Spatial dose-distribution-based risk mapping to predict moist desquamation in breast radiotherapy
Bibliographic record
Abstract
Abstract Objective. A relationship between the regional spatial distribution of skin dose and the development of moist desquamation (MD) was established for patients treated with breast radiotherapy. Approach. A 56-patient dataset was used to develop and validate a dose-distance based metric to predict MD. Dose distributions for the skin were extracted from AcurosXB treatment plans, and patient reported outcomes were used to classify the incidence of MD across the whole breast and then more specifically in the inferior breast. The sensitivity and specificity of the metric was compared against dose-area (A38 Gy ⩽ 50 cm 2 ) and dose-volume (V105% ⩽ 2% of the breast volume) predictive metrics with the same dataset. Main results. With a sensitivity of 70% and a specificity of 72%, the dose-distance metric outperformed the dose-area (45%, 55%) and dose-volume (43%, 56%) predictive metrics. The test performance improves to a sensitivity and specificity of 81% when excluding the full coverage breast support devices that confounded the skin dose identification in the analysis. Significance. This metric offers regional MD prediction and risk mapping to highlight regions at high risk of developing severe skin toxicity and is suitable for implementation within the treatment planning process. This work is based on data acquired for the following clinical trials: ClinicalTrials.gov NCT04543851 and NCT04257396.
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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 | 0.001 |
| 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".