Usefulness of contrast kinetics for predicting and monitoring tissue changes in muscle following thermal therapy in long survival studies
Bibliographic record
Abstract
PURPOSE: To investigate Gd-DTPA kinetics as indicators of subacute and subchronic histopathological changes following focused ultrasound (FUS) thermal therapy for improved evaluation. MATERIALS AND METHODS: A total of 18 FUS lesions were created in the thigh muscle of five rabbits under magnetic resonance (MR) guidance at 1.5 Tesla. The rabbits were killed at different times: 40 hours, three days, and seven days. All lesions were analyzed histologically. An analysis of the uptake kinetics of Gd-DTPA, injected within two hours postheating and before sacrifice, was performed. The resulting kinetic maps, permeability (K(trans)) and leakage space (v(e)), were correlated to T(2)-weighted MR and histology. RESULTS: Images of K(trans) and v(e) better differentiate subacute and subchronic changes not visible on conventional MR in the days following therapy and are consistent with the histopathology observed. In particular, the border between nonviable and viable tissue is well demarcated. The extent of damage is best indicated on v(e), whereas the borders of inflammation are shown on K(trans). The total lesion extent is relatively stable over the 7 days posttherapy and can be predicted by v(e) or T(2)-weighted MR at early times after heating. CONCLUSION: Our results suggest that Gd-DTPA kinetics can complement conventional MR for improved evaluation of FUS thermal therapy by providing finer differentiation of necrotic states, inflammation, and repair processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".