Neoadjuvant strategies in head and neck squamous cell carcinoma (HNSCC) are reshaping therapeutic paradigms by shifting emphasis from anatomical staging toward biology-driven response stratification. The transition from induction chemotherapy to immune checkpoint–based and combination regimens has transformed the perioperative setting into a translational platform that enables interrogation of tumor–immune interactions and clonal selection under therapeutic pressure prior to surgery. In this context, pathological response assessment has emerged as a robust surrogate endpoint, overcoming the limitations of radiologic evaluation, which often fails to capture immune-mediated pseudoprogression and spatially heterogeneous regression. Quantification of residual viable tumor (RVT) provides a reproducible metric of therapeutic efficacy, while characterization of immune-related regression beds, tertiary lymphoid structures, macrophage polarization states, and compartment-specific nodal responses offers mechanistic insight into tumor clearance and resistance evolution. Evidence from phase II trials, single-cell sequencing, spatial transcriptomics, and multiplex immune profiling supports the prognostic relevance of pathology-driven endpoints. Integration of digital pathology and artificial intelligence–assisted image analysis further enhances reproducibility and enables high-resolution mapping of residual disease and immune architecture. Within this modern oncologic framework, the neoadjuvant-treated specimen functions as a dynamic biomarker platform guiding response-adapted surgical strategies and biomarker-driven clinical trial design. This study was designed as a narrative review. A structured literature search was performed using PubMed and major oncology journals to identify relevant studies on pathology-driven response assessment in neoadjuvant-treated head and neck squamous cell carcinoma. The review focused on publications addressing histopathological response criteria, immune microenvironment remodeling, spatial profiling technologies, and computational pathology approaches.
Mauro et al. (Sat,) studied this question.