A deep learning prediction model for early evaluation of treatment response to neoadjuvant chemotherapy based on ultrasound images of breast cancer patients
10.3760/cma.j.cn131148-20221227-00881
- VernacularTitle:基于超声的深度学习影像组学模型早期预测乳腺癌新辅助化疗效果的研究
- Author:
Feihong YU
1
;
Yanyan ZHANG
;
Shumei MIAO
;
Cuiying LI
;
Jing DENG
;
Bin YANG
;
Xinhua YE
;
Yun LIU
;
Hui WANG
Author Information
1. 南京医科大学第一附属医院超声科，南京 210029
- Keywords:
Ultrasonography;
Neoadjuvant chemotherapy;
Deep learning;
Breast cancer
- From:
Chinese Journal of Ultrasonography
2023;32(7):614-620
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the feasibility of deep learning radiomics model in the prediction of neoadjuvant chemotherapy (NAC) response in breast cancer based on ultrasound images at an early stage.Methods:Between January 2018 and June 2021, 218 patients with breast cancer who underwent NAC were enrolled in the retrospective study. All patients received a full cycle of NAC before surgery and underwent standard ultrasound examination before NAC and after the second cycles of NAC. Of all the patients, 166 patients came from institution 1 (the First Affiliated Hospital of Nanjing Medical University) were allocated into a primary cohort.Based on the architecture of Resnet 50 convolutional neural, a deep learning prediction model was built.Further validation was performed in an external testing cohort ( n=52) from institution 2 (General Hospital of Eastern Theater Command, PLA). The clinical model was constructed using independent clinical variables. To evaluate the predictive performance, areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. Results:The Resnet 50 model predicted the response of NAC with accuracy. The deep learning model, achieving an AUC of 0.923 (95% CI=0.884-0.962) in the primary cohort and an AUC of 0.896 (95% CI=0.807-0.980) in the test cohort, outperformed the clinical model and also performed better than two radiologists′ prediction (all P<0.05). Furthermore, the two radiologists achieved a better predictive efficacy (AUC 0.832 and 0.808 for radiologists 1 and 2, respectively) when assisted by the DL model (all P<0.01). Conclusions:The deep learning radiomics model is able to predict therapy response in the early-stage of NAC for breast cancer patients, which could guide clinicians and provide benefit for timely treatment strategy adjustment.
