Preoperative Clinical and Tumor Factors Associated With Adjuvant Therapy for Oral Cavity Cancer
Notice bibliographique
Résumé
Importance: The standard of care for patients with oral cavity squamous cell carcinoma (OCSCC) is generally primary surgical resection with or without adjuvant therapy (AT), based on pathological factors. Identifying preoperative factors that are associated with the receipt of AT may enhance treatment planning. Objective: To identify preoperative patient and tumor factors associated with receiving AT, either radiation therapy (RT) or chemoradiation therapy (CRT), in patients with OCSCC. Design, Setting, and Participants: This cohort study, spanning January 2005 to December 2019 at 9 academic centers in Canada, was conducted as part of the Canadian Head & Neck Collaborative Research Initiative, a national network of head and neck surgical oncologists. Participants included patients with oral cavity cancer who underwent surgery. The data analysis was performed in March 2024. Exposures: Preoperative variables, including demographics (age, sex, smoking history, and Charlson Comorbidity Index [CCI]) and tumor characteristics (clinical T and N stage, biopsy grade, tumor size). Main Outcomes and Measures: The main outcomes were the receipt of AT vs surgery alone; the type of AT, either RT or CRT; and the presence of a strong pathologic indicator for AT. Results: Of the 3980 patients, 2438 underwent surgery alone (61%) and 1542 received AT (39%). Of these, 1907 (48%) had a strong pathologic indicator for AT. The mean (SD) age was 63 (13) years, and 1498 participants (38%) were female. On multivariable analysis, factors independently associated with AT included being older than 65 years (odds ratio [OR], 0.50 [95% CI, 0.38-0.64]), CCI of 4 or higher (OR, 1.83 [95% CI, 1.26-2.65]), previous head and neck cancer (OR, 0.40 [95% CI, 0.26-0.62]), maxillary alveolus (OR, 2.16 [95% CI, 1.11-4.22]) and retromolar trigone (OR, 1.85 [95% CI, 1.04-3.29) subsites, tumor dimension (OR, 1.35 [95% CI, 1.22-1.50] per cm), increasing clinical T and N stages, and worse grade on biopsy (poorly differentiated: OR, 1.89 [95% CI, 1.25-2.84]). Among those receiving AT, poorly differentiated grade (OR, 2.40 [95% CI, 1.34-4.30]) and advanced N stage were associated with CRT rather than RT. Among patients with strong pathologic indicators for AT, factors associated with not receiving AT included age, CCI, grade, stage, and tumor dimension. The prediction model showed good discriminatory power (area under the receiver operating characteristic curve, 0.84 [95% CI, 0.82-0.86]). Conclusions and Relevance: The results of this cohort study suggest that preoperative variables can help to identify patients with OCSCC who are more likely to receive AT, despite many factors not being predictable until the postoperative period. Early identification of patients at high risk may improve treatment planning and reduce delays in initiating AT, potentially enhancing patient outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».