Artificial Intelligence–Assisted Detection of Tumor Calcification in Oral Squamous Cell Carcinoma: Emerging Opportunities in Computational Oral Pathology and Biomineralization Research

Objective: Tumor-associated calcification is increasingly recognized as a potentially relevant feature of the tumor microenvironment across multiple malignancies; however, its significance in oral squamous cell carcinoma (OSCC) remains poorly understood and is frequently overlooked during routine histopathologic assessment. This perspective examines the potential of artificial intelligence (AI)-assisted computational pathology to improve the detection, quantification, and spatial characterization of tumor-associated calcification in OSCC and discusses its potential as an emerging histopathologic research phenotype and candidate biomarker requiring further validation. Design: A narrative synthesis of the current literature was undertaken to evaluate the biological mechanisms underlying tumor-associated calcification in OSCC, recent advances in biomineralization research, and the application of AI and computational pathology in digital histopathology. Current evidence was integrated to propose future AI-based workflows for objective calcification assessment and to identify opportunities for investigating its potential biological and clinical relevance. Results: Current evidence indicates that calcification has been reported or proposed in vascular, stromal, necrotic, and extracellular matrix-associated contexts in OSCC and related pathological settings; however, the prevalence, biological mechanisms, and reproducibility of these patterns in OSCC remain insufficiently characterized. Potential associations with hypoxia, chronic inflammation, oxidative stress, endothelial dysfunction, and osteogenic-like cellular reprogramming are supported primarily by evidence from broader biomineralization, vascular calcification, and other tumor contexts rather than by calcification-specific mechanistic studies in OSCC. Advances in whole-slide imaging, deep learning, and computational pathology have enabled automated tissue segmentation, feature extraction, and quantitative histopathologic analysis. However, their application to the systematic detection and spatial characterization of calcification in OSCC remains limited. AI-assisted analysis could facilitate objective identification, quantification, and spatial mapping of mineralized structures, providing a conceptual research framework for investigating tumor heterogeneity and microenvironmental remodeling. Conclusions: Integrating computational pathology with biomineralization research may improve the characterization of tumor-associated calcification in OSCC and provide a methodological basis for investigating quantitative digital phenotypes. At present, evidence is insufficient to establish calcification as a validated diagnostic, prognostic, or predictive biomarker in OSCC. Future multidisciplinary studies combining AI, spatial pathology, molecular profiling, and clinical validation are required to determine its biological and clinical significance and to establish whether calcification-associated features have reproducible biomarker potential.