SynthesisScientific reports2019
Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unveiling the diagnostic potential of diffusion kurtosis imaging and intravoxel incoherent motion for detecting and characterizing prostate cancer: a meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.Radiological physics and technology · 2026Article
- Deep learning network enhances imaging quality of low-b-value diffusion-weighted imaging and improves lesion detection in prostate cancer.BMC cancer · 2025Article
- Comparison of synthesized and acquired high b-value diffusion-weighted MRI for detection of prostate cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Review
- Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.Magma (New York, N.Y.) · 2022Review
- Quantitative diffusion MRI of the abdomen and pelvis.Medical physics · 2022Article
- Diffusion-Weighted MRI in the Genitourinary System.Journal of clinical medicine · 2022Review
- Diffusion and quantification of diffusion of prostate cancer.The British journal of radiology · 2022Review
- Using IVIM Parameters to Differentiate Prostate Cancer and Contralateral Normal Tissue through Fusion of MRI Images with Whole-Mount Pathology Specimen Images by Control Point Registration Method.Diagnostics (Basel, Switzerland) · 2021Article
- The Role of Non-Gaussian Models of Diffusion Weighted MRI in Hepatocellular Carcinoma: A Systematic Review.Journal of clinical medicine · 2021Review
- Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions.Scientific reports · 2021Article
- Assessment of DCE Utility for PCa Diagnosis Using PI-RADS v2.1: Effects on Diagnostic Accuracy and Reproducibility.Diagnostics (Basel, Switzerland) · 2020Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I
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