The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

Simple Summary: This systematic review evaluates the potential of magnetic resonance imaging (MRI) to predict tumor biology in primary squamous cell carcinoma of the head and neck (HNSCC). Fifty-eight articles were analyzed, examining the relationship between MRI parameters and biological features. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower diffusion-weighted metrics. Moreover, lower diffusion values were also with a high Ki-67 proliferation index, indicating high cellularity. Several perfusion parameters describing the vascularity were significantly associated with HIF-1α. Analysis results of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) were inconclusive. Larger datasets are needed to develop and validate radiomic-based prediction models, which already show promising results in capturing diverse tumor biology features. Overall, MRI holds potential for non-invasive and rapid tumor biology characterization, enhancing future clinical outcome predictions and personalized patient management for HNSCC. Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC. [ABSTRACT FROM AUTHOR]