An Effective Primary Treatment Using Radiotherapy in Patients with Eyelid Merkel Cell Carcinoma
Bibliographic record
Abstract
Background: Merkel cell carcinoma (MCC) is a rare type of neuroendocrine tumor. Palpebral localization represents 2.5% of MCCs. Surgery is not always possible due to the localization or comorbidities of elderly patients. We hypothesized that radiotherapy (RT) alone could be a curative treatment in patients contraindicated for oncological surgery. Methods: We performed a retrospective monocentric study of patients with localized eyelid MCC treated with curative intent using curative radiotherapy. Results: Overall, 11 patients with histologically confirmed eyelid MCC were treated with curative radiotherapy. The median age was 77 years old (range: 53–94). Curative RT was decided mainly due to difficult localization and significant co-morbidities. The median lesion dose was 57 Gy (range: 47–70). Most patients had adjuvant lymph nodes irradiation with a median dose of 50 Gy (n = 9; 82%). The median follow-up was 62 months (6–152 months). None of the seven deaths were MCC-related. None of our patients relapsed during follow-up. Side effects related to radiotherapy were mild (no grade ≥ 2) and rare (n = 3, 21%). Conclusion: Our data suggest that curative radiotherapy is an effective and safe treatment for Merkel cell carcinoma of the eyelid and periocular region. Radiotherapy alone allows limiting the aesthetic and functional sequelae in elderly and comorbid patients who are contraindicated for oncological surgery.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".