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Decoding Occult Cervical Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma: From AI-Driven Multimodal Fusion to Clinical Translation
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DOI：10.1007/s11912-026-01802-6.png)
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Abstract
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This review examines the limitations of conventional macroscopic approaches in detecting occult cervical lymph node metastasis (OCLNM). It also explores how multi-omics technologies and artificial intelligence (AI) driven cross-scale multimodal integration are reshaping precision diagnosis for patients with clinically node-negative (cN0) head and neck squamous cell carcinoma. Recent studies show that combining high-throughput omics data, such as genome-wide DNA methylation and spatial transcriptomics, with radiomics through deep learning models leads to markedly better performance than conventional imaging alone. New computational strategies, including habitat radiomics and cross-modal fusion networks, allow more detailed characterization of spatial tumor heterogeneity. They also provide insights into how the tumor microenvironment evolves during early metastatic spread. In addition to improving detection of microscopic disease, these approaches are beginning to support more reliable survival risk stratification. The integration of AI with multi-omics data is moving the field toward a noninvasive “panoramic virtual biopsy.” This framework offers a practical path to address a long-standing clinical challenge, balancing overtreatment against undertreatment in cN0 patients. With further validation, it may support safer surgical de-escalation and enable more personalized treatment strategies.
Keywords:
Head and Neck Squamous Cell Carcinoma (HNSCC)
Occult Cervical Lymph Node Metastasis (OCLNM)
Artificial Intelligence (AI)
Multi-omics
Radiopathomics
Multimodal fusion
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