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Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study

文献类型：期刊论文

作者Gu, Jionghui1,5; Tong, Tong1,4; He, Chang5; Xu, Min5; Yang, Xin1,4; Tian, Jie1,3,4; Jiang, Tianan2,5; Wang, Kun1,4
刊名EUROPEAN RADIOLOGY
出版日期2021-10-15
页码11
ISSN号0938-7994
关键词Breast cancer Deep learning Neoadjuvant chemotherapy Ultrasonography Treatment outcome
DOI10.1007/s00330-021-08293-y
通讯作者Jiang, Tianan(tiananjiang@zju.edu.cn) ; Wang, Kun(kun.wang@ia.ac.cn)
英文摘要Objectives Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage. Methods In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration. Results In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC. Conclusions The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients. Key Points We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.
WOS关键词PATHOLOGICAL RESPONSE ; ULTRASOUND ; INDEX
资助项目Ministry of Science and Technology of China[2017YFA0205200] ; National Key R&D Program of China[2018YFC0114900] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[82027803] ; National Natural Science Foundation of China[81971623] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者SPRINGER
WOS记录号WOS:000707537100003
资助机构Ministry of Science and Technology of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS ; Project of High-Level Talents Team Introduction in Zhuhai City
源URL[http://ir.ia.ac.cn/handle/173211/46209]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Jiang, Tianan; Wang, Kun
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Zhejiang Prov Key Lab Pulsed Elect Field Technol, Hangzhou, Zhejiang, Peoples R China
3.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Ultrasound, Hangzhou 310003, Zhejiang, Peoples R China
推荐引用方式
GB/T 7714		 Gu, Jionghui,Tong, Tong,He, Chang,et al. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study[J]. EUROPEAN RADIOLOGY,2021:11.
APA Gu, Jionghui.,Tong, Tong.,He, Chang.,Xu, Min.,Yang, Xin.,...&Wang, Kun.(2021).Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.EUROPEAN RADIOLOGY,11.
MLA Gu, Jionghui,et al."Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study".EUROPEAN RADIOLOGY (2021):11.

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来源：自动化研究所

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