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

  • Qianqian Xiong
  • Xuezhi Zhou
  • Zhenyu Liu
  • Chuqian Lei
  • Ciqiu Yang
  • Mei Yang
  • Liulu Zhang
  • Teng Zhu
  • Xiaosheng Zhuang
  • Changhong Liang
  • Zaiyi Liu
  • Jie TianEmail author
  • Kun WangEmail author
Research Article
  • 72 Downloads

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.

Keywords

Breast cancer Radiomics MRI Neoadjuvant chemotherapy Insensitive 

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

Notes

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].

Compliance with ethical standards

Conflict of interest

We declare that none of the authors have any conflict of interest.

Ethical approval

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.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

12094_2019_2109_MOESM1_ESM.docx (361 kb)
Supplementary material 1 (DOCX 361 kb)

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Copyright information

© Federación de Sociedades Españolas de Oncología (FESEO) 2019

Authors and Affiliations

  1. 1.Department of Breast Cancer, Cancer CenterGuangdong Provincial People’s Hospital and Guangdong Academy of Medical SciencesGuangzhouChina
  2. 2.The Second School of Clinical MedicineSouthern Medical UniversityGuangzhouChina
  3. 3.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina
  4. 4.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of SciencesBeijingChina
  5. 5.Shantou University Medical CollegeShantouChina
  6. 6.Department of RadiologyGuangdong Provincial People’s Hospital & Guangdong Academy of Medical SciencesGuangzhouChina
  7. 7.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang UniversityBeijingChina

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