Hemangiopericytoma from meningioma - is diffusion weighted imaging useful in their differentiation?
Bibliographic record
Abstract
Purpose: Hemangiopericytoma and Meningioma appear similar on routine diagnostic imaging. Diffusion weighted images (DWI) has been used to characterize different types of tumors. The purpose of this study was to assess whether DWI can be used to differentiate hemangiopericytoma from meningioma on diagnostic imaging. Materials and Methods: In a retrospective study, our tumor database was analyzed for diagnosis of hemangiopericytoma with DWI available at the time of diagnostic imaging. These patients were then matched based on location and size of the tumor in a ratio of 1 hemangiopericytoma vs. 2 matched meningioma. The minimum and mean Apparent Diffusion Coefficient (ADC) was measured in the tumor and the contralateral Normal Appearing White Matter (NAWM) to calculate a normalized ADC (nADC) as the ratio of the two. The two tumors were also subjectively assessed for their heterogeneity. Results: Seven patients with histopathological diagnosis of hemangiopericytoma were matched based on size and location with 14 patients of meningioma. Primary meningioma were significantly homogeneous (p<0.001) in appearance compared to hemangiopericytomas. Hemangiopericytomas had a higher mean ADC compared to that of meningioma (p<0.001). Conclusion: Hemangiopericytoma showed heterogeneity on DWI and significantly higher ADC compared to that of meningiomas in our small study. This needs to be confirmed in a study with a larger sample size.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".