Role of Radiomics to Predict Disease Recurrence in Sinonasal Squamous Cell Carcinoma: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Radiomics offers the potential to predict oncological outcomes from pre-operative imaging, aiding in the identification of 'high risk' patients with sinonasal cancer who are at an increased risk of recurrence. This study aims to comprehensively review the current literature on the role of radiomics as a predictor of disease recurrence in sinonasal squamous cell carcinoma. METHODS: A systematic search was conducted in Medline, EMBASE and Web of Science databases. Retrospective and prospective studies examining the use of radiomics to predict post-operative recurrence in sinonasal cancer that met the inclusion criteria were included. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score (RQS) tools. RESULTS: Five studies met the inclusion criteria, encompassing 638 participants. All studies were single-centre and utilised MRI-based radiomics in the construction of their models. Radiomic models demonstrated excellent predictive performance. The median AUC, sensitivity and specificity were 0.947, 0.86 and 0.923 in the training set, and 0.914, 0.833 and 0.878 in the validation set. A pooled meta-analysis estimated the combined AUC across training sets as 0.931 (95% CI, 0.898-0.963) and 0.922 (95% CI, 0.880-0.964) for validation sets. CONCLUSION: Our systematic review provides evidence supporting the role of radiomics in predicting post-operative disease recurrence in sinonasal cancer. Radiomics shows promise in enhancing personalised treatment strategies by improving prognostic accuracy. However, further research is needed to standardise methodologies and validate these findings in larger, multicentre cohorts.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".