Personalizing Elective Nodal Irradiation in Head and Neck Squamous Cell Carcinoma: A Systematic Review of SPECT/CT-Guided Sentinel Lymph Node Mapping
Bibliographic record
Abstract
Bilateral elective nodal irradiation (ENI) remains standard for treating most head and neck squamous cell carcinomas (HNSCC) but is associated with significant toxicity. Advances in lymphatic mapping, particularly with SPECT/CT-guided sentinel lymph node (SLN) identification, have enabled more personalized radiotherapy strategies. This systematic review evaluates the efficacy and quality-of-life impact of ENI strategies using SPECT/CT-guided SLN mapping. This systematic review, conducted according to PRISMA guidelines, included ten studies published between January 2014 and March 2024, including prospective, retrospective studies, randomized trials, and systematic reviews, examining oncologic outcomes and toxicity in patients undergoing SPECT/CT-guided SLN mapping or individualized ENI. Findings show that in well-lateralized, early stage carcinomas, SPECT/CT-guided ENI safely allows for unilateral treatment in up to 82% of patients, with a low contralateral regional failure rate. This approach significantly reduces radiation exposure to organs at risk and rates of xerostomia, dysphagia, and hypothyroidism, leading to improved quality of life. However, its applicability to advanced or midline tumors remains limited. SPECT/CT-guided SLN mapping and individualized ENI offer a promising, less toxic alternative for selected patients. Further prospective, multicenter, and randomized studies are needed to confirm these benefits and support broader clinical adoption.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".