Predictive value of dual-energy CT quantitative parameters in determining breast cancer molecular subtypes and EGFR expression
10.3760/cma.j.cn115807-20241018-00322
- VernacularTitle:双能量CT定量参数预测乳腺癌分子亚型及EGFR表达的价值
- Author:
Bing SUN
1
;
Yinshi ZHENG
1
;
Kunpeng FENG
1
;
Mu YUAN
1
;
Hongmei CHEN
1
;
Wenqi HUANG
1
Author Information
1. 商丘市第一人民医院CT室，商丘 476000
- Publication Type:Journal Article
- Keywords:
Dual-energy CT;
Breast cancer;
Molecular subtypes;
Epidermal growth factor receptor
- From:
Chinese Journal of Endocrine Surgery
2025;19(2):213-217
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the predictive potential of dual-energy CT (DECT) quantitative parameters in identifying breast cancer molecular subtypes and the expression of the epidermal growth factor receptor (EGFR) .Methods:A cohort of 97 breast cancer patients, treated between Jun. 2022 and Jun. 2024 were selected. The study compared DECT parameters-such as iodine concentration (IC) , normalized iodine concentration (NIC) , spectral curve slope (λ HU) , and effective atomic number (Z eff) in both arterial and venous phases across different molecular subtypes. A multiclass logistic regression model was employed to assess the parameters' value in predicting molecular subtypes, while a binary logistic regression model was used to evaluate their predictive value for EGFR expression. Results:Multiple Logistic regression analysis showed that after adjusting for confounder age and family history, IC ( OR=1.72, 2.78, 3.05) , NIC ( OR=2.52, 1.94, 2.93) , λ HU ( OR=2.08, 2.54, 3.17) and Z eff ( OR=2.03, 2.30, 2.37) at arterial stage were independently correlated with the molecular subtypes of breast cancer ( P<0.05) . Binary logistic regression analysis, adjusted for tumor size and lymph node metastasis, identified arterial phase IC ( OR=3.45) , NIC ( OR=2.73) , λ HU ( OR=2.59) , and Z eff ( OR=1.76) as independent risk factors for EGFR-positive breast cancer ( P<0.05) . Conclusion:DECT quantitative parameters, particularly arterial phase IC, NIC, λ HU, and Zeff, offer valuable insights into the molecular subtyping of breast cancer and EGFR expression, thereby assisting in the development of personalized treatment strategies.
