ArticleAnnals of surgical oncology2026
A Percentage of Residual Viable Tumor Outperforms Traditional AJCC Staging in Oral Squamous Cell Carcinoma After Neoadjuvant Immunochemotherapy.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundTraditional American Joint Committee on Cancer (AJCC) staging exhibits critical limitations when assessing oral squamous cell carcinoma (OSCC) treated with neoadjuvant immunochemotherapy (NICT). This study proposed using the percentage of residual viable tumor (%RVT) as an alternative to traditional anatomic staging.
methodsThe study retrospectively evaluated 365 patients with primary OSCC who underwent NICT followed by curative-intent surgery. The patient population was divided into a training cohort (n = 243) and a validation cohort (n = 122). The study used restricted cubic splines and maximally selected rank statistics to determine optimal %RVT cutoff points for disease-free survival (DFS). The predictive performance of the proposed %RVT staging then was compared with the traditional AJCC eighth-edition ypT staging.
resultsThe optimal %RVT cutoffs were 0%, 10%, and 50%, defining T1 (0%), T2 (> 0-10%), T3 (> 10-50%), and T4 (> 50%). Compared with T1, the adjusted hazard ratios (HRs) for a DFS event in the training cohort were 2.54 (95% confidence interval [CI], 1.38-4.67) for T2, 5.93 (95% CI, 3.21-10.96) for T3, and 11.28 (95% CI, 5.87-21.68) for T4. The corresponding HRs in the validation cohort were 2.48 (95% CI, 1.28-4.80), 5.76 (95% CI, 2.94-11.28), and 10.95 (95% CI, 5.22-22.97), respectively (all P ≤ 0.007). The %RVT-based system demonstrated greater discrimination than AJCC ypT staging in the training (C-index, 0.79 vs 0.68) and validation (C-index, 0.77 vs 0.66) cohorts.
conclusionA novel %RVT-based T staging system outperforms traditional AJCC staging in predicting DFS for OSCC patients treated with NICT. Integrating %RVT into clinical practice provides a superior tool for risk stratification, aiding in the personalization of post-surgical adjuvant therapies.
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