18F-FACBC PET/MRI in Diagnostic Assessment and Neurosurgery of Gliomas
Bibliographic record
Abstract
PURPOSE: This pilot study aimed to evaluate the amino acid tracer F-FACBC with simultaneous PET/MRI in diagnostic assessment and neurosurgery of gliomas. MATERIALS AND METHODS: Eleven patients with suspected primary or recurrent low- or high-grade glioma received an F-FACBC PET/MRI examination before surgery. PET and MRI were used for diagnostic assessment, and for guiding tumor resection and histopathological tissue sampling. PET uptake, tumor-to-background ratios (TBRs), time-activity curves, as well as PET and MRI tumor volumes were evaluated. The sensitivities of lesion detection and to detect glioma tissue were calculated for PET, MRI, and combined PET/MRI with histopathology (biopsies for final diagnosis and additional image-localized biopsies) as reference. RESULTS: Overall sensitivity for lesion detection was 54.5% (95% confidence interval [CI], 23.4-83.3) for PET, 45.5% (95% CI, 16.7-76.6) for contrast-enhanced MRI (MRICE), and 100% (95% CI, 71.5-100.0) for combined PET/MRI, with a significant difference between MRICE and combined PET/MRI (P = 0.031). TBRs increased with tumor grade (P = 0.004) and were stable from 10 minutes post injection. PET tumor volumes enclosed most of the MRICE volumes (>98%) and were generally larger (1.5-2.8 times) than the MRICE volumes. Based on image-localized biopsies, combined PET/MRI demonstrated higher concurrence with malignant findings at histopathology (89.5%) than MRICE (26.3%). CONCLUSIONS: Low- versus high-grade glioma differentiation may be possible with F-FACBC using TBR. F-FACBC PET/MRI outperformed MRICE in lesion detection and in detection of glioma tissue. More research is required to evaluate F-FACBC properties, especially in grade II and III tumors, and for different subtypes of gliomas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".