The Impact of Histopathological Features on the Prognosis of Oral Squamous Cell Carcinoma: A Comprehensive Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Over many decades, studies on histopathological features have not only presented high-level evidence of contribution for treatment directions and prognosis of oral squamous cell carcinoma (OSCC) but also provided inconsistencies, making clinical application difficult. The 8th TNM staging system of OSCC has acknowledged the importance of some histopathological features, by incorporating depth of invasion (DOI) to T category and extranodal extension (ENE) to N category. The aim of this systematic review with meta-analysis is to determine the most clinically relevant histopathological features for risk assessment and treatment planning of OSCC and to elucidate gaps in the literature. METHODS: A systematic review was conducted using PRISMA guidelines, and the eligibility criteria were based on population, exposure, comparison, outcome, and study type (PECOS). PubMed, Cochrane, Scopus, and Web of Science were searched for articles exploring the impact of histopathological features on OSCC outcomes with Cox multivariate analysis. Pooled data were subjected to an inverse variance method with random effects or fixed effect model, and the risk of bias was evaluated using quality in prognosis studies (QUIPS). Quality of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria. RESULTS: The study included 172 articles published from 1999 to 2021. Meta-analyses confirmed the prognostic potential of DOI, ENE, perineural invasion, lymphovascular invasion, and involvement of the surgical margins and brought promising results for the association of bone invasion, tumor thickness, and pattern of invasion with increased risk for poor survival. Although with a small number of studies, the results also revealed a clinical significance of tumor budding and tumor-stroma ratio on predicted survival of patients with OSCC. Most of the studies were considered with low or moderate risk of bias, and the certainty in evidence varied from very low to high. CONCLUSION: Our results confirm the potential prognostic usefulness of many histopathological features and highlight the promising results of others; however, further studies are advised to apply consistent designs, filling in the literature gaps to the pertinence of histopathological markers for OSCC prognosis. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO), identifier CRD42020219630.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".