# A multifunctional MRI model based on IVIM and DKI predicts HIF-1α, Ki-67, and VEGF status in breast carcinoma

## TL;DR

This study shows that combining MRI techniques can predict important markers in breast cancer, potentially improving diagnosis and treatment planning.

## Contribution

The novel contribution is the integration of IVIM and DKI MRI models to predict HIF-1α, Ki-67, and VEGF status in breast carcinoma.

## Key findings

- High HIF-1α, VEGF, and Ki-67 levels correlate with lower D, MD, and ADC values and higher D*, f, and MK values.
- The combined IVIM-DKI model outperforms individual parameters in predicting molecular marker status.
- The f parameter is the best single predictor of VEGF expression, with an AUC of 0.882.

## Abstract

The research aims to explore the predictive significance of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and their integrated models in relation to Hypoxia-inducible factor-1 alpha (HIF-1α), Ki-67, and vascular endothelial growth factor (VEGF) expression levels in breast carcinoma.

This retrospective study included 104 patients with pathologically confirmed breast carcinoma from our institution as the training set, while an external validation cohort of 91 eligible patients was recruited from another tertiary medical center. Two independently working radiologists analyzed IVIM-derived parameters apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f), and DKI-derived parameters mean diffusivity (MD) and mean kurtosis (MK). Receiver operating characteristic (ROC) curves were constructed for evaluation of diagnostic efficacy. The outcomes of the multivariate logistic regression model were employed to create a nomogram of the combined model for molecular marker status prediction.

High expression levels of HIF-1α, VEGF, and Ki-67 were consistently associated with lower D, MD, and ADC values, and higher perfusion-related D*, f, and MK values (all P<0.05). ROC curve analysis showed that among the individual parameters, the D value exhibited the highest predictive efficacy (Area Under the Curve, AUC = 0.724). A D value ≤ 0.88×10-3 mm2/s should strongly suggest high HIF-1α expression. ROC curve analysis revealed that the f parameter was the most powerful single indicator for predicting VEGF expression (AUC = 0.882). In clinical practice, an f value ≥ 29.82% can serve as a key imaging biomarker suggesting high VEGF expression, i.e., active tumor angiogenesis. ROC curve analysis indicated MD as the most predictive single parameter for Ki-67 expression (AUC = 0.762), showing significantly greater efficacy than D* (Z = 2.022, P = 0.043). Thus, an MD value ≤ 2.21×10-3 mm2/s strongly suggests high tumor proliferative activity. In the training set, the combined models integrating select parameters from IVIM and DKI showed significantly higher predictive performance (AUCs: 0.852-0.923) compared to individual parameters. This performance was replicated in the external validation set (AUCs: 0.841-0.918), with no statistically significant difference in AUCs between the training and external validation sets according to DeLong’s test (all P > 0.05). Moreover, the solid line provided a better approximation of the ideal dotted line, indicating higher predictive accuracy of the nomograms (P = 0.59, 0.40, and 0.08). According to the decision curve analysis (DCA), the predictive model provided a substantial net clinical benefit.

Our findings suggest that IVIM may be usefully combined with DKI to help predict the expression levels of Ki-67, HIF-1α, and VEGF in breast cancer, generating hypotheses for future research. Furthermore, the diagnostic efficiency of the parameters D* and f appears to be enhanced by employing more low b-values (<100–200 s/mm²). These results require confirmation in prospective, multi-center studies.