Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer

Qin Li, Qin Xiao, Jianwei Li, Zhe Wang, He Wang, Yajia Gu, Qin Li, Qin Xiao, Jianwei Li, Zhe Wang, He Wang, Yajia Gu

Abstract

Background: To assess the value of radiomics based on multiphases contrast-enhanced magnetic resonance imaging (CE-MRI) for early prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with human epithelial growth factor receptor 2 (HER2) positive invasive breast cancer.

Methods: A total of 127 patients with newly diagnosed primary HER2 positive invasive breast cancer underwent CE-MRI before NAT and performed surgery after NAT. Radiomic features were extracted from the 1st postcontrast CE-MRI phase (CE1) and multi-phases CE-MRI (CEm),respectively. With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE1 and CEm were constructed respectively for differentiating pCR and non-pCR patients. The performances of classifiers were assessed and compared by their accuracy, sensitivity, specificity and AUC (area under curve). The optimal machine learning classification was used to discriminate pCR vs non-pCR in mass and non-mass groups, uni-focal and unilateral multi-focal groups, respectively.

Results: For the task of pCR classification, 6 radiomic features from CE1 and 6 from CEm were selected for the construction of machine learning models, respectively. The linear SVM based on CEm outperformed the logistic regression model using CE1 with an AUC of 0.84 versus 0.69. In mass and non-mass enhancement groups, the accuracy of linear SVM achieved 84% and 76%. Whereas in unifocal and unilateral multifocal cases, 79% and 75% accuracy were achieved by linear SVM.

Conclusion: Multiphases CE-MRI imaging may offer more heterogeneity information in the tumor and provide a non-invasive approach for early prediction of pCR to NAT in patients with HER2-positive invasive breast cancer.

Keywords: breast cancer; machine learning; magnetic resonance imaging; neoadjuvant therapy; radiomics.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2021 Li et al.

Figures

Figure 1
Figure 1
Flow chart of patient recruitment in this study.
Figure 2
Figure 2
The source images of radiomics features.
Figure 3
Figure 3
ROC of linear SVM predicting pCR and non-pCR.
Figure 4
Figure 4
ROC of logistic regression predicting pCR and non-pCR.
Figure 5
Figure 5
Rad-score box plot for pCR classification based on the CE1 and CEm.

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