Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases

Xiao Luo; Hui Xie; Yadi Yang; Cheng Zhang; Yijun Zhang; Yue Li; Qiuxia Yang; Deling Wang; Yingwei Luo; Zhijun Mai; Chuanmiao Xie; Shaohan Yin

doi:10.3389/fonc.2022.878388

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases

Front Oncol. 2022 Jun 6:12:878388. doi: 10.3389/fonc.2022.878388. eCollection 2022.

Authors

Xiao Luo^{1

2}, Hui Xie^{1

2}, Yadi Yang^{1

2}, Cheng Zhang^{1

2}, Yijun Zhang^{1

3}, Yue Li^{1

4}, Qiuxia Yang^{1

2}, Deling Wang^{1

2}, Yingwei Luo^{1

2}, Zhijun Mai^{1

2}, Chuanmiao Xie^{1

2}, Shaohan Yin^{1

2}

Affiliations

¹ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
² Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
³ Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
⁴ Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou, China.

Abstract

Backgrounds: A significant proportion of breast cancer patients showed receptor discordance between primary cancers and breast cancer brain metastases (BCBM), which significantly affected therapeutic decision-making. But it was not always feasible to obtain BCBM tissues. The aim of the present study was to analyze the receptor status of primary breast cancer and matched brain metastases and establish radiomic signatures to predict the receptor status of BCBM.

Methods: The receptor status of 80 matched primary breast cancers and resected brain metastases were retrospectively analyzed. Radiomic features were extracted using preoperative brain MRI (contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2 fluid-attenuated inversion recovery, and combinations of these sequences) collected from 68 patients (45 and 23 for training and test sets, respectively) with BCBM excision. Using least absolute shrinkage selection operator and logistic regression model, the machine learning-based radiomic signatures were constructed to predict the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status of BCBM.

Results: Discordance between the primary cancer and BCBM was found in 51.3% of patients, with 27.5%, 27.5%, and 5.0% discordance for ER, PR, and HER2, respectively. Loss of receptor expression was more common (33.8%) than gain (18.8%). The radiomic signatures built using combination sequences had the best performance in the training and test sets. The combination model yielded AUCs of 0.89, 0.88, and 0.87, classification sensitivities of 71.4%, 90%, and 87.5%, specificities of 81.2%, 76.9%, and 71.4%, and accuracies of 78.3%, 82.6%, and 82.6% for ER, PR, and HER2, respectively, in the test set.

Conclusions: Receptor conversion in BCBM was common, and radiomic signatures show potential for noninvasively predicting BCBM receptor status.

Keywords: brain neoplasms; breast neoplasms; magnetic resonance imaging; radiomics; receptor.