Does Reorganization of Clinicopathological Information Improve Prognostic Stratification and Prediction of Chemoradiosensitivity in Sinonasal Carcinomas? A Retrospective Study on 145 Patients
Bibliographic record
Abstract
Background: The classification of sinonasal carcinomas (SNCs) is a conundrum. Consequently, prognosis and prediction of response to non-surgical treatment are often unreliable. The availability of prognostic and predictive measures is an unmet need, and the first logical source of information to be investigated is represented by the clinicopathological features of the disease. The hypothesis of the study was that clinicopathological information on SNC could be exploited to better predict prognosis and chemoradiosensitivity. Methods: All patients affected by SNC who received curative treatment, including surgery, at the Unit of Otorhinolaryngology-Head and Neck Surgery of the University of Brescia between October 1998 and February 2019 were included in the analysis. The institutional series was reviewed and a survival analysis was performed. Machine learning and multivariable statistical methods were employed to develop, analyze, and test 3 experimental classifications (classification #1, based on cytomorphological, histomorphological, and differentiation information; classification #2, based on differentiation information; and classification #3, based on locoregional extension) of SNC, based on the inherent clinicopathological information. The association of experimental classifications with prognosis and chemoradiosensitivity was tested. Results: The study included 145 patients. From a prognostic standpoint, the machine learning-generated classification of SNC provided better prediction than the current World Health Organization classification. However, the prediction of the chemoradiosensitivity of SNC was not achievable. Conclusions: Reorganization of clinicopathological information, with special reference to those related to tumor differentiation, can improve the reliability of prognosis of SNC. Prediction of chemoradiosensitivity remains an unmet need and further research is required.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".