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.

The clinical translation of automated HPV detection in Whole Slide Images (WSIs) is challenged by staining variability, sparse viral effects, and the biological continuum between cytology and histology. This work presents a fully automated pipeline for binary patch-level classification of HPV-induced lesions on H&E-stained tissue. The core contribution is a domain-adaptive transfer learning strategy: a ResNet50 backbone is pretrained on the SIPaKMeD cervical cytology dataset rather than ImageNet, then fine-tuned on a target histological cohort. Preprocessing includes adaptive tissue segmentation, blur rejection, and Macenko stain normalization to ensure vendor-agnostic inputs. Evaluated using a strict Leave-One-Patient-Out cross-validation on 42 diagnostic specimens, the SIPaKMeD-based initialization significantly outperforms the ImageNet baseline. This approach achieves higher AUC-ROC scores and superior stability across folds, demonstrating that domain-specific pretraining effectively mitigates data scarcity and class imbalance in digital cervical cancer screening. Under a complementary 5-fold patient-level cross-validation covering all 19 patients of the cohort ($\mathrm{133,704}$ patches, 7181 HPV-positive, prevalence $5.37\%$), the SIPaKMeD-pretrained model attains a mean test AUC-ROC of $0.694$ with a $95\%$ patient-aware bootstrap confidence interval of $[0.681, 0.705]$, consistently above the ImageNet baseline mean of $0.656$ obtained on the controlled three-fold ablation.

Gastric adenocarcinomas with yolk sac tumor (YST) differentiation represent an exceptionally rare and poorly understood phenotype, characterized by the emergence of extraembryonic features within an epithelial malignancy. Their histogenesis remains debated, with increasing evidence supporting somatic lineage plasticity rather than germ cell origin. Here, we performed an integrated histopathologic and genomic characterization of three gastric adenocarcinomas with YST differentiation surgically treated at a tertiary cancer center. Histologically, all tumors showed a predominant adenocarcinoma component associated with variable YST differentiation, displaying reticular/microcystic and papillary patterns and expression of oncofetal markers, including alpha-fetoprotein (AFP) and Glypican-3. Targeted next-generation sequencing using a 523-gene panel revealed microsatellite-stable profiles with intermediate tumor mutational burden and substantial intertumoral heterogeneity. Despite gene-level variability, the detected alterations involved signaling pathways commonly implicated in epithelial tumorigenesis, including PI3K-AKT and RTK/RAS-MAPK signaling. Several recurrent alterations were identified across cases, including CCND3 variants and MDM2 copy number gains; however, their biological significance requires validation in larger cohorts. Functional enrichment analysis identified alterations involving developmental and proliferative signaling programs. Overall, these findings suggest that YST differentiation may represent a phenotypic manifestation of epithelial tumor plasticity arising within gastric adenocarcinoma and is associated with epithelial-related oncogenic programs, although broader genomic and comparative studies are required to clarify its histogenesis. This study provides preliminary molecular and histopathologic insights into this rare entity and supports the integration of molecular profiling into its diagnostic and translational management.