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Clinical Article
Imaging radiomics features based on DCE-MRI combined with ADC in predicting expression level of Ki-67 in breast cancer
HAN Jianjian  MA Wenjun  MA Peiqi  XIE Yuhai 

HAN J J, MA W J, MA P Q, et al. Imaging radiomics features based on DCE-MRI combined with ADC in predicting expression level of Ki-67 in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(8): 63-67, 85. DOI:10.12015/issn.1674-8034.2023.08.010.

[Abstract] Objective To investigate the clinical value of imaging radiomics features based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with apparent diffusion coefficient (ADC) in predicting the expression level of Ki-67 in breast cancer.Materials and Methods MRI images of 234 patients with breast cancer confirmed by pathology from December 2018 to December 2021 were retrospectively analyzed. According to postoperative immunohistochemical results, the tumors were divided into the Ki-67 high expression group (n=180) and low expression group (n=54). 1906 radiomics features were extracted form the first phase of the DCE-MRI by semi-automatic separation method. Using intraclass correlation coefficient (ICC), the linear correlation analysis and the least absolute shrinkage and selection operator (LASSO), four features were selected to construct the radiomics model. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic effectiveness of the radiomics, average ADC values and combined models. Calibration curves and decision curves were used to evaluate the clinical usefulness of the predictive model.Results A total of 1906 features were extracted from the tumor body, 207 features were excluded by ICC analysis, 1626 features were excluded by linear correlation analysis, and the remaining 73 features were selected by LASSO dimensionality reduction to select 4 optimal omics features. Four radiomics features and the average ADC values were significantly different between two groups (P<0.05). Radiomics model, the average ADC value and the combined model predicted that the area under the curve (AUC) of Ki-67 high expression were 0.820, 0.676 and 0.856, respectively, with statistically significant differences each other (P<0.05). The combined model had the best predictive efficiency for Ki-67 expression, and its AUC, sensitivity and specificity were 0.856, 88.3% and 74.1%, calibration curves and decision curves showed that the combined model had clinical application value.Conclusions The combined model which constructed by the images radiomics features based on DCE-MRI and the average ADC values has high efficacy in predicting Ki-67 expression in breast cancer.The combined model is superior to the radiomics model and the average ADC value.
[Keywords] breast cancer;Ki-67;radiomics;dynamic contrast-enhanced;diffusion weighted imaging;magnetic resonance imaging

HAN Jianjian1   MA Wenjun2   MA Peiqi3   XIE Yuhai2*  

1 Department of Radiology, the First Affiliated Hospital of Wannan Medical College, Wuhu 241000, China

2 Department of Radiology, Taihe People's Hospital/Taihe Hospital Affiliated to Wannan Medical College, Fuyang 236600, China

3 Department of Radiology, Fuyang People's Hospital, Fuyang 236000, China

Corresponding author: Xie YH, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Scientific Research Project of Wannan Medical College (No. JXYY202139).
Received  2022-09-16
Accepted  2023-07-21
DOI: 10.12015/issn.1674-8034.2023.08.010
HAN J J, MA W J, MA P Q, et al. Imaging radiomics features based on DCE-MRI combined with ADC in predicting expression level of Ki-67 in breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(8): 63-67, 85. DOI:10.12015/issn.1674-8034.2023.08.010.

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