Combining 3′-Deoxy-3′-[18F] fluorothymidine and MRI increases the sensitivity of glioma volume detection
Bibliographic record
Abstract
OBJECTIVE: 3'-Deoxy-3'-[18F] fluorothymidine (18F-FLT) is a marker of cell proliferation and displays a high tumor-to-background ratio in brain tumor lesions. We determined whether combining 18F-FLT PET and MRI study improves the detection of tumoral tissue compared to MRI alone and whether 18F-FLT uptake has a prognostic value by studying its association with histopathological features. METHODS: Thirteen patients with a supratentorial malignant glioma were recruited and scheduled for surgery. The tumor volume was defined in all patients on both 18F-FLT PET and MRI images. The images were coregistered and uploaded onto a neuronavigation system. During surgery, an average of 11 biopsies per patient were taken in regions of the brain that were positive to one or both imaging modalities, as well as from control peritumoral regions. The standardized uptake values (SUVs) of each biopsy region were correlated to histopathological data (i.e., proliferation index and number of mitoses) and the SUV values of high and low-grade samples were compared. RESULTS: Out of a total of 149 biopsies, 109 contained tumoral tissue at histopathological analysis. The positive predictive value was 93.1% for MRI alone and 78.3% for MRI and PET combined. In addition, 40% of the biopsy samples taken from areas of the brain that were negative at both PET and MRI had evidence of malignancy at pathology. The SUV values were not significantly correlated to either the proliferation index or the number of mitoses, and could not differentiate between high- and low-grade samples. CONCLUSION: In patients with newly diagnosed glioma, a combination of MRI and 18F-FLT-PET detects additional tumoral tissue and this may lead to a more complete surgical resection. Also, the addition of a negative PET to a negative MRI increases the negative predictive value. However, 18F-FLT still underestimated the margins of the lesion and did not correlate with histopathological features.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".