ArticleThe neurologist2025
Diffusion-Weighted Imaging-Based Radiomics Features and Machine Learning Method to Predict the 90-Day Prognosis in Patients With Acute Ischemic Stroke.
Article in The neurologist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Radiomic Features Extracted from Magnetic Resonance Imaging to Identify Arteriopathy in a Humanized Mouse Model of Sickle Cell Anemia and Sickle Cell Trait.Annals of biomedical engineering · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great significance in clinical practice. We aim to evaluate the feasibility and effectiveness of diffusion-weighted imaging (DWI) image-based radiomics features and machine learning methods in predicting 90-day prognosis among patients with AIS. PATIENTS AND
methodsWe enrolled a total of 171 patients with AIS in this study, including 134 patients with a good prognosis and 37 patients with a poor prognosis, and collected the patients' clinical and DWI image data. Radiomics features from manually sketched ischemic lesions were extracted using the Pyradiomics package of Python, and the best radiomics features were selected by a t test and the least absolute shrinkage and selection operator. The radiomics model and clinical model were constructed using support vector machine and logistic regression, respectively, and the predictive performance of each model was evaluated.
resultsWe selected 9 features from a total of 851 radiomics features to build the final radiomics model. For predicting the poor prognosis of patients with AIS, the area under the curves, accuracy, sensitivity and specificity of the clinical model, radiomics model in the training set and radiomics model in the testing set were 0.865, 0.930 and 0.906, 81.3%, 92.0% and 90.0%, 81.1%, 76.0% and 75.0%, and 81.3%, 97.0% and 95.0%, respectively.
conclusionsDWI image-based radiomics features and machine learning methods can accurately predict the 90-day prognosis of patients with AIS, and the radiomics model is superior to the clinical model in predicting prognosis.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.