The Abscopal Effect in Head-and-Neck Squamous Cell Carcinoma Treated with Radiotherapy and Nivolumab: A Case Report and Literature Review
Bibliographic record
Abstract
Introduction: vaccine effect of radiotherapy, the abscopal effect may be augmented by immunotherapy. This report is the first case of the abscopal effect observed in metastatic head-and-neck squamous cell carcinoma (hnscc) treated with concurrent radiotherapy and single-agent nivolumab. Case Description: An otherwise healthy 57-year-old man underwent craniofacial resection and adjuvant chemoradiotherapy for advanced sinonasal squamous cell carcinoma. Distant metastatic disease developed shortly after primary treatment, and immunotherapy in the form of nivolumab was initiated. Subsequent oligometastatic progression despite immunotherapy prompted palliative radiotherapy to a single metastasis due to pending symptomatology. Post-radiotherapy, the abscopal effect was observed with all distant sites of metastatic disease shrinking. Five months following treatment, a sustained reduction in disease burden has been demonstrated. Summary: We present the first case of the abscopal effect in a patient with metastatic hnscc treated with palliative radiotherapy concurrent with single-agent nivolumab immunotherapy, and only the third case of the abscopal effect in metastatic head-and-neck cancer. Dual treatment with immunotherapy and radiotherapy may be an important treatment option in the future, mediated through the abscopal effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".