Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer

Miao He; Yu Hu; Dongdong Wang; Meili Sun; Huijie Li; Peng Yan; Yingxu Meng; Ran Zhang; Li Li; Dexin Yu; Xiuwen Wang

doi:10.3389/fonc.2022.852809

Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer

Front Oncol. 2022 Apr 7:12:852809. doi: 10.3389/fonc.2022.852809. eCollection 2022.

Authors

Miao He¹, Yu Hu¹, Dongdong Wang², Meili Sun^{3

4}, Huijie Li⁵, Peng Yan^{3

4}, Yingxu Meng⁶, Ran Zhang⁷, Li Li¹, Dexin Yu², Xiuwen Wang¹

Affiliations

¹ Department of Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
² Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
³ Department of Oncology, Jinan Central Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
⁴ Department of Oncology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
⁵ Department of Oncology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
⁶ Department of Comprehensive Section of Medical Affairs, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
⁷ Huiying Medical Technology Co. Ltd, Beijing, China.

Abstract

Objective: This study aims to assess the performance of machine learning (ML)-based contrast-enhanced CT radiomics analysis for predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer.

Methods: This retrospective study analyzed 83 patients with breast cancer liver metastases. Radiomics features were extracted from arterial phase, portal venous phase, and delayed phase images, respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and validation sets consisted of 58 and 25 cases. Variance threshold, SelectKBest, and LASSO logistic regression model were employed for feature selection. The ML classifiers were K-nearest-neighbor algorithm (KNN), support vector machine (SVM), XGBoost, RF, LR, and DT, and the performance of classifiers was evaluated by ROC analysis.

Results: The SVM classifier had the highest score in portal venous phase. The results were as follows: The AUC value of the poor prognosis group in validation set was 0.865, the sensitivity was 0.77, and the specificity was 0.83. The AUC value of the good prognosis group in validation set was 0.865, the sensitivity was 0.83, and the specificity was 0.77. In arterial phase, the XGBoost classifier had the highest score. The AUC value of the poor prognosis group in validation set was 0.601, the sensitivity was 0.69, and the specificity was 0.38. The AUC value of the good prognosis group in validation set was 0.601, the sensitivity was 0.38, and the specificity was 0.69. The LR classifier had the highest score in delayed phase. The AUC value of poor prognosis group in validation set was 0.628, the sensitivity was 0.62, and the specificity was 0.67. The AUC value of the good prognosis group in validation set was 0.628, the sensitivity was 0.67, and the specificity was 0.62.

Conclusion: Radiomics analysis represents a promising tool in predicating the efficacy of anti-HER2 therapy for patients with liver metastases from breast cancer. The ROI in portal venous phase is most suitable for predicting the efficacy of anti-HER2 therapy, and the SVM algorithm model has the best efficiency.

Keywords: CT; anti-HER2 therapy; breast cancer; liver metastases; radiomics.