Metabolic tumour volume as a prognostic factor for oral cavity squamous cell carcinoma treated with primary surgery
Bibliographic record
Abstract
BACKGROUND: Metabolic tumour volume (MTV) obtained from pre-treatment 18 F-fluorodeoxydeglucose positron emission tomography with computed tomography (PET-CT) has been validated as an independent predictive factor of outcomes in head and neck cancer patients (HNC) treated with primary chemoradiotherapy (CRT). However its role in patients treated with primary surgery has not yet been studied. OBJECTIVE: To evaluate the prognostic value of MTV in patients treated with primary surgery for oral cavity squamous cell carcinoma (OCSCC). METHOD: Demographic and survival data was obtained from patients diagnosed with OCSCC from 2008-2012 in Alberta, Canada. All patients included in the study had PET-CT scan before curative surgical resection. MTV and maximum standardized uptake value (SUVmax) value was delineated from pre-treatment PET-CT scans using Segami Oasis software (Columbus, OH). MTV and SUVmax were divided into intertertile thirds before statistical analysis to allow for in-group comparison of survival. RESULTS: A total of 80 patients were analyzed using SPSS ver. 20.0 (SPSS Inc, Chicago, IL). Five-year overall, and disease-free survival using Kaplan-Meier curves were 70% and 73% respectively. When the combined SUVmax (tumour primary and locoregional metastasis) was evaluated, it failed to predict overall (HR = 1.0, p = 0.99) or disease-free survival (HR = 1.0, p = 0.227). CONCLUSIONS: This study shows that MTV is an independent adverse prognostic factor for death and disease recurrence in OCSCC treated with primary surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".