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Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy

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Abstract

Purpose

To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC).

Methods

A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller–Payne grading system was applied to assess the response to NAC. Grade 1–2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation.

Results

Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1–2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848–1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis.

Conclusion

The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.

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Abbreviations

NAC:

Neoadjuvant chemotherapy

MRI:

Magnetic resonance imaging

HER2:

Human epidermal growth factor receptor-2

DCE:

Dynamic contrast enhanced

T2WI:

T2-weighted imaging

DWI:

Diffusion-weighted imaging

TR:

Repetition time

TE:

Echo time

FOV:

Field of view

ER:

Estrogen receptor

PR:

Progesterone receptor

ISH:

In situ hybridization

LASSO:

Least absolute shrinkage and selection operator

AUC:

Area under the receiver operating characteristic curve

ROC:

Receiver operating characteristic

GOF:

Goodness-of-fit

PPV:

Positive predictive value

NPV:

Negative predictive value

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Acknowledgements

We thank all the patients for their participation and their physicians for their remarkable efforts. This study was funded by the National Key R&D Program of China [Grant No.: 2017YFC1309100]; Natural Science Foundation of Guangdong Province, China [grant numbers: 2017A030313882]; National Natural Science Foundation of China [Grant Nos.: 81871513, 81772012, 81501549]; the Beijing Natural Science Foundation [Grant No.: 7182109]; Science and Technology Planning Project of Guangdong Province [Grant No.: 2017B020227012]; and CSC0-constant Rui Tumor Research Fund, China [Grant No.: Y-HR2016-067].

Author information

Author notes

  1. Qianqian Xiong, Xuezhi Zhou and Zhenyu Liu contributed equally to this study, and should be considered as co-first authors.

Authors and Affiliations

  1. Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital and Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Qianqian Xiong, Chuqian Lei, Ciqiu Yang, Mei Yang, Liulu Zhang, Teng Zhu, Xiaosheng Zhuang & Kun Wang

  2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China

    Qianqian Xiong, Chuqian Lei & Kun Wang

  3. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, 710126, Shaanxi, China

    Xuezhi Zhou & Jie Tian

  4. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 100190, No. 95 Zhongguancun East Road, Beijing, China

    Zhenyu Liu & Jie Tian

  5. Shantou University Medical College, Shantou, 515041, Guangdong, China

    Xiaosheng Zhuang

  6. Department of Radiology, Guangdong Provincial People’s Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China

    Changhong Liang & Zaiyi Liu

  7. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, 100191, Beijing, China

    Jie Tian

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  1. Qianqian Xiong
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Correspondence to Jie Tian or Kun Wang.

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All procedures performed in this study, which involved human participants, were in accordance with the ethical standards of the institutional and/or nation research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Xiong, Q., Zhou, X., Liu, Z. et al. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy. Clin Transl Oncol 22, 50–59 (2020). https://doi.org/10.1007/s12094-019-02109-8

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