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Characterization of response-adapted surgery following neoadjuvant systemic therapy in oral cavity cancer.

Created on 01 Jul 2026

Authors

Richard J Lu, Lara Dunn, Kenric Tam, Michael T Starc, Richard J Wong, Ian Ganly, Snehal Patel, Nancy Lee, Winston Wong, Marc A Cohen, Alan Ho, Jennifer R Cracchiolo

Published in

Oral oncology. Volume 180. Pages 108060. Jun 30, 2026. Epub Jun 30, 2026.

Abstract

Despite decades of experience with neoadjuvant systemic therapy (NST) in oral cavity squamous cell carcinoma (OCSCC) demonstrating its potential to facilitate surgical de-escalation through tumor downstaging, the impact of NST on response-adapted surgery has not been systematically evaluated.
Surgical de-escalation following neoadjuvant NST was quantified using the Response-Adapted Surgery-Oral Cavity classification and the Oral Cavity Surgical Morbidity Score (OC-SMS) in 70 consecutive patients with resectable, locally advanced OCSCC. The relationship between surgical de-escalation, pathologic response, and margin status was assessed.
Surgical de-escalation was observed in 44 patients (62.8 %). Mean OC-SMS decreased from 9.1 (95 % CI, 8.5-9.7) before NST to 6.5 (95 % CI, 5.8-7.3) after NST. The largest contributors to de-escalation were reduced extent of resection (52 %), decreased need for free flap reconstruction (27 %), reduced surgical access requirements (17 %), and less extensive neck dissection (3 %). The greatest reductions were observed in tongue cancers (mean decrease, 3.1 points; p < 0.001), followed by buccal mucosa (2.1 points; p = 0.024) and floor of mouth cancers (2.0 points; p = 0.250). Gingival cancers demonstrated the smallest reduction (1.1 points; p = 0.116). Surgical de-escalation correlated with pathologic response, and 97 % of de-escalated cases achieved negative margins.
Neoadjuvant systemic therapy resulted in measurable surgical de-escalation in OCSCC without compromising margin status. Surgical de-escalation should be incorporated as a key endpoint in future prospective trials designed to optimize NST approaches.

PMID:
42378748
Bibliographic data and abstract were imported from PubMed on 01 Jul 2026.

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