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

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases

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

Radiomic Signatures for Predicting Receptor Status in Breast Cancer Brain Metastases

Xiao Luo et al. Front Oncol. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of participants. ER, estrogen receptor; P, progesterone receptor; HER2, human epidermal growth factor receptor 2.
Figure 2
Figure 2
The flowchart of radiomic analysis. T1CE, contrast-enhanced T1-weighted imaging; T2WI, T2-weighted imaging; T2 FLAIR, T2 fluid-attenuated inversion recovery; LASSO, least absolute shrinkage selection operator; ROC, receiver operating characteristic curve.
Figure 3
Figure 3
Receptor switch in BCBM and radiomics predicting receptor status in the test set. Receptor (A) and subtype (B) switch in BCBM; the prediction results for BCBM (C) BCBM, breast cancer brain metastases; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2.
Figure 4
Figure 4
The confusion matrices and ROCs of combination radiomic signatures in test set. Confusion matrices for ER (A), PR (B) and HER2 (C); ROCs for ER (D), PR (E) and HER2 (F) ROC, receiver operating characteristic curve; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2; AUC, area under the curve; T1CE, contrast-enhanced T1-weighted imaging; T2WI, T2-weighted imaging; T2 FLAIR, T2 fluid-attenuated inversion recovery; combination, combination features of three sequences above.
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