Reliability of CT, DECT, and MRI for the diagnosis of hemorrhagic transformation after thrombectomy
Bibliographic record
Abstract
INTRODUCTION: Computed Tomography (CT) is the main modality used for the diagnosis and classification of hemorrhagic transformation (HT) after thrombectomy, however its reliability has shown limitations. Dual-energy CT (DECT) and magnetic resonance imaging (MRI) have been suggested to enhance the reliability of HT detection and classification, but direct three-way comparison of these modalities is lacking. To measure and compare the reliability of CT, DECT and MRI for the diagnosis, classification, and therapeutic consequences of HT after thrombectomy. PATIENTS AND METHODS: Between June 2017 and September 2019, 66 of 324 patients included in the BP-TARGET trial underwent CT, DECT and MRI scans within 36 h after thrombectomy. Seven readers, including three neurologists, two diagnostic, and two interventional neuroradiologists independently reviewed the images. They were asked for each patient and each imaging modality to score the presence of a hemorrhagic transformation (of any type), the type of hemorrhagic transformation according to the European Cooperative Acute Stroke Study (ECASS), and whether they would start the patient on antiplatelet based on the imaging finding. The readers repeated the same readings 1 month later. Interrater and intrarater agreement were measured using Kappa statistics. RESULTS: There were frequent discrepancies between CT, DECT and MRI scans evaluations. The use of MRI led to an increased rate of HT diagnosis compared to CT and DECT scans. Interrater agreement for ECASS classification was only fair-to-moderate for all three imaging modalities but improved to a substantial level after dichotomization into 0/HI1/HI2 versus PH1/PH2. The interrater agreement for the decision to start antiplatelet therapy was substantial only with CT (κ = 0.636 [0.577-0.694]) and remained moderate with MRI and DECT. CONCLUSION: In our study, the imaging modality influenced the diagnosis and classification of HT, the management of antiplatelet therapy, and the interrater and intrarater agreement. These findings may guide the choice of imaging modality in research or clinical settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".