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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.
Academic Radiology
|August 3, 2026
Summary
This study shows that combining clinicopathological data with multiparametric MRI, including IVIM-DWI and DCE-MRI, accurately predicts hypoxia-inducible factor-1α (HIF-1α) expression in breast cancer non-invasively.
Area of Science:
- Oncology
- Radiology
- Biomarkers
Background:
- Hypoxia-inducible factor-1α (HIF-1α) is a key regulator of tumor response to therapy.
- Accurate prediction of HIF-1α expression is crucial for personalized breast cancer treatment.
Purpose of the Study:
- To evaluate the predictive value of clinicopathological features and multiparametric MRI (conventional MRI, IVIM-DWI, DCE-MRI) for HIF-1α expression in breast cancer.
- To develop a nomogram for non-invasive prediction of HIF-1α expression.
Main Methods:
- Retrospective analysis of 146 breast cancer patients with preoperative multiparametric MRI.
- Multivariate logistic regression and ROC curve analyses were used to identify independent predictive factors.
- A nomogram was constructed based on significant predictors.
Main Results:
- High HIF-1α expression was associated with axillary lymph node metastasis, advanced grade, unclear margin, TIC-III, lower D values, and higher Ktrans and Kep.
- The combined model (clinicopathological + IVIM-DWI + DCE-MRI) achieved an AUC of 0.958 in the training cohort.
- The combined model significantly outperformed individual models (pathological, conventional MRI, IVIM-DWI, DCE-MRI).
Conclusions:
- A nomogram integrating clinicopathological and multimodal MRI parameters offers accurate non-invasive prediction of HIF-1α expression.
- This tool can aid in personalized breast cancer therapy planning.