Validation of the Histologic Risk Model in a New Cohort of Patients With Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Half of the patients with head and neck squamous cell carcinoma (HNSCC) can be expected to fail therapy, indicating that more aggressive treatment is warranted for this group. We have developed a novel risk model that can become a basis for developing new treatment paradigms. Here we report on the performance of our model in a new multicenter cohort. DESIGN: Eligible patients from 3 institutions (Montefiore Medical Center, University of Manitoba, and New York University Medical Center) were identified and pathology slides from their resection specimens were reviewed by Margaret Brandwein-Gensler; risk category was assigned as previously published. Kaplan-Meier analysis was performed for disease progression and survival. Cox proportional hazards regression was performed, adjusted for potential confounders. A teaching module was also developed; attending pathologists were asked to score coded slides after a lecture and multiheaded microscope teaching session. Agreement was assessed by calculating Cohen unweighted kappa coefficients. RESULT: The validation cohort consisted of 305 patients, from the above institutions, with 311 primary HNSCC of the oral cavity, oropharynx, and larynx. The median follow-up period for all patients was 27 months. Risk category predicts time to disease progression (P=0.0005), locoregional recurrence (P=0.013), and overall survival (P=0.0000) by Kaplan-Meier analysis. High-risk status is significantly associated with decreased time to disease progression, adjusted for clinical confounders (P=0.015, hazard ratio 2.32, 95% confidence interval 1.18-4.58) compared with collapsed intermediate and low-risk groups. We also demonstrate substantial interrater agreement (kappa=0.64), and very good rater agreement when compared with the standard (kappa=0.87). CONCLUSIONS: We demonstrate significant predictive performance of the risk model in a new cohort of patients with primary HNSCC, adjusted for confounders. Our training experience also supports the feasibility of adapting the risk model in clinical practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".