64 and 59% respectively. ComPbx detected more frequently tumour upgrading than fusPbx (71 vs. 64%, p = 0.016) and sysPbx (71 vs. 59%, p < 0.001) and more patients had to be reclassified after comPbx than after fusPbx or sysPbx alone. Conclusions: The combination of fusPbx and sysPbx outperforms both modalities alone with regard to the detection of upgrading and reclassification in patients under AS. Because a high missing rate of significant PCa still exists in both biopsy modalities, a combination of fusPbx and sysPbx should be recommended in these patients.

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Section: Discussionsupporting

confidence: 81%

Evaluation of Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy in Patients with Low-Risk Prostate Cancer Under Active Surveillance Undergoing Surveillance Biopsy

Borkowetz¹,

Renner²,

Platzek³

et al. 2018

Urol Int

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“…This has been supported by other studies with one recent study showing that only 4% of clinically significant disease would have been missed with a target alone strategy 16 . Other authors have also demonstrated negative predictive values of 90% or higher [17][18][19] . Third, consensus is lacking regarding the definition of clinically significant cancer.…”

Section: Methodological Limitationsmentioning

confidence: 89%

Magnetic resonance imaging targeted transperineal prostate biopsy: a local anaesthetic approach

Bass

Donaldson

Freeman

et al. 2017

Prostate Cancer Prostatic Dis

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“…Although the prostate cancer is usually not the direct cause of death, the differentiation of clinically significant prostate cancer lesions from those low-grade lesions is critical 1 . The use of multiparametric MRI (mpMRI) with Prostate Imaging Reporting And Data System (PI-RADS) has increased the sensitivity in prostate cancer detection, but the specificity is still poor which may lead to unnecessary biopsies 4,5 . The accuracy of mpMRI with PI-RADS is also largely dependent on the experience of the radiologist 5 .…”

Section: Introductionmentioning

confidence: 99%

Prostate cancer diagnosis using deep learning with 3D multiparametric MRI

Liu¹,

et al. 2017

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A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved the second highest AUC (0.84) in the PROSTATEx challenge. This study shows the great potential of deep learning for cancer imaging.

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The Diagnostic Performance of Multiparametric Magnetic Resonance Imaging to Detect Significant Prostate Cancer

Cited by 132 publications

References 27 publications

Evaluation of Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy in Patients with Low-Risk Prostate Cancer Under Active Surveillance Undergoing Surveillance Biopsy

Evaluation of Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy in Patients with Low-Risk Prostate Cancer Under Active Surveillance Undergoing Surveillance Biopsy

Magnetic resonance imaging targeted transperineal prostate biopsy: a local anaesthetic approach

Prostate cancer diagnosis using deep learning with 3D multiparametric MRI

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