MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC

Qin Li; Qin Xiao; Jianwei Li; Shaofeng Duan; He Wang; Yajia Gu

doi:10.2147/CMAR.S271876

MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC

Cancer Manag Res. 2020 Oct 27:12:10603-10613. doi: 10.2147/CMAR.S271876. eCollection 2020.

Authors

Qin Li^#^{1

2}, Qin Xiao^#^{2

3}, Jianwei Li^{3

4}, Shaofeng Duan⁵, He Wang⁶, Yajia Gu^{2

3}

Affiliations

¹ Shanghai Institute of Medical Imaging, Shanghai, China.
² Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
³ Department of Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
⁴ Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
⁵ PDx, GE Healthcare, Shanghai, China.
⁶ Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.

^# Contributed equally.

Abstract

Purpose: To identify MRI-based radiomics signature (Rad-score) as a biomarker of risk stratification for disease-free survival (DFS) in patients with HER2-positive invasive breast cancer treated with trastuzumab-based neoadjuvant chemotherapy (NAC) and establish a radiomics-clinicoradiologic-based nomogram that combines Rad-score, MRI findings, and clinicopathological variables for DFS estimation.

Patients and methods: A total of 127 patients were divided into a training set and testing set according to the ratio of 7:3. Radiomic features were extracted from multiphase CE-MRI (CE_m). Rad-score was calculated using the LASSO (least absolute shrinkage and selection operator) regression analysis. The cutoff point of Rad-score to divide the patients into high- and low-risk groups was determined by receiver operating characteristic curve analysis. A Kaplan-Meier survival curves and the Log rank test were used to investigate the association of the Rad-score with DFS. Univariate and multivariate Cox proportional hazards model were used to determine the association of Rad-score, MRI features, and clinicopathological variables with DFS. A radiomics-clinicoradiologic-based nomogram combining the Rad-score, MRI features, and clinicopathological findings was plotted to validate the radiomic signatures for DFS estimation.

Results: The Rad-score stratified patients into high- and low-risk groups for DFS in the training set (P<0.0001) and was validated in the testing set (P=0.002). The radiomics-clinicoradiologic-based nomogram estimated DFS (training set: C-index=0.974, 95% confidence interval (CI)=0.954-0.994; testing set: C-index=0.917, 95% CI=0.842-0.991) better than the clinicoradiologic-based nomogram (training set: C-index=0.855, 95% CI=0.739-0.971; testing set: C-index=0.831, 95% CI=0.643-0.999).

Conclusion: The Rad-score is an independent biomarker for the estimation of DFS in invasive HER2-positive breast cancer with NAC and the radiomics-clinicoradiologic-based nomogram improved individualized DFS estimation.

Keywords: breast cancer; magnetic resonance imaging; prognosis; radiomics.