Predictive value of nomogram-based multiparametric MRI combined with pathological biomarkers for HIF-1α Expression in Breast Cancer

Background Hypoxia-inducible factor-1α (HIF-1α) expression is associated with tumor progression, metastasis, and therapeutic resistance in breast cancer; however, its non-invasive prediction remains challenging. The aim of this study was to evaluate the predictive value of clinicopathological characteristics, conventional 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. Methods This retrospective study enrolled 146 breast cancer patients who underwent preoperative multiparametric MRI and surgical resection between October 2019 and September 2023. Patients were randomly split into training (n=103) and validation (n=43) cohorts (7:3 ratio). Clinicopathological features (age, ALN metastasis, histologic grade, ER, PR, HER-2, Ki-67), conventional MRI characteristics (tumor diameter, margin, TIC types, enhancement pattern), IVIM-DWI parameters (ADC, D, D*, f), and DCE-MRI quantitative parameters (K ^trans , Kep, Ve) were retrospectively extracted. Multivariate logistic regression and receiver operating characteristic (ROC) curve analyses were performed using SPSS 25.0, MedCalc 19.5.1, and R version 4.0.0. A nomogram was developed based on independent predictors. Results There were 40 low-expressions and 63 high-expressions in the training cohort and 21 low-expressions and 22 high-expressions in the validation cohort. High-expression group had a higher proportion of ALN metastasis, advanced histological grades, unclear margin, TIC-III type, lower D values, and higher K ^trans and K _ep values compared to the low-expression group ( P < 0.05). The 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 (Z=3.724–4.890, all P < 0.05). The nomogram demonstrated robust calibration (C-index=0.937) and validation performance (AUC=0.835). Conclusion The nomogram combining clinicopathological and multimodal MRI parameters shows promising accuracy for non-invasive prediction of HIF-1α expression, potentially facilitating personalized therapeutic strategies in breast cancer.