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

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 1^st postcontrast CE-MRI phase (CE₁) and multi-phases CE-MRI (CE_m),respectively. With selected features using a forward stepwise regression, 23 machine learning classifiers based on CE₁ and CE_m 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 CE_m were selected for the construction of machine learning models, respectively. The linear SVM based on CE_m 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:

• radiomics
• breast cancer
• neoadjuvant therapy
• machine learning
• magnetic resonance imaging

Acknowledgments

This study has received funding by Clinical Research Plan of SHDC(SHDC2020CR2008A). .

Abbreviations

AUC, area under the receiver operating characteristic curve; CE, contrast enhanced; NAT, neoadjuvant therapy; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; GLDM, gray level dependence matrix; ICC, intraclass correlation coefficient; ROC, receiver operating characteristic curve; ROI, region of interest; SVM, support vector machine; PCR, pathological complete response; RUS, random under sampling.

Data Sharing Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

Ethics Approval and Consent to Participate

Written informed consents were waived due to the study design. The medical ethics committee of Fudan University cancer hospital approved this retrospective study and confirmed that the data was anonymized and maintained with confidentiality.

Author Contributions

All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that they have no competing interests.