ArticleCancer imaging : the official publication of the International Cancer Imaging Society2022
Detection of prostate cancer using diffusion-relaxation correlation spectrum imaging with support vector machine model - a feasibility study.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Informed Dictionary-Guided Monte Carlo Inversion for Robust and Reproducible Multidimensional MRI.Magnetic resonance in medicine · 2026Article
- Exploration of prognostic prediction models for renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging.BMC medical imaging · 2026Article
- Data Processing in Multidimensional MRI For Biomarker Identification: Is It Necessary?bioRxiv : the preprint server for biology · 2025Article
- Multidimensional MRI reveals cortical astrogliosis linked to dementia in Alzheimer's disease.Brain communications · 2025Article
- MRI-based virtual pathology of the prostate.Magma (New York, N.Y.) · 2024Review
- In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI.Human brain mapping · 2024Article
- Investigation of clear cell renal cell carcinoma grades using diffusion-relaxation correlation spectroscopic imaging with optimized spatial-spectrum analysis.The British journal of radiology · 2024Article
- Variability of multidimensional diffusion-relaxation MRI estimates in the human brain.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Article
- Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.Frontiers in endocrinology · 2023Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundTo evaluate the performance of diffusion-relaxation correlation spectrum imaging (DR-CSI) with support vector machine (SVM) in detecting prostate cancer (PCa).
methodsIn total, 114 patients (mean age, 66 years, range, 48-87 years) who received a prostate MRI and underwent biopsy were enrolled in three stages. Thirty-nine were assigned for the exploration stage to establish the model, 18 for the validation stage to choose the appropriate scale for mapping and 57 for the test stage to compare the diagnostic performance of the DR-CSI and PI-RADS.
resultsIn the exploration stage, the DR-CSI model was established and performed better than the ADC and T
conclusionsDR-CSI combined with the SVM model may improve the diagnostic accuracy of PCa.
trial registrationThis study was approved by the Ethics Committee of our institute (Approval No. KY2018-213). Written informed consent was obtained from all participants.
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