Predictive Value of Nomogram-based Multiparametric MRI Combined with Pathological Biomarkers for HIF-1α Expression in Breast Cancer.

Rationale

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Objectives

This study aimed to assess the predictive value of clinicopathological characteristics, conventional magnetic resonance imaging (MRI), intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and dynamic contrast-enhanced MRI (DCE-MRI) parameters for hypoxia-inducible factor-1α (HIF-1α) expression in breast cancer.

Materials And Methods

We retrospectively enrolled 146 breast cancer patients receiving preoperative multiparametric MRI and surgery from 2019 to 2023, who were randomly assigned into training (n = 103) and validation (n = 43) cohorts at 7:3 ratio. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were conducted, and a nomogram was constructed based on independent predictive factors.

Results

The high-expression group had a higher proportion of axillary lymph node metastasis (ALN_metastasis), advanced histological grades, unclear margin, time intensity curve (TIC)-III type, lower D values, and higher Ktrans and Kep values compared with the low-expression group (P < 0.05). The area under the curves(AUCs) for the pathological, conventional MRI, IVIM-DWI, DCE-MRI, and combined models (ALN_metastasis + TIC type + D + Kep) were 0.765, 0.732, 0.771, 0.804, and 0.958 in the training cohort, respectively. The combined model significantly outperformed individual models (combined model vs. conventional MRI model, Z = 4.890, P < 0.001; combined model vs. pathological model, Z = 4.429, P < 0.001; combined model vs. IVIM model, Z = 3.724, P < 0.001; combined model vs. DCE-MRI model, Z = 3.691, P < 0.001).

Conclusion

The nomogram combining clinicopathological and multimodal MRI parameters can accurately predict HIF-1α expression non-invasively and assist personalized breast cancer therapy.