MRI-Based Radiomics and Artificial Intelligence for Prediction of Recurrence and Prognostic Outcomes in Oral Tongue Squamous Cell Carcinoma: A Systematic Review with Functional Meta-Synthesis.
Authors
Affiliations (4)
Affiliations (4)
- Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, India.
- Basic Sciences Department, Biomedical Stomatology Research Group, Faculty of Dentistry, Universidad de Antioquia U de A, Medellín 050010, Colombia.
- Faculty of Dentistry, Institución Universitaria Visión de las Américas, Medellín 050040, Colombia.
- Department of Basic Sciences, Faculty of Dentistry, Universidad de Antioquia, Medellín 050010, Colombia.
Abstract
Oral tongue squamous cell carcinoma (OTSCC) remains clinically challenging because conventional clinicopathological markers do not fully explain variability in recurrence and survival. This systematic review and functional meta-synthesis aimed to identify and critically appraise studies using preoperative magnetic resonance imaging (MRI)-based radiomics, artificial intelligence (AI), deep learning, or quantitative MRI-derived models to predict recurrence and prognostic outcomes in OTSCC. PubMed, Scopus, and Embase were searched from inception to March 2026. Eligible studies included prognostic model investigations in adults with OTSCC or primary tongue cancer without reported base-of-tongue/oropharyngeal involvement, undergoing preoperative MRI and surgery, with recurrence- or survival-related follow-up. The primary synthesis was a functional meta-synthesis; pooling was not performed because studies were not sufficiently comparable. Seven retrospective studies were included, with a summed descriptive sample of 1287 participants. The evidence base was heterogeneous in MRI sequences, segmentation workflows, model architecture, validation strategy, and endpoint definition. Functional meta-synthesis identified four domains: direct recurrence-oriented modeling, broader prognostic stratification, reported incremental or complementary value over clinical frameworks, and translational maturity/technical implementation. Several studies reported associations between MRI-derived signatures and recurrence- or survival-related outcomes, but findings were interpreted narratively because of differences in primary endpoints, imaging features, model design, validation methods, and outcome definitions. Most studies were judged at high overall risk of bias, and certainty of evidence ranged from low to very low. MRI-based radiomics and AI show preliminary promise for prognostic stratification in OTSCC, particularly recurrence-related risk refinement, but current evidence remains limited by retrospective design, heterogeneity, sparse external validation, and low certainty.