Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network

Jensen, Carina; Sørensen, Kristine Storm; Jørgensen, Cecilia Klitgaard; Nielsen, Camilla Winther; Høy, Pia Christine; Langkilde, Niels Christian; Østergaard, Lasse Riis

doi:10.1117/1.jmi.6.1.014501

Cited by 14 publications

(15 citation statements)

References 39 publications

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“…As with AI models for cancer detection tasks, AI models for prostate segmentation on MRI are mostly trained and validated on small data sets (40–250 patients), 123 – 131 often with retrospective, single-center data 124 , 131 – 133 without validation in external cohorts. 124 , 131 , 132 The trained models and the source code to pre-process the data and train the model are often not publicly available, 123 – 125 , 127 , 128 , 130 , 132 , 133 , 135 limiting the comparison between these models, as well as their usage. The better-performing models achieved Dice scores (metric of similarity between manual and AI-predicted segmentations) of at least 0.90 in internal and 0.80 in external data sets.…”

Section: Ai Models For Supporting Tasks In Cancer Detectionmentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

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A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

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Section: Ai Models For Supporting Tasks In Cancer Detectionmentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

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“… ? ✓ – ✓ Axial T2W 5.0 Jensen et al [ 31 ] 2 – ✓ ✓ ✓ ✓ ✓ ✓ ✓ Axial T2W 1.5–3.0 Khan et al [ 17 ] 2 ✓ – ✓ ✓ ✓ ✓ ✓ ✓ Axial T2W 3.0–4.0 Cheng et al [ 30 ] multiple ? ?…”

Section: Resultsmentioning

confidence: 99%

Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature

Montagne

Hamzaoui

et al. 2022

Insights Imaging

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Objectives Accurate zonal segmentation of prostate boundaries on MRI is a critical prerequisite for automated prostate cancer detection based on PI-RADS. Many articles have been published describing deep learning methods offering great promise for fast and accurate segmentation of prostate zonal anatomy. The objective of this review was to provide a detailed analysis and comparison of applicability and efficiency of the published methods for automatic segmentation of prostate zonal anatomy by systematically reviewing the current literature. Methods A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted until June 30, 2021, using PubMed, ScienceDirect, Web of Science and EMBase databases. Risk of bias and applicability based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria adjusted with Checklist for Artificial Intelligence in Medical Imaging (CLAIM) were assessed. Results A total of 458 articles were identified, and 33 were included and reviewed. Only 2 articles had a low risk of bias for all four QUADAS-2 domains. In the remaining, insufficient details about database constitution and segmentation protocol provided sources of bias (inclusion criteria, MRI acquisition, ground truth). Eighteen different types of terminology for prostate zone segmentation were found, while 4 anatomic zones are described on MRI. Only 2 authors used a blinded reading, and 4 assessed inter-observer variability. Conclusions Our review identified numerous methodological flaws and underlined biases precluding us from performing quantitative analysis for this review. This implies low robustness and low applicability in clinical practice of the evaluated methods. Actually, there is not yet consensus on quality criteria for database constitution and zonal segmentation methodology.

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“…The study reported an improved image quality, allowing improved detection of small positive lymph nodes on 3.0 T. There were fewer motion artifacts according to all 3 readers, and better lymph node border delineation according to 2 readers. Jensen et al evaluated a 2D convolutional neural network based on the U-Net architecture for the zonal segmentation of the prostate gland on 1.5 T and 3.0 T MRI [ 25 ]. The study reported no significant difference between 3.0 T and 1.5 T MRI scanners in mean Dice similarity coefficients (0.778 ± 0.180 vs. 0.808 ± 0.102, respectively) or mean absolute distances (3.257 ± 1.665 mm vs. 3.431 ± 1.782 mm, respectively) of the central gland and no significant difference in Dice similarity coefficients (0.690 ± 0.148 vs. 0.694 ± 0.183, respectively) or mean absolute distances (3.000 ± 1.351 mm vs. 2.985 ± 1.59 mm, respectively) of the peripheral zones ( p > 0.05).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of 3.0 T versus 1.5 T MRI in staging prostate cancer: systematic review and meta-analysis

Virarkar¹,

Szklaruk²,

Diab³

et al. 2022

Pol J Radiol

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Purpose To compare the diagnostic performance of 3.0 T and 1.5 T MRI in the staging of prostate cancer. Material and methods English-language studies on the diagnostic accuracy of 3.0 T and 1.5 T MRI in prostate cancer staging published through May 2020 were searched for in relevant databases. The focus was on studies in which both 3.0 T and 1.5 T MRI were performed in the study population, to reduce interstudy heterogeneity. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve were determined for 3.0 T and for 1.5 T along with 95% confidence intervals (CIs). Results Out of 8 studies identified, 4 met the inclusion criteria. 3.0 T ( n = 160) had a pooled sensitivity of 69.5% (95% CI: 56.4-80.1%) and a pooled specificity of 48.8% (95% CI: 6.0-93.4%), while 1.5 T ( n = 139) had a pooled sensitivity of 70.6% (95% CI: 55.0-82.5%; p = 0.91) and a pooled specificity of 41.7% (95% CI: 6.2-88.6%; p = 0.88). The pooled DOR for 3.0 T was 3 (95% CI: 0-26.0%), while the pooled DOR for 1.5 T was 2 (95% CI: 0-18.0%), which was not a significant difference ( p = 0.89). Conclusions 3.0 T has slightly better diagnostic performance than 1.5 T MRI in prostate cancer staging (3 vs. 2), although without statistical significance. Our findings suggest the need for larger, randomized trials directly comparing 3.0 T and 1.5 T MRI in prostate cancer.

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Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network

Cited by 14 publications

References 39 publications

A review of artificial intelligence in prostate cancer detection on imaging

A review of artificial intelligence in prostate cancer detection on imaging

Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature

Diagnostic value of 3.0 T versus 1.5 T MRI in staging prostate cancer: systematic review and meta-analysis

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