Prediction of subtle thermal histopathological change using a novel analysis of Gd‐DTPA kinetics
Bibliographic record
Abstract
PURPOSE: To investigate Gd-DTPA kinetics as predictors of histopathological changes following focused ultrasound (FUS) thermal ablation for improved planning and assessment. MATERIALS AND METHODS: Twenty-nine FUS lesions were created in the thigh muscle of eight rabbits under MR-guidance at 1.5 Tesla. Three rabbits were killed at four hours; and 11 lesions were analyzed with histopathology. Temperature-sensitive MRI using proton-resonant frequency-shift was used for time-dependent temperature measurements. Analysis of the uptake kinetics of Gd-DTPA was performed after Gd-DTPA injection, within 20 minutes after heating and again at two hours after heating. The resulting kinetic maps, permeability (K(trans)) and leakage space (v(e)), were correlated to peak temperatures, T(2)-weighted MR, and histopathology. RESULTS: Images of K(trans) and v(e) reveal regions of histopathological change not visible on conventional post-therapy MR. At early times after heating, v(e) predicts the area of injury more accurately than T(2) (7 +/- 2% vs. 25 +/- 6% underestimation). A circular region of extensive structural/vascular disruption is indicated only on K(trans) maps. The sharp decrease in K(trans) at the boundary of this region occurs at 47.5 +/- 0.5 degrees C, and may be a better estimate of cell death than the conventional method of temperature threshold (55 degrees C for coagulation) used in therapy planning. CONCLUSION: Our results suggest Gd-DTPA kinetics can predict different histopathological changes following FUS ablation and may be valuable for early prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".