1. Nomogram prediction of the 70-gene signature (MammaPrint) binary and quartile categorized risk using medical history, imaging features and clinicopathological data among Chinese breast cancer patients
- Author
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Bo Pan, Ying Xu, Ru Yao, Xi Cao, Xingtong Zhou, Zhixin Hao, Yanna Zhang, Changjun Wang, Songjie Shen, Yanwen Luo, Qingli Zhu, Xinyu Ren, Lingyan Kong, Yidong Zhou, and Qiang Sun
- Subjects
Breast cancer ,70-gene signature (MammaPrint) ,Prognosis ,Nomogram ,Risk prediction ,Medicine - Abstract
Abstract Background The 70-gene signature (70-GS, MammaPrint) test has been recommended by the main guidelines to evaluate prognosis and chemotherapy benefit of hormonal receptor positive human epidermal receptor 2 negative (HR + /Her2−) early breast cancer (BC). However, this expensive assay is not always accessible and affordable worldwide. Based on our previous study, we established nomogram models to predict the binary and quartile categorized risk of 70-GS. Methods We retrospectively analyzed a consecutive cohort of 150 female patients with HR + /Her2− BC and eligible 70-GS test. Comparison of 40 parameters including the patients’ medical history risk factors, imaging features and clinicopathological characteristics was performed between patients with high risk (N = 62) and low risk (N = 88) of 70-GS test, whereas risk calculations from established models including Clinical Treatment Score Post-5 years (CTS5), Immunohistochemistry 3 (IHC3) and Nottingham Prognostic Index (NPI) were also compared between high vs low binary risk of 70-GS and among ultra-high (N = 12), high (N = 50), low (N = 65) and ultra-low (N = 23) quartile categorized risk of 70-GS. The data of 150 patients were randomly split by 4:1 ratio with training set of 120 patients and testing set 30 patients. Univariate analyses and multivariate logistic regression were performed to establish the two nomogram models to predict the the binary and quartile categorized risk of 70-GS. Results Compared to 70-GS low-risk patients, the high-risk patients had significantly less cardiovascular co-morbidity (p = 0.034), more grade 3 BC (p = 0.006), lower progesterone receptor (PR) positive percentage (p = 0.007), more Ki67 high BC (≥ 20%, p
- Published
- 2023
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