ArticleRadiology2017
Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer.
Article in Radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Automatic Tumor Segmentation With a Convolutional Neural Network in Multiparametric MRI: Influence of Distortion Correction.Tomography (Ann Arbor, Mich.) · 2019Trial
- Microstructure imaging of prostate cancer by diffusion MRI.Magma (New York, N.Y.) · 2026Review
- Spatial Image Gradient Estimation From the Diffusion MRI Profile.Magnetic resonance in medicine · 2026Article
- Quantitative synthetic MRI improves peripheral zone prostate cancer detection beyond PI-RADS v2.1.International urology and nephrology · 2026Article
- Serial TEuropean radiology · 2026Article
- Multi-parametric MRI synthesis for glioblastoma from quantitative MR fingerprinting: Quantitative synthetic neural network (QS-Net).Medical physics · 2026Article
- Prostate Diffusion-weighted Imaging: Selecting the Optimal Repetition Time Using Synthetic Imaging Techniques.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
- Noninvasive Differentiation of Indolent from Aggressive Renal Neoplasms with MR Fingerprinting Combined with Diffusion/Perfusion MRI.Radiology · 2026Article
- Accelerating 2D Kidney Magnetic Resonance Fingerprinting Using Deep Learning Based Tissue Quantification.Journal of magnetic resonance imaging : JMRI · 2026Article
- Clinical MR Biomarkers.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Spatial image gradient estimation from the diffusion MRI profile.bioRxiv : the preprint server for biology · 2025Article
- Simultaneous liver TMagnetic resonance in medicine · 2025Article
- Synthetic DWI: contrast improvement for diffusion-weighted imaging in prostate using T1 shine-through by synthesizing images with adjusted TR and TE.Magma (New York, N.Y.) · 2025Article
- Quantitative Prostate MRI, From theAJR. American journal of roentgenology · 2025Review
- Repeatability of quantitative MR fingerprinting for TEuropean radiology · 2025Article
- Time-resolved MR fingerprinting for TMagnetic resonance in medicine · 2025Article
- Incorporating Spatial and Spectral Saturation Modules Into MR Fingerprinting.NMR in biomedicine · 2025Article
- Research Progress on Glioma Microenvironment and Invasiveness Utilizing Advanced Multi-Parametric Quantitative MRI.Cancers · 2024Review
- Optimizing prostate biopsy decision-making for patients with Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions: novel magnetic resonance imaging (MRI)-based nomograms.Quantitative imaging in medicine and surgery · 2024Article
- Cancer detection in patients with prostate-specific antigen levels within the grey zone: can synthetic magnetic resonance imaging aid in the differentiation between prostate cancer and noncancerous lesions?Quantitative imaging in medicine and surgery · 2024Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10
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