Research progress on the value of CT and MRI in predicting hemorrhagic transformation after acute ischemic stroke
Bibliographic record
Abstract
Cerebral infarction is the second leading cause of death in the world, and has become the most serious cause of disability and death in China. Acute ischemic stroke (AIS) is the most common type of cerebral infarction, accounting for about 80% of all cerebral infarctions. Hemorrhagic transformation (HT) is one of the natural regression processes in patients with AIS, and is the most serious complication after treatments [such as intravenous thrombolysis (IVT) or endovascular thrombectomy (EVT)], which brings a heavy burden for patients and their families and even the all society. Accurate prediction and evaluation are of important clinical significance. In recent years, imaging research has focused on the value of CT and MRI in evaluating HT. The diagnostic value of CT plain scan is limited. Before AIS treatment, an early diagnostic score ≤ 7 points in the Alberta Cerebral Infarction Plan is associated with the occurrence of HT (P=0.033), and high-density middle cerebral artery sign is an independent risk factor for the occurrence of HT (OR=10.334). For AIS patients treated with thrombectomy within 2-7 days, dual energy CT scanning at 24 hours after therapy had a high efficacy for prediction of HT occurrence, with a sensitivity of 82.5% and specificity of 100%. CT angiography suggests that patients with high thrombus burden had a higher probability of developing HT (OR=1.28). The incidence of HT in AIS patients with good collateral circulation is low. CT perfusion imaging parameters, including surface permeability, cerebral blood volume, Tmax, etc., have good predictive value for predicting HT occurence. In MRI plain scan, high signal on FLAIR can predict the occurrence of HT. The volume of high signal areas on MRI diffusion-weighted imaging can predict HT, with area under the ROC curve of 0.78.Brush like sign, and micro bleeding lesions on sensitivity weighted MRI indicate the occurrence of HT. Enhanced T1 weighted imaging of MRI shows a significant correlation between brain parenchymal enhancement and HT occurrence (P<0.05). Perfusion weighted imaging of MRI shows a decrease in cerebral blood flow (CBV) in the infarcted area may predict HT occurence. In addition, the CT and MRI image post-processing system RAPID has improved the evaluation efficiency for HT occurrence. In the future, personalized imaging detection methods and processes should set up based on the hardware and local medical conditions of each emergency center for HT management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".