NIMG-41. VARIATION IN PERFUSION AND PERMEABILITY MRI DEPENDING ON VASCULAR INPUT FUNCTION SELECTION
Bibliographic record
Abstract
Abstract BACKGROUND According to the updated RANO criteria, MRI contrast enhancement and peritumoral edema during the initial 12 weeks within the radiation field following chemoradiation may be pseudoprogression (PP) or true progression (TP). This ambiguity results in diagnostic uncertainty and delays in effective therapies. MRI perfusion and permeability imaging markers, including relative blood volume (rBV), and volume transfer constant (Ktrans), may be useful in differentiating PP from TP. Both techniques rely on identification of a vascular input function (VIF) to produce reliable output maps. METHODS Perfusion and permeability maps were acquired from 7 patients on a Siemens 3T Verio MRI as part of an ongoing prospective study on glioblastoma multiforme. Patients received standard surgical resection with concurrent chemo-radiotherapy treatment and MRI follow-up at one and every 2 months. Maps were based on the follow-up MRI. The 3D contrast enhancing lesion (CEL) was segmented from the T1-Post-Gd MRI. VIF pixels were identified in the internal carotid artery (ICA), the M2 segment of the middle cerebral artery (MCA2), the superior sagittal sinus (SSS), automatically using software (Olea Sphere 3.0.22), and auto-edited (removed pixels from the auto VIF definition that were outside the brain). RESULTS For the different VIF pixel selections (ICA, MCA2, SSS, Auto, Auto-edited), mean Ktrans values from CEL were 0.32, 0.48, 0.05, 0.09, 0.08, respectively, showing a 10-fold variation. Mean rBV values from CEL were 2.70, 2.36, N/A, 3.10, 3.19, respectively, showing a 1.3-fold variation. CONCLUSIONS VIF pixel selection is a critical step in generating reliable perfusion and permeability MRI maps. Variation of up to a factor of 10 in the Ktrans values, depending on VIF selection, was observed. SSS for the permeability VIF resulted in maps that most closely matched literature values, whereas perfusion imaging showed less sensitivity to VIF selection.
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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 | 0.001 |
| 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) | 0.002 | 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".