Compartmentalization of High Infratemporal Fossa in Oral Cavity Squamous Cell Carcinomas and Its Impact on Clinical Outcome
Notice bibliographique
Résumé
Background: According to the 8th edition of the American Joint Committee on Cancer (AJCC), involvement of the masticator space and infratemporal fossa (ITF) in oral cancers indicates advanced disease (T4b), which is often considered unresectable. Previous studies have shown that the extent of ITF involvement influences management and outcomes. Therefore, to optimize management, T4b disease should be subclassified based on ITF involvement. Notably, infranotch disease has a more favorable prognosis compared to supranotch disease. Our study also observed that certain subsets of high anterior retroantral ITF involvement may be operable with favorable clinical outcomes. This study aims to derive a new image-based compartmentalization of high ITF involvement and assess its impact on the management and outcomes of oral head and neck squamous cell carcinoma (HNSCC) patients with high ITF involvement. Materials and Methods: This retrospective observational study included 154 non-metastatic, upfront unresectable locally advanced HNSCC patients who were fit for induction neoadjuvant chemotherapy (NACT). ITF involvement was classified into distinct compartments, and detailed staging of the primary tumor (T) and regional nodes (Ns) was performed. Clinical data, including patient demographics, treatment received, and follow-up notes, were documented. Prognosis was assessed using survival metrics: event-free survival (EFS), progression-free survival (PFS), and overall survival (OS). The ITF was categorized into the following compartments: compartment 1 (low ITF: medial pterygoid), compartment 2 (anterior high ITF: retroantral fat), compartment 3 (posterior high ITF), including 3a (paramandibular compartment: paramandibular fat/temporalis), 3b (muscle compartment: lateral pterygoid), and 3c (Perineural compartment: pterygopalatine fossa and pterygomaxillary fissure). Results: Of the 154 cases, 142 (92%) were classified as T4b, with 63 (40.9%) having high ITF involvement and 79 (55.6%) having low ITF involvement. Twelve cases had T4a disease, which was deemed unresectable due to extensive nodal involvement. Subcompartmentalization of the 63 high ITF cases revealed 26 (41.2%) with compartment 2 involvement, 17 (26.9%) with compartment 3a involvement, 11 (17.4%) with compartment 3b involvement, and 9 (14%) with compartment 3c involvement. Disease progression following NACT was significantly higher in compartment 3c, which showed a poor response (p = 0.007). Univariate analysis for PFS revealed similar outcomes for compartments 1 and 2 (p = 0.692), while compartment 3 demonstrated poorer outcomes (p = 0.033). Among thosehigh ITF involvement, compartment 3c had the worst PFS outcome (p = 0.03). Conclusions: Baseline imaging plays a critical role in guiding individualized treatment and predicting clinical outcomes. Low ITF involvement and disease limited to the high retroantral fat compartment exhibit similar clinical outcomes. Among the posterior high ITF compartments, involvement of the pterygopalatine fossa and pterygomaxillary fissure (compartment 3c) is associated with the worst prognosis and poor response to chemotherapy. Subcompartmentalization of ITF involvement provides valuable prognostic information to tailor treatment strategies.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».