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Updated: Aug 13, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Predictive Value of Nomogram-based Multiparametric MRI Combined with Pathological Biomarkers for HIF-1α Expression in
Fan Zhao1, Weiwei Wang2, Yahan Zhong3
1Department of Oncology, Affiliated Hospital of Jining Medical University, Jining, China.
Rationale And 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.