Morphologic and topographic radiologic features of human papillomavirus‐related and –unrelated oropharyngeal carcinoma
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare the clinicoradiologic characteristics of human papillomavirus (HPV)-related (HPV-positive) and HPV-unrelated (HPV-negative) oropharyngeal carcinoma (OPC). METHODS: Primary tumor and lymph node features of HPV-positive and HPV-negative OPCs from 2008 to 2013 were compared on pretreatment CT/MRI. Intrarater/interrater concordance was assessed. Multivariable analyses identified factors associated with HPV-positivity to be used in nomogram construction. RESULTS: Compared to HPV-negative (n = 194), HPV-positive (n = 488) tumors were more exophytic (73% vs 63%; p = .02) with well-defined border (58% vs 47%; p = .033) and smaller axial dimensions; lymph node involvement predominated (89% vs 69%; p < .001) with cystic appearance (45% vs 32%; p = .009) but similar topography. Intrarater/interrater concordance varied (fair to excellent). Nomograms combining clinical (age, sex, smoking pack-years, subsite, T/N classification) and/or radiologic (nonnecrotic tumor and cystic lymph node) features were used to weigh the likelihood of HPV-driven tumors (area under the curve [AUC] = 0.84). CONCLUSION: HPV-positive OPC has different radiologic tumor (exophytic/well-defined border/smaller axial dimension) and lymph node (cystic) features but similar lymph node topography.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".