Radiation therapy for deep periocular cancer treatments when protons are unavailable: is combining electrons and orthovoltage therapy beneficial?
Bibliographic record
Abstract
Deep periocular cancers can be difficult to plan and treat with radiation, given the difficulties in apposing bolus to skin, and the proximity to the retina and other optic structures. We sought to compare the combination of electrons and orthovoltage therapy (OBE) with existing modalities for these lesions. Four cases-a retro-orbital melanoma (Case 1) and basal cell carcinomas, extending across the eyelid (Case 2) or along the medial canthus (Cases 3-4)-were selected for comparison. In each case, radiotherapy plans for electron only, 70% electron and 30% orthovoltage (OBE), volumetric-modulated arc therapy (VMAT), conformal arc, and protons were compared. Dose-volume histograms for planning target volume coverage and selected organs at risk (OARs) were then calculated. The V90% coverage of the planning target volume was >98% for electrons, VMAT, conformal arc and proton plans and 90.2% and 89.5% in OBE plans for Cases 2 and 3, respectively. The retinal V80% was >98% in electron, VMAT and proton plans and 79.4%; and 87.1% in OBE and conformal arcs for Case 2 and 91.3%, 36.4%, 56.9%, 52.4% and 43.7% for Case 3 in electrons, OBE, VMAT, conformal arc and proton plans, respectively. Protons provided superior coverage, homogeneity and OAR sparing, compared with all other modalities. However, given its simplicity and widespread availability, OBE is a potential alternative treatment option for moderately deep lesions where bolus placement is difficult.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".