ArticleScientific reports2020
A radiomics-based comparative study on arterial spin labeling and dynamic susceptibility contrast perfusion-weighted imaging in gliomas.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis.Contrast media & molecular imaging · 2020Pooled it
- Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.Neuroradiology · 2026Article
- Nano-radiomics of molecular MRI for early amyloid associated patterns in an Alzheimer's disease mouse model via an automatic pipeline.BMC medical imaging · 2026Article
- Diffuse glioma perfusion quantification with ASL and DSC: a head-to-head comparison withFrontiers in oncology · 2026Article
- Explainable incremental-value analysis of apparent diffusion coefficient and arterial spin labeling radiomics for ATRX status prediction in glioblastoma.Frontiers in oncology · 2026Article
- Differentiating tumor recurrence and pseudoprogression in postoperative gliomas using pseudo-continuous arterial spin labeling (pCASL) technique.BMC medical imaging · 2025Article
- Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2025Review
- A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof-of-Concept Study.Life (Basel, Switzerland) · 2025Article
- The Value of Cerebral Blood Volume Derived from Dynamic Susceptibility Contrast Perfusion MRI in Predicting IDH Mutation Status of Brain Gliomas-A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset.Clinical neuroradiology · 2025Article
- Endoscopic ultrasonography-based intratumoral and peritumoral machine learning ultrasomics model for predicting the pathological grading of pancreatic neuroendocrine tumors.BMC medical imaging · 2025Article
- Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?Computational and structural biotechnology journal · 2024Article
- Comparison of ASL and DSC perfusion methods in the evaluation of response to treatment in patients with a history of treatment for malignant brain tumor.BMC medical imaging · 2024Article
- Review
- Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.Journal of magnetic resonance imaging : JMRI · 2023Review
- Diffusion-weighted imaging and arterial spin labeling radiomics features may improve differentiation between radiation-induced brain injury and glioma recurrence.European radiology · 2023Article
- RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas.Cancers · 2022Article
- Hemodynamic Imaging in Cerebral Diffuse Glioma-Part A: Concept, Differential Diagnosis and Tumor Grading.Cancers · 2022Review
- Review
- 3D pCASL-perfusion in preoperative assessment of brain gliomas in large cohort of patients.Scientific reports · 2022Article
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Abstract
Radiomics has potential for reflecting the differences in glioma perfusion heterogeneity between arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) imaging. The aim of this study was to compare radiomic features of ASL and DSC imaging-derived parameters (cerebral blood flow, CBF) and assess radiomics-based classification models for low-grade gliomas (LGGs) and high-grade gliomas (HGGs) using their parameters. The ASL-CBF and DSC-relative CBF of 46 glioma patients were normalized (ASL-nCBF and DSC-nrCBF) for data analysis. For each map, 91 radiomic features were extracted from the tumor volume. Seventy-five radiomic features were significantly different (P < 0.00055) between ASL-nCBF and DSC-nrCBF. Positive correlations were observed in 75 radiomic features between ASL-nCBF and DSC-nrCBF. Even though ASL imaging underestimated CBF compared with DSC imaging, there were significant correlations (P < 0.00055) in the first-order-based mean, median, 90
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