Application value of IVIM, DKI and DCE-MRI radiomics predicting HER-2 expression in breast cancer

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• Clinical Article •

ZHAO Xiaomeng SHAO Shuo ZHENG Ning CUI Jingjing LIU Shihan WU Jianwei

Cite this article as: ZHAO X M, SHAO S, ZHENG N, et al. Application value of IVIM, DKI and DCE-MRI radiomics predicting HER-2 expression in breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(7): 105-111. DOI:10.12015/issn.1674-8034.2024.07.018.

Abstract

[Abstract] Objective To explore the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and diagnostic value of radiomics models based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), in prediction of human epidermal growth factor receptor 2 (HER-2) positive status in breast cancer patients.Materials and Methods The clinical data of 192 patients with breast cancer were analyzed retrospectively. Patients were divided into HER-2 positive group (48 cases) and HER-2 negative group (144 cases) based on their pathological results. All patients underwent IVIM, DKI, and DCE-MRI scans before surgery. And then these data were randomly divided into training sets (n=154) and test sets (n=38) at a ratio of 8∶2. The three-dimensional volume region of interest of the tumor was manually delineated on the perfusion fraction (f), perfusion related diffusion coefficient (D^*), real diffusion coefficient (D), mean diffusivity (MD) and mean kurtosis (MK) parameter maps and the second phase of dynamic contrast-enhanced MRI, and radiomics features were extracted. The Z-score normalization was used for feature normalization, and the Select K Best, max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to single out the most valuable radiomic features. The parametric map models and a combined model were established by logistic regression (LR) classifier, and the stability of the models was verified by the 5-fold cross-validation. The receive operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the efficacy of the model. In addition, the DeLong test was used to compare the models, and decision curve analysis (DCA) was used to evaluate the models.Results A total of 2286 radiomics features were extracted from each ROI, and 7, 6, 7, 6, 7, 12 and 10 features were selected from the f, D^*, D, MD, and MK parametric maps, the second phase of dynamic contrast-enhanced MRI (DCE-2) and combined sequence, respectively, which were related to breast cancer HER-2 status. The AUC of the f, D^*, D, MD, and MK models and the DCE-2 model in the test group were 0.693, 0.679, 0.586, 0.682, 0.661 and 0.732, respectively. The AUC of the combined model in the test group was 0.861 (95% CI: 0.775-0.958). The sensitivity and specificity were 100.0% and 71.4%. By DeLong's test, in the training set there were statistically significant differences between combined model and the f model, the D model, the D^* model, the MD model, the MK model and the DCE-2 model (P<0.05). The results showed that the combined model was better than the single parameter diagram model in predicting the status of HER-2.Conclusions The combined radiomics model based on DCE-MRI, IVIM and DKI can better predict the expression status of HER-2 in breast cancer patients, which is important for the diagnosis, treatment and prognosis of breast cancer.

[Keywords] human epidermal growth factor receptor 2；breast cancer；diffusion kurtosis imaging；intravoxel incoherent motion；radiomics；magnetic resonance imaging

Contributor Information

ZHAO Xiaomeng¹ SHAO Shuo² ZHENG Ning^2* CUI Jingjing³ LIU Shihan¹ WU Jianwei¹

¹ Clinical Medical College, Jining Medical University, Jining 272013, China

² Magnetic Resonance Imaging Room, Jining First People's Hospital, Jining 272000, China

³ United Imaging Intelligence Medical Technology Co., Ltd., Beijing 100089, China

Corresponding author: ZHENG N, E-mail: zhengning_369@163.com

Conflicts of interest None.

Publication Information

Received 2024-03-19

Accepted 2024-06-06

DOI: 10.12015/issn.1674-8034.2024.07.018

Text

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