Multimodal 18 F-Fluciclovine PET/MRI and Ultrasound-Guided Neurosurgery of an Anaplastic Oligodendroglioma
Bibliographic record
Abstract
Structural magnetic resonance imaging (MRI) and histopathologic tissue sampling are routinely performed as part of the diagnostic workup for patients with glioma. Because of the heterogeneous nature of gliomas, there is a risk of undergrading caused by histopathologic sampling errors. MRI has limitations in identifying tumor grade and type, detecting diffuse invasive growth, and separating recurrences from treatment induced changes. Positron emission tomography (PET) can provide quantitative information of cellular activity and metabolism, and may therefore complement MRI. In this report, we present the first patient with brain glioma examined with simultaneous PET/MRI using the amino acid tracer 18F-fluciclovine (18F-FACBC) for intraoperative image-guided surgery. A previously healthy 60-year old woman was admitted to the emergency care with speech difficulties and a mild left-sided hemiparesis. MRI revealed a tumor that was suggestive of glioma. Before surgery, the patient underwent a simultaneous PET/MRI examination. Fused PET/MRI, T1, FLAIR, and intraoperative three-dimensional ultrasound images were used to guide histopathologic tissue sampling and surgical resection. Navigated, image-guided histopathologic samples were compared with PET/MRI image data to assess the additional value of the PET acquisition. Histopathologic analysis showed anaplastic oligodendroglioma in the most malignant parts of the tumor, while several regions were World Health Organization (WHO) grade II. 18F-Fluciclovine uptake was found in parts of the tumor where regional WHO grade, cell proliferation, and cell densities were highest. This finding suggests that PET/MRI with this tracer could be used to improve accuracy in histopathologic tissue sampling and grading, and possibly for guiding treatments targeting the most malignant part of extensive and eloquent gliomas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".