Compartmentalization of High Infratemporal Fossa in Oral Cavity Squamous Cell Carcinomas and Its Impact on Clinical Outcome
Bibliographic record
Abstract
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.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".