Impact of daily plan adaptation on organ-at-risk normal tissue complication probability for adrenal lesions undergoing stereotactic ablative radiation therapy
Bibliographic record
Abstract
INTRODUCTION: Stereotactic ablative radiotherapy (SABR) can achieve good local control for metastatic adrenal lesions. Magnetic resonance (MR)-guidance with daily on-table plan adaptation can augment the delivery of SABR with greater dose certainty. The goal of this study was to quantify the potential clinical benefit MR-guided daily-adaptive adrenal SABR using the normal tissue complication probability (NTCP) framework. METHODS: Patients treated with adrenal MR-guided SABR at a single institution were retrospectively reviewed. Lyman-Kutcher-Burman NTCP models were used to calculate the NTCP of upper abdominal organs-at-risk (OARs) at simulation and both before and after daily on-table plan adaptation. Differences in OAR NTCPs were assessed using signed-rank tests. Potential predictors of the benefits of adaptation were assessed by linear regression. RESULTS: Fifty-two adrenal MR-guided SABR courses were analyzed. The baseline simulation plan underestimated the absolute stomach NTCP by 10.0% on average (95% confidence interval: 4.7-15.2%, p < 0.001). Daily on-table adaptation lowered absolute NTCP by 8.7% (4.2-13.2%, p < 0.001). The most significant predictor of the benefits of adaptation was lesion laterality (p = 0.018), with left-sided lesions benefitting more (13.3% [6.3-20.4%], p < 0.001) than right-sided lesions (2.1% [-1.6-5.7%], p = 0.25). Sensitivity analyses did not change the statistical significance of the findings. CONCLUSION: NTCP analysis revealed that patients with left adrenal tumors were more likely to benefit from MR-guided daily on-table adaptive SABR using current dose/fractionation regimens due to reductions in predicted gastric toxicity. Right-sided adrenal lesions may be considered for dose escalation due to low predicted NTCP.
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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".