ArticleAging2021
Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- The performance of artificial intelligence in image-based prediction of hematoma enlargement: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.Neuroradiology · 2024Pooled it
- Predictive value of radiomics for intracranial aneurysm rupture: a systematic review and meta-analysis.Frontiers in neuroscience · 2024Pooled it
- Severe neonatal complications and long-term health-related quality of life in very preterm and/or very low birth weight survivors: evidence from the Dutch Project on preterm and small for gestational age cohort.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- A dual-center study: can ultrasound radiomics differentiate type I and type II epithelial ovarian cancer patients with normal CA125 levels?The British journal of radiology · 2024Article
- Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approachDiagnostic and interventional radiology (Ankara, Turkey) · 2024Article
- Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.Scientific reports · 2024Article
- The Added Value of Intraventricular Hemorrhage on the Radiomics Analysis for the Prediction of Hematoma Expansion of Spontaneous Intracerebral Hemorrhage.Diagnostics (Basel, Switzerland) · 2022Article
- Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer.The British journal of radiology · 2022Article
- Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.Neurosurgical review · 2022Article
- A data-driven health index for neonatal morbidities.iScience · 2022Article
- Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We constructed a radiomics-clinical model to predict intraventricular hemorrhage (IVH) growth after spontaneous intracerebral hematoma. The model was developed using a training cohort (N=626) and validated with an independent testing cohort (N=270). Radiomics features and clinical predictors were selected using the least absolute shrinkage and selection operator (LASSO) method and multivariate analysis. The radiomics score (Rad-score) was calculated through linear combination of selected features multiplied by their respective LASSO coefficients. The support vector machine (SVM) method was used to construct the model. IVH growth was experienced by 13.4% and 13.7% of patients in the training and testing cohorts, respectively. The Rad-score was associated with severe IVH and poor outcome. Independent predictors of IVH growth included hypercholesterolemia (odds ratio [OR], 0.12 [95%CI, 0.02-0.90]; p=0.039), baseline Graeb score (OR, 1.26 [95%CI, 1.16-1.36]; p<0.001), time to initial CT (OR, 0.70 [95%CI, 0.58-0.86]; p<0.001), international normalized ratio (OR, 4.27 [95%CI, 1.40, 13.0]; p=0.011), and Rad-score (OR, 2.3 [95%CI, 1.6-3.3]; p<0.001). In the training cohort, the model achieved an AUC of 0.78, sensitivity of 0.83, and specificity of 0.66. In the testing cohort, AUC, sensitivity, and specificity were 0.71, 0.81, and 0.64, respectively. This radiomics-clinical model thus has the potential to predict IVH growth.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.