Prognostic and clinicopathological significance of tumor-stroma ratio in head and neck squamous cell carcinoma: A systematic review
Bibliographic record
Abstract
BACKGROUND: Analysis of the tumor microenvironment has been proposed as a strategy for the treatment and prognosis of different neoplastic processes. A grading system based on the tumor-stroma ratio (TSR), which evaluates the proportion of stroma in relation to neoplastic parenchyma at the invasion front, has shown a strong prognostic value in different neoplastic processes. The aim of the present systematic review was to understand the role of the TSR in head and neck squamous cell carcinoma (HNSCC), evaluating its correlation with clinical and prognostic parameters. MATERIAL AND METHODS: An electronic search was performed in PubMed/Medline, Web of Science, Science Direct, Scopus, Embase, and the Cochrane Collaboration Library. Publications assessing the relationship between TSR and prognosis in cases of HNSCC were eligible. The quality of the studies was assessed independently by four evaluators using the Newcastle-Ottawa scale. RESULTS: After application of the previously es+lished inclusion/exclusion criteria, nine articles were included in the qualitative synthesis. With regards to quality on the Newcastle-Ottawa scale, an overall value of 4.55 was obtained. This systematic review demonstrated a strong association between TSR and prognosis in esophageal and oral squamous cell carcinomas. CONCLUSIONS: Histopathological analysis of the TSR can optimize the analysis of the prognosis of cases diagnosed with HNSSC. In addition, the TSR is a reliable and simple parameter that can be evaluated in hematoxylin/eosin-stained slides during routine laboratory examinations, showing high inter- and intraobserver agreement.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".