Proposed Version Nine of the AJCC and UICC TNM Classification for Salivary Gland Carcinoma

3Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Importance: A unified salivary gland carcinoma (SGC)-specific tumor-node-metastasis (TNM) classification can enhance prognostic accuracy, support clinical decision-making, and improve the quality of patient care. Objective: To derive and validate an SGC-specific pTNM classification with improved prognostic accuracy and optimized stage distribution for version nine of the American Joint Committee on Cancer/Union for International Cancer Control staging protocol. Design, Setting, and Participants: This retrospective prognostic cohort study derived a novel pTNM classification using data from the National Cancer Database (NCDB) of patients with surgically treated major SGC (2012-2017) and validated it in an international major SGC cohort (2008-2021) and a single-institution minor SGC cohort (Memorial Sloan Kettering Cancer Center; 1985-2016). Data were analyzed from June to November 2024. Exposures: Surgery with or without postoperative radiotherapy or chemoradiotherapy. Main Outcomes and Measures: The primary end point was overall survival (OS). Cox proportional hazards multivariable analysis was used to confirm the prognostic importance of pathologically positive lymph node (LN) number and extranodal extension (pENE) and derive an optimal pTNM classification. Results: The NCDB dataset included 8409 patients with SGC: 7659 with M0 disease (5748 with pN0 disease and 1911 with pN+ disease) and 750 with M1 disease. Among the 7659 patients with M0 disease, the median (IQR) age was 60 (48-71) years, and 3861 (50.4%) were male. The median (IQR) follow-up was 88.4 (72.3-108.5) months. The 5-year OS was 87.2% (95% CI, 86.3-88.0) for N0 disease, 68.2% (95% CI, 63.9-72.8) for 1 positive LN without pENE, 60.2% (95% CI, 53.5-67.5) for 2 positive LNs without pENE, 68.4% (95% CI, 58.0-76.6) for 3 positive LNs without pENE, 47.5% (95% CI, 41.6-52.8) for more than 3 positive LNs without pENE, and 41.4% (38.1-44.8) for pENE-positive LNs. Multivariable analysis confirmed the independent prognostication of LN count compared with pN0 disease (1 positive LN: adjusted hazard ratio [aHR], 1.70; 95% CI, 1.44-2.01; 2 positive LNs: aHR, 1.61; 95% CI, 1.31-1.98; 3 positive LNs: aHR, 2.10; 95% CI, 1.65-2.68; 4 positive LNs: aHR, 2.46; 95% CI, 1.87-3.24; more than 4 positive LNs: aHR, 2.07; 95% CI, 2.08-2.91) and pENE-positive LNs compared with pENE-negative LNs (aHR, 1.27; 95% CI, 1.10-1.48). The proposed pN classification were pN1 for 1 to 3 positive LNs and pENE negativity and pN2 for more than 3 positive LNs or pENE positivity. Model fit improved with the proposed pN classification vs the current pN classification (Akaike Information Criterion, 26442 vs 26483). Based on the aHR model, the following stage groups were proposed: stage I: T1N0 (1 [reference]); stage II: T2N0 (aHR, 1.34; 95% CI, 1.11-1.61); stage IIIA: T1-2N1 or T3-4N0 (aHR, 2.36; 95% CI, 1.99-2.80); stage IIIB: T1-2N2 or T3-4N1-2 (aHR, 5.15; 95% CI, 4.38-6.06); and stage IV: M1 disease (aHR, 13.61; 95% CI, 11.37-16.29). The C index values were similar (proposed classification: 0.792; current classification: 0.790), while the AIC improved slightly (proposed classification: 26441; current classification: 26482). Stage-specific OS differences were evident in both the international major SGC cohort (n = 1015) and Memorial Sloan Kettering Cancer Center minor SGC cohort (n = 444). Conclusions and Relevance: This unified, SGC-specific staging system improved prognostic accuracy and sample size balance and was applicable to both major and minor SGCs.

Cite

CITATION STYLE

APA

Huang, S. H., Cotler, J., Palis, B., Seethala, R. R., Hosni, A., O’Sullivan, B., … Ganly, I. (2026). Proposed Version Nine of the AJCC and UICC TNM Classification for Salivary Gland Carcinoma. JAMA Otolaryngology - Head and Neck Surgery, 152(4), 366–375. https://doi.org/10.1001/jamaoto.2025.5396

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free