Sci‐Sat AM(1): Imaging‐01: Tumour and normal tissue T2 and ADC distributions for a mouse model at 9.4T
Bibliographic record
Abstract
Magnetic resonance (MR) measurements of relaxation times and diffusion coefficient in tissue have been demonstrated to be sensitive to biological changes induced by radiation therapy. We are currently using mouse models of human glioblastoma multiforme (GBM) to study tumour response to ionizing radiation by MRI at 9.4T. Utilizing conventional imaging techniques coupled with quantitative measurements of transverse relaxation time (T2) and apparent diffusion coefficient (ADC), we monitor changes during tumour growth and subsequent changes after single-fraction radiotherapy. In addition to tumour parameters, we have measured T2 and ADC in other structures that appear in the same transverse slices as tumour tissue. Here we report the measured distributions of T2 and ADC in tumour and in normal tissues that are likely to be encountered during MR imaging of tumour xenografts in mice, including liver, kidney, fat, skeletal muscle, spinal cord, and brain. Quantitative knowledge of these distributions in normal tissue is important in optimizing the sequences used for imaging of these tissues, and in optimizing continued measurements of T2 and ADC changes. Knowledge of parameter distributions in tumour is important because recent studies have suggested that the T2 and ADC responses after therapy may be the result of large shifts in smaller isolated pockets of tumour, rather than more moderate shifts in T2 and ADC over the whole tumour volume. These distributions provide a baseline measurement of typical distributions in advance of radiation therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".