ArticleFrontiers in neuroscience2022
CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Quantitative validation of a CT perfusion-derived corrected CBV index as a surrogate of angiographic collateral status(ASITN/SIR grading) in anterior circulation ischemia.Neurosurgical review · 2026Article
- Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Research progress of artificial intelligence in moyamoya disease.Frontiers in neurology · 2025Review
- Prediction of the mean transit time using machine learning models based on radiomics features from digital subtraction angiography in moyamoya disease or moyamoya syndrome-a development and validation model study.Cardiovascular diagnosis and therapy · 2023Article
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
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11 authors.
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Abstract
Purpose: To build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD). Methods: Fifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC). Results: Of the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group ( Conclusion: The TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.
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