An effective MRI perfusion threshold based workflow to triage additional 18F-FET PET in posttreatment high grade glioma
Bibliographic record
Abstract
MRI is the preferred method for follow-up imaging of post-treatment WHO grade 3 or 4 gliomas. While positron emission tomography with O-(2-[ 18 F]fluoroethyl)-L-tyrosine) ( 18 F-FET PET) offers higher diagnostic accuracy, its use is limited due to low availability. We propose a sequential, threshold-based workflow to triage patients for additional 18 F-FET PET scans based on MRI dynamic susceptibility contrast (DSC) perfusion-derived rCBV values, to optimize 18 F-FET PET resource allocation. Patients with high-grade gliomas who had undergone standard-of-care treatment and developed new or enlarging contrast-enhancing post-treatment lesions on MRI were included, with a 18 F-FET PET study performed within 4 months of the MRI. Patients were excluded if there were significant changes in lesion size or treatment between the MRI and 18 F-FET PET scan. An rCBV threshold was determined and the performance of a threshold-based imaging workflow was evaluated compared to the gold standard defined here as surgical verification or long-term imaging follow-up without further intervention. Forty-one patients with a total of 49 lesions were included (tumor progression n = 40, treatment-related changes n = 9). Above the rCBV threshold of 2.4, MRI was 100% accurate (21/21 patients) in diagnosing tumor progression. Below the threshold, MRI identified 9 true negatives but produced 19 false negatives. 18 F-FET PET reclassified 18/19 (95%) false negatives resulting in an overall accuracy of 48/49 (98%) for the workflow. Our MRI DSC perfusion rCBV-based threshold workflow for triaging patients for additional 18 F-FET PET imaging in post-treatment high grade glioma has the potential to optimize 18 F-FET PET resource allocation.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".