Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy
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To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC).
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.
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.
The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.
KeywordsBreast cancer Radiomics MRI Neoadjuvant chemotherapy Insensitive
Magnetic resonance imaging
Human epidermal growth factor receptor-2
Dynamic contrast enhanced
Field of view
In situ hybridization
Least absolute shrinkage and selection operator
Area under the receiver operating characteristic curve
Receiver operating characteristic
Positive predictive value
Negative predictive value
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.
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 was obtained from all individual participants included in the study.
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