TH‐C‐141‐06: Estimating Cell Density Using Fractional Anisotropy From Postoperative Diffusion Tensor Imaging of High‐Grade Gliomas
Bibliographic record
Abstract
Purpose: Fractional anisotropy (FA) from diffusion tensor imaging (DTI) has been suggested as a predictor of glioma cell density and proliferation activity. The purpose of this study is estimate the glioma cell density inside high‐grade glioma radiotherapy target volumes using FA images. Methods: Five patients with histologically‐confirmed glioma underwent radiotherapy planning with postoperative magnetic resonance imaging (MRI) and computed tomography. T1‐weighted images with gadolinium contrast enhancement and T2‐weighted fluid attenuated inversion recovery images used for treatment planning were obtained. DTI was obtained using echo planar imaging for 20 noncolinear directions with b=1000 s/mm2 and one additional image with b=0. Diffusion imaging was repeated four times for signal averaging. The gross tumor volume (GTV) was delineated using T1‐weighted and T2‐weighted images. A clinical target volume (CTV) was defined as a 2‐cm expansion of the GTV and a shell volume (CTV‐shell) between the GTV and CTV contours was created. The distribution of FA inside the GTV, CTV‐shell, and normal brain tissue was calculated. Cell density inside the CTV was estimated from FA values using a linear model. Results: The mean FA inside the GTV was 0.13±0.08 and inside CTV‐shell was 0.20±0.12. The mean FA in normal brain tissue (0.28±0.12) was significantly higher than the mean FA inside the GTV (p=0.003) and CTV‐shell (p=0.01). The estimated mean cell density inside CTV‐shell was 1.57±0.94 times the mean cell density inside the GTV (p=0.02). Conclusion: FA values in the GTV and CTV‐shell were significantly smaller than values in normal brain regions. FA and estimated cell density values approached those of normal brain tissue as the distance from the GTV increased, consistent with the expectation of a gradual and decreasing presence of tumor cells. Further research is warranted to determine if treatment planning using FA images will improve treatment outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".