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References from Radiomics and deep learning for intracranial aneurysm rupture-status assessment on CTA/MRA: a multicenter retrospective study. Local targets link to admitted publications; unresolved targets remain external evidence.
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
10.1038/ncomms5006 · 2014 · External reference
Unresolved reference
2016 · External reference
Deep learning–based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses
10.1148/radiol.2019181960 · 2019 · External reference
Prediction of intracranial aneurysm rupture from computed tomography angiography using an automated artificial intelligence framework
10.1016/j.compbiomed.2025.110965 · 2025 · External reference
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
10.1007/s00062-024-01474-4 · 2025 · External reference
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
10.2307/2531595 · 1988 · External reference
The unruptured intracranial aneurysm treatment score: a multidisciplinary consensus
10.1212/wnl.0000000000001891 · 2015 · External reference
External validation of the PHASES score in patients with multiple intracranial aneurysms
10.1016/j.jstrokecerebrovasdis.2021.105643 · 2021 · External reference
Automatic differentiation of ruptured and unruptured intracranial aneurysms on computed tomography angiography based on deep learning and radiomics
10.1186/s13244-023-01423-8 · 2023 · External reference
Radiomics: images are more than pictures, they are data
10.1148/radiol.2015151169 · 2016 · External reference
Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies
10.1016/s1474-4422(13)70263-1 · 2014 · External reference
Deep learning-driven multimodal fusion model for prediction of middle cerebral artery aneurysm rupture risk
10.1016/j.acra.2025.07.001 · 2025 · External reference
Risk factors for intracranial aneurysm rupture: a systematic review
10.1093/neuros/nyx238 · 2018 · External reference
Lifelong rupture risk of intracranial aneurysms depends on risk factors: a prospective Finnish cohort study
10.1161/strokeaha.114.005318 · 2014 · External reference
Radiomics: the process and the challenges
10.1016/j.mri.2012.06.010 · 2012 · External reference
Radiomics: the bridge between medical imaging and personalized medicine
10.1038/nrclinonc.2017.141 · 2017 · External reference
A deep-learning method for the end-to-end prediction of intracranial aneurysm rupture risk
10.1016/j.patter.2023.100709 · 2023 · External reference
Unresolved reference
2017 · External reference
Measuring computed tomography scanner variability of radiomics features
10.1097/rli.0000000000000180 · 2015 · External reference
The unruptured intracranial aneurysm treatment score as a predictor of aneurysm growth or rupture
10.1111/ene.14636 · 2021 · External reference
Validation of the unruptured intracranial aneurysm treatment score: comparison with real-world cerebrovascular practice
10.3171/2017.4.jns17548 · 2018 · External reference
Deep learning–based detection of intracranial aneurysms in 3D TOF-MRA
10.3174/ajnr.a5911 · 2019 · External reference
Accuracy of radiomics-based models in distinguishing between ruptured and unruptured intracranial aneurysms: a systematic review and meta-analysis
10.1016/j.ejrad.2024.111739 · 2024 · External reference
Machine learning classification of cerebral aneurysm rupture status with morphologic variables and hemodynamic parameters
10.1148/ryai.2019190077 · 2020 · External reference
Guidelines for the management of patients with unruptured intracranial aneurysms: a guideline for healthcare professionals from the American Heart Association/American Stroke Association
10.1161/str.0000000000000070 · 2015 · External reference
The biological meaning of radiomic features
10.1148/radiol.2021202553 · 2021 · External reference
Repeatability and reproducibility of radiomic features: a systematic review
10.1016/j.ijrobp.2018.05.053 · 2018 · External reference
Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor
10.1007/s00330-023-09672-3 · 2023 · External reference
Computational radiomics system to decode the radiographic phenotype
10.1158/0008-5472.can-17-0339 · 2017 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis
10.3389/fnins.2024.1474780 · 2024 · External reference
Automatic risk prediction of intracranial aneurysm on CTA image with convolutional neural networks and radiomics analysis
10.3389/fneur.2023.1126949 · 2023 · External reference
Prediction of intracranial aneurysm rupture risk using non-invasive radiomics analysis based on follow-up magnetic resonance angiography images: a preliminary study
10.3390/app12178615 · 2022 · External reference
Comparison of ruptured intracranial aneurysms identification using different machine learning algorithms and radiomics
10.3390/diagnostics13162627 · 2023 · External reference
Prediction of small intracranial aneurysm rupture status based on combined clinical–radiomics model
10.1016/j.heliyon.2024.e30214 · 2024 · External reference
User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability
10.1016/j.neuroimage.2006.01.015 · 2006 · External reference
The accuracy in rupture risk prediction of intracranial aneurysms by artificial intelligence algorithms using imaging data from CTA and DSA: a systematic review and meta-analysis
10.1049/syb2.70050 · 2026 · External reference
Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis
10.1007/s10143-024-03086-5 · 2024 · External reference
The image biomarker standardisation initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
