Lymph node metastasis in oral squamous cell carcinoma: Where we are and where we are going
Bibliographic record
Abstract
Abstract Background Oral cancer is a common malignant tumour in the head and neck region, with over 90% of cases being oral squamous cell carcinoma (OSCC). Lymph node metastasis (LNM) is a significant adverse prognostic factor in OSCC; however, the mechanism of LNM in OSCC remains unclear, while the strategies for managing cervical lymph nodes (CLNs) in OSCC continue to evolve. At present, neck dissection is necessary for the vast majority of OSCC patients, but complications of surgery can significantly impair patients’ postoperative quality of life. A recent clinical trial discovery indicates that immunotherapy can activate anti‐tumour T cells in nearby lymph nodes, leading to vibrant discussions among clinicians about the importance of preserving lymph nodes during surgical treatment. Aim This review aims to provide an overview of where we are: the current knowledge of OSCC LNM patterns and management strategies, and where we are going: prospects for clinical and basic research on the issue of OSCC LNM in the era of cancer immunotherapy. Result We summarize the general patterns and management strategies of OSCC LNM. The process of LNM in OSCC encompasses four stages: Preparation, Unleash, Migration, and Planting (‘PUMP’ principle). We propose adopting the ‘PRECISE’ model, an overall workflow that includes seven elements—prevention, radiology, preoperative evaluation, chemotherapy, immunotherapy, surgery, and postoperative evaluation—for neck management in OSCC, promoting personalized precision medicine augmented by neoadjuvant therapy. We further discussed recent advances in clinical and basic research on OSCC LNM and provided an outlook on future research directions. Conclusion Over the past two centuries, our understanding and management strategies of LNM in OSCC have evolved, seeking more precise and personalized approaches to reduce patient burden and enhance survival and quality of life. Further research should explore lymph nodes’ role in cancer immunotherapy and OSCC interactions, leading to better, less invasive treatments and improved outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.000 | 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".