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Updated: Sep 13, 2025
Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
Published on: October 2, 2020
Radiotherapy improves survival in HER2-positive breast cancer with lung metastases: a retrospective study with
Tengfei Ren1, Xiangkun Wang2, Shengyuan Luo3,4
1Department of Hemangioma, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, China.
Background:
Human epidermal growth factor receptor 2 (HER2)-positive breast cancer is an aggressive subtype with a high risk of distant metastasis, particularly to the lungs. While systemic therapies have improved outcomes, the role of radiotherapy (RT) in the management of lung metastases remains uncertain.
Methods:
This retrospective study analyzed 248 HER2-positive breast cancer patients with lung metastases treated at two institutions between 2006 and 2021. Propensity score matching (PSM) was used to balance baseline characteristics between the RT and non-RT groups. Overall survival (OS) was assessed using Kaplan-Meier curves and Cox regression. A least absolute shrinkage and selection operator (LASSO)-Cox model was developed to identify prognostic factors, and its performance was evaluated using risk score visualization, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA).
Results:
RT significantly improved median OS both before (50.4 vs. 34.0 months, p < 0.001) and after PSM (51.5 vs. 32.3 months, p < 0.001). LASSO-Cox analysis confirmed RT as an independent prognostic factor. The predictive model demonstrated good discrimination (1- and 3-year AUCs of 0.716 and 0.722, respectively) and clinical utility by DCA.
Conclusion:
RT offers a significant survival benefit in HER2-positive breast cancer patients with lung metastases. AI-based modeling enhances prognostic accuracy and supports personalized treatment decisions.
