Imaging tumor microenvironment heterogeneity in oral squamous cell carcinoma using IVIM DWI habitat analysis.

OBJECTIVE

The tumor microenvironment (TME) of oral squamous cell carcinoma (OSCC) is spatially heterogeneous and critically influences prognosis. Conventional magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) metrics average heterogeneity and obscure relevant biological intratumoral patterns. Habitat imaging enables voxel-wise characterization of diffusion-perfusion heterogeneity.

MATERIALS AND

METHODS

Eighty-four patients with pathologically confirmed OSCC were prospectively enrolled and underwent preoperative multi-b-value intravoxel incoherent motion (IVIM)-DWI. A voxel-wise habitat imaging framework was applied based on diffusion-related (Dt) and perfusion-related (f) parameters. Four habitats were defined using population-level Dt and f thresholds, corresponding to distinct combinations of diffusion and perfusion characteristics, to ensure standardized subregion definitions across patients. Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and cervical lymph node metastasis (CLNM) were assessed on histopathological sections. Habitat metrics, including subregional percentage, volume, and mean Dt and f values, were quantified and correlated with tumor-level pathological phenotypes. The predictive performance was evaluated.

RESULTS

High-TSR tumors exhibited higher Dt value within hypocellular habitats (subregions 3 and 4), which independently predicted a stroma-rich phenotype. High-TIL tumors demonstrated lower Dt in a cellular-dominant, high-perfusion habitat (subregion 2) and a reduced percentage of hypocellular, low-perfusion habitat (subregion 3), consistent with immune-enriched microenvironments. CLNM-positive tumors showed overall habitat expansion, with increased volume of a putative stroma-dominant habitat independently associated with metastasis. Habitat-based models outperformed whole-tumor apparent diffusion coefficient and Dt for predicting TSR (area under the receiver operating characteristic curve (AUC) = 0.791) and TILs (AUC = 0.782).

CONCLUSION

IVIM-DWI-based habitat imaging provides a noninvasive approach for characterizing diffusion-perfusion heterogeneity in OSCC and may complement histopathological assessment for preoperative risk stratification.

KEY POINTS: Question: Can IVIM-DWI-based habitat imaging noninvasively characterize tumor microenvironment heterogeneity and identify imaging features associated with stromal composition, immune infiltration, and nodal metastasis in OSCC?

FINDINGS: IVIM-DWI-based habitat imaging identified spatially distinct diffusion-perfusion habitats associated with TSR, TILs, and CLNM, with improved discrimination over conventional whole-tumor diffusion metrics.

RELEVANCE STATEMENT: IVIM-DWI-based habitat imaging provides clinically relevant, noninvasive biomarkers of TME features, improving preoperative risk stratification and supporting more precise, individualized therapeutic decisionmaking in OSCC.