ArticleEuropean radiology2021
T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
26 citing papers in PubMed, 40 citations in OpenAlex.
- Prospective Validation of an Automated Hybrid Multidimensional MRI Tool for Prostate Cancer Detection Using Targeted Biopsy: Comparison with PI-RADS-based Assessment.Radiology. Imaging cancer · 2025Trial
- Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance.Journal of imaging informatics in medicine · 2026Article
- Development and validation of a risk prediction model for prostate cancer integrating quantitative MRI and clinical data.Frontiers in oncology · 2026Article
- Simultaneous liver TMagnetic resonance in medicine · 2025Article
- Quantitative Prostate MRI, From theAJR. American journal of roentgenology · 2025Review
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- MRI-based virtual pathology of the prostate.Magma (New York, N.Y.) · 2024Review
- Article
- Synthesis of MR fingerprinting information from magnitude-only MR imaging data using a parallelized, multi network U-Net convolutional neural network.Frontiers in radiology · 2024Article
- Imaging in Interventional Radiology: 2043 and Beyond.Radiology · 2023Review
- Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study.Diagnostics (Basel, Switzerland) · 2023Article
- Glucocorticoid nanoformulations relieve chronic pelvic pain syndrome and may alleviate depression in mice.Journal of nanobiotechnology · 2023Article
- Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI.Scientific reports · 2023Article
- Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.European radiology · 2023Article
- Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.European journal of radiology open · 2023Article
- Biparametric MR signal characteristics can predict histopathological measures of prostate cancer.European radiology · 2022Article
- Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T.Scientific reports · 2022Article
- Infiltrative growth pattern of prostate cancer is associated with lower uptake on PSMA PET and reduced diffusion restriction on mpMRI.European journal of nuclear medicine and molecular imaging · 2022Article
- MR fingerprinting of the prostate.Magma (New York, N.Y.) · 2022Review
- Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review.Journal of clinical medicine · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of prostate cancer (PCa) and prostatitis. MATERIALS AND
methodsA retrospective, IRB-approved, HIPAA-compliant cohort consisting of 14 PCa patients who underwent 3 T multiparametric MRI along with T1 and T2 MRF maps prior to radical prostatectomy was used. Correspondences between whole mount specimens and MRI and MRF were manually established. Prostatitis, PCa, and normal peripheral zone (PZ) regions of interest (ROIs) on pathology were segmented for TCRs of epithelium, lumen, and stroma using two U-net deep learning models. Corresponding ROIs were mapped to T2-weighted MRI (T2w), apparent diffusion coefficient (ADC), and T1 and T2 MRF maps. Their correlations with TCRs were computed using Pearson's correlation coefficient (R). Statistically significant differences in means were assessed using one-way ANOVA.
resultsStatistically significant differences (p < 0.01) in means of TCRs and T1 and T2 MRF were observed between PCa, prostatitis, and normal PZ. A negative correlation was observed between T1 and T2 MRF and epithelium (R = - 0.38, - 0.44, p < 0.05) of PCa. T1 MRF was correlated in opposite directions with stroma of PCa and prostatitis (R = 0.35, - 0.44, p < 0.05). T2 MRF was positively correlated with lumen of PCa and prostatitis (R = 0.57, 0.46, p < 0.01). Mean T2 MRF showed significant differences (p < 0.01) between PCa and prostatitis across both transition zone (TZ) and PZ, while mean T1 MRF was significant (p = 0.02) in TZ.
conclusionSignificant associations between MRF (T1 in the TZ and T2 in the PZ) and tissue compartments on corresponding histopathology were observed. KEY POINTS: • Mean T2 MRF measurements and ADC within cancerous regions of interest dropped with increasing ISUP prognostic groups (IPG). • Mean T1 and T2 MRF measurements were significantly different (p < 0.001) across IPGs, prostatitis, and normal peripheral zone (NPZ). • T2 MRF showed stronger correlations in the peripheral zone, while T1 MRF showed stronger correlations in the transition zone with histopathology for prostate cancer.
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