Modeling the involution of microwave liver ablation zones
Bibliographic record
Abstract
Background The post-ablation involution of microwave liver ablation zones (AZs) remains poorly understood. This study develops mathematical models to characterize AZ involution and identifies key predictors influencing its dynamics.Materials and methods Fifty-four patients (mean age 61 ± 10 years (standard deviation), 33 men) underwent microwave liver ablation (MWA) of 76 liver tumors and follow-up contrast enhanced CT (CECT) imaging in this retrospective single-center cohort study. AZs were segmented on intraprocedural post-ablation portal-venous phase CECT and all available subsequent postprocedural follow-up scans, or until local tumor progression (LTP). Volumetric AZ involution was modeled using non-linear regression methods and correlated with initial tumor and ablation parameters.Results In total, 366 AZ segmentations were performed over median 304 days CECT-follow-up (range 21–741). Involution was best modeled by mono-exponential decay (SSE = 4.64, RMSE = 0.11). AZs shrank to one-third of baseline volume within a year, with a half-life of 158 days. At 6 weeks, relative volume was 0.81 of baseline (95% prediction interval 0.59–1.04, 95% confidence interval 0.80–0.83). Variables with a significant effect on involution included initial tumor diameter (p = 0.03), initial AZ volume (p < 0.01), and tumor:AZ volume ratio (p = 0.04).Conclusions Microwave ablation zones rapidly involute and stabilize at approximately one-third of their baseline volume within a year. The involution process is best modeled by mono-exponential decay and influenced by the type of tissue ablated. These findings highlight the potential need for predictive models to adjust for involution for follow-up imaging-based margin assessment to optimize accuracy and ablation outcomes.
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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".