期刊
BREAST
卷 60, 期 -, 页码 90-97出版社
CHURCHILL LIVINGSTONE
DOI: 10.1016/j.breast.2021.09.005
关键词
Radiomics; Endocrine resistance; Hormone receptor-positive; Breast cancer
资金
- National Science and Technology Major Project [2020ZX09201021]
- National Natural Science Foundation of China [82003311, 81572596, 81972471]
- Guangzhou Science and Technology Program [202102010272]
- Medical Artificial Intelligence Project of Sun Yat-Sen Memorial Hospital [YXRGZN201902]
- Sun YatSen Clinical Research Cultivating Program [SYS-Q-202004, SYS-C-201801]
- Sun Yat-sen Memorial Hospital Yat-Sen scientific research launch project [YXQH201920]
- Guangdong Medical Science and Technology Program [A2020391, A2020558]
- Natural Science Foundation of Guangdong Province [2017A030313828]
- Guangzhou Science and Technology Major Program [201704020131]
- Guangdong Science and Technology Department [2017B030314026]
- Sun Yat-Sen University Clinical Research 5010 Program [2018007]
- Tencent Charity Foundation [SYSU-8100020200311-0001, SYSU-05160-20200506-0001]
This study developed and validated a combined radiomic-clinical model using preoperative multiparametric MRI to predict endocrine resistance in non-metastatic breast cancer patients, showing superior performance compared to clinical and radiomic models alone in both training and validation cohorts.
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/).
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