Magnetic resonance imaging radiomics signatures for predicting endocrine resistance in hormone receptor-positive non-metastatic breast cancer

被引:4
|
作者
Yang, Yaping [1 ]
Li, Junwei [2 ]
Liu, Yajing [2 ]
Zhong, Ying [2 ]
Ren, Wei [2 ]
Tan, Yujie [2 ]
He, Zifan [2 ]
Li, Chenchen [2 ]
Ouyang, Jie [3 ]
Hu, Qiugen [4 ]
Yu, Yunfang [2 ,5 ]
Yao, Herui [1 ,2 ]
机构
[1] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Breast Tumor Ctr, Guangdong Prov Key Lab Malignant Tumor Epigenet &, Guangzhou, Peoples R China
[2] Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Phase 1 Clin Trial Ctr, Dept Med Oncol,Guangdong Prov Key Lab Malignant T, Yanjiang West Rd 107, Guangzhou 510120, Peoples R China
[3] Sun Yat Sen Univ, Tungwah Hosp, Dept Breast Surg, Dongguan, Peoples R China
[4] Southern Med Univ, Shunde Hosp, Dept Radiol, Foshan, Peoples R China
[5] Beijing Normal Univ Hong Kong Baptist Univ United, Div Sci & Technol, AI & Digital Media Concentrat Program, Zhuhai, Peoples R China
来源
BREAST | 2021年 / 60卷
基金
中国国家自然科学基金;
关键词
Radiomics; Endocrine resistance; Hormone receptor-positive; Breast cancer; ESTROGEN-RECEPTOR; RECURRENCE; TAMOXIFEN; THERAPY; CONSENSUS; KI-67;
D O I
10.1016/j.breast.2021.09.005
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: One-third of patients with hormone receptor (HR)-positive breast cancers fail to respond to hormone therapy, and some patients even progress within two years of adjuvant endocrine therapy (ET) toward primary endocrine resistance. However, there is no effective way to predict endocrine resistance. Objective: To build a model that incorporates the radiomic signature of pretreatment magnetic resonance imaging (MRI) with clinical information to predict endocrine resistance. Methods: Clinical data of non-metastatic breast cancer patients diagnosed between May 1, 2015 and December 31, 2018 and preoperative dynamic contrast-enhanced magnetic resonance imaging (DCEMRI) were retrospectively collected from three hospitals in China. The significant clinicopathological characteristics and radiomic signatures were included in multivariable logistic regression to establish a combined model to predict endocrine resistance in the training set, and validate the internal and external validation set. Results: A total of 744 female non-metastatic breast cancer patients from three hospitals in China were included. In the training cohort, the AUC of the Radiomic-Clinical combined model to predict endocrine resistance was 0.975, which was higher than clinical model (0.849), IHC4 model (0.682) and similar as radiomic model (0.941). Also, the AUC of the combined model in the internal (0.921) and external validation cohort (0.955) were higher than clinical model and IHC4 model. The sensitivity of combined model was higher than radiomic alone, and got the best thresholding of the AUC. Conclusion: This study developed and validated a pretreatment multiparametric MRI-based radiomicclinical combined model and showed good performance in predicting endocrine resistance. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:90 / 97
页数:8
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