CASIA OpenIR  > 中国科学院分子影像重点实验室
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study
Gu, Jionghui1,2; Tong, Tong2,3; He, Chang1; Xu, Min1; Yang, Xin2,3; Tian, Jie2,3,4; Jiang, Tianan1,5; Wang, Kun2,3
Source PublicationEUROPEAN RADIOLOGY
ISSN0938-7994
2021-10-15
Pages11
Abstract

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.

KeywordBreast cancer Deep learning Neoadjuvant chemotherapy Ultrasonography Treatment outcome
DOI10.1007/s00330-021-08293-y
WOS KeywordPATHOLOGICAL RESPONSE ; ULTRASOUND ; INDEX
Indexed BySCI
Language英语
Funding ProjectMinistry 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
Funding OrganizationMinistry 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
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000707537100003
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46210
Collection中国科学院分子影像重点实验室
Corresponding AuthorJiang, Tianan; Wang, Kun
Affiliation1.Zhejiang Univ, Coll Med, Affiliated Hosp 1, Dept Ultrasound, Hangzhou 310003, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Beihang Univ, Sch Med & Engn, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
5.Zhejiang Prov Key Lab Pulsed Elect Field Technol, Hangzhou, Zhejiang, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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