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Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer

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Abstract

To explore radiomic features of pharmacokinetic dynamic contrast-enhanced (Pk-DCE) MRI on the extensive Tofts model to diagnose breast cancer and predict molecular phenotype. Breast lesions enrolled must undergo Pk-DCE MRI before treatment or puncture, and be identified as primary lesions by pathology. Ktrans, Kep, Ve and Vp were generated on the extensive Tofts model. Radiomic features (histogram, geometry and texture features) were extracted from parametric maps and selected by LASSO. The subjects were divided into training and validation cohort with a ratio of 4:1 to construct model in diagnosis of breast cancer. Feature analysis was made to predict the molecular phenotype. Area under curve (AUC), sensitivity, specificity and accuracy were used to evaluate radiomic features. DeLong’s test was performed to compare AUC values. 228 breast lesions met the criteria were used to discrimination and 126 malignant lesions were used to study molecular phenotypes. The number of training cohort and validation cohort were 182 and 46, respectively. The AUC of Ktrans, Kep, Ve, and Vp was 0.95, 0.93, 0.89, and 0.96, and their accuracy was 85%, 89%, 89%, 94% respectively in diagnosis of breast lesions, while their AUC was 0.71 to 0.77, 0.61 to 0.68, and 0.67 to 0.74 to predict ER/PR, Her-2, and Ki-67. There was no significant difference among parameters (P > 0.05). Radiomic features based on Pk-DCE MRI have an advantage to diagnose breast cancer and less ability to predict molecular phenotypes, which are beneficial to guide clinical treatment of breast lesions in some extent.

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Abbreviations

Pk-DCE MRI:

Pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging

HR:

Hormone receptor

ER:

Estrogen receptor

PR:

Progesterone receptor

Her-2:

Human epidermal growth factor receptor 2

ROC curve:

Receiver operating characteristic curve

OR:

Odds ratio

AUC:

Area under the curve

ROI:

Region of interest

VIF:

Variance inflation factor

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Acknowledgements

This study was supported by the grant from Natural Science Foundation of Jiangsu Province (Grant No. BK20170626 to G. F.) and Jiangsu Provincial Natural Science Foundation (Grant No. 19KJB520025 to Xiaoyu Zhou). All authors thank Mr. Zhoushe Zhao, a scientist at GE Healthcare.

Author information

Author notes

  1. Xiaoyu Zhou and Feng Gao have contributed equally to this work.

Authors and Affiliations

  1. Research Center of Internet Things (Sensory Mine), China University of Mining and Technology, Xuzhou, People’s Republic of China

    Xiaoyu Zhou

  2. Faculty of Mechanical Electronic and Information Engineering, Jiangsu Vocational College of Finance and Economics, Huai’an, People’s Republic of China

    Xiaoyu Zhou

  3. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, People’s Republic of China

    Feng Gao

  4. GE Healthcare China, Shanghai, People’s Republic of China

    Shaofeng Duan

  5. Department of Pathology, The Affiliated Huai’an No. 1 People’s Hospital of Nanjing Medical University, Huai’an, People’s Republic of China

    Lianmei Zhang

  6. Department of Radiology, The Affiliated Huai’an No. 1 People’s Hospital of Nanjing Medical University, Huanghe Road No. 1, Huai’an, 223300, Jiangsu Province, People’s Republic of China

    Yan Liu & Genji Bai

  7. Department of Medical Imaging, Jiangsu University, Zhenjiang, People’s Republic of China

    Junyi Zhou

  8. Department of Nuclear Medicine, The Affiliated Huai’an No. 1 People’s Hospital of Nanjing Medical University, Huanghe Road No.1, Huai’an, 223300, Jiangsu Province, People’s Republic of China

    Weijing Tao

Authors
  1. Xiaoyu Zhou
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  2. Feng Gao
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  3. Shaofeng Duan
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  4. Lianmei Zhang
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  5. Yan Liu
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  6. Junyi Zhou
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  7. Genji Bai
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  8. Weijing Tao
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Corresponding authors

Correspondence to Genji Bai or Weijing Tao.

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The authors declare no competing financial interests.

Ethical approval

This study was approved by the Ethics Committee of local hospital.

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Informed consent was obtained from all study participants from October 2016 to June 2018.

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Zhou, X., Gao, F., Duan, S. et al. Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer. Phys Eng Sci Med 43, 517–524 (2020). https://doi.org/10.1007/s13246-020-00852-9

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  • DOI: https://doi.org/10.1007/s13246-020-00852-9

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