Abstract
ABSTRACT
Current diagnostic approaches for head and neck cancer rely primarily on white‐light endoscopy and tissue biopsy, providing only static, episodic assessments that fail to capture dynamic changes in tumor biology over time. The absence of continuous, detailed molecular and microscopic structural information makes early identification of dysplasia or cancer difficult and can delay critical treatment. Here, we introduce a unified, label‐free endoscopic platform that combines Raman spectroscopy (RS) and optical coherence tomography (OCT) within a miniaturized, forward‐viewing fiber‐optic probe. This integration enables simultaneous video‐rate OCT imaging and 1 s RS acquisition, linking morphological and molecular tissue trajectories in vivo. We developed an explainable AI‐based fusion framework that learns joint temporal representations of biochemical and structural features to classify tissue states while highlighting the most influential predictors. In a longitudinal 4NQO‐induced murine model of carcinogenesis, the system tracked epithelial transformation from hyperplasia through dysplasia and carcinoma in situ to invasive cancer. OCT provides the dominant structural contrast in this thin‐epithelium murine model, while RS adds complementary molecular specificity. By capturing temporally resolved morphological and molecular trajectories in vivo, this platform demonstrates a new approach for early cancer detection and has potential to support surveillance and clinical decision‐making.