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Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer : A multicenter study

文献类型：期刊论文

作者Li, Bao11,12; Li, Fengling9,10; Liu, Zhenyu4,11; Xu, FangPing7,8; Ye, Guolin6; Li, Wei6; Zhang, Yimin5; Zhu, Teng3,7; Shao, Lizhi11; Chen, Chi2,11
刊名BREAST
出版日期2022-12-01
卷号66页码:183-190
ISSN号0960-9776
关键词Breast cancer Neoadjuvant chemotherapy Pathological complete response Whole-slide image Deep learning
DOI10.1016/j.breast.2022.10.004
通讯作者Bu, Hong(hongbu@scu.edu.cn) ; Wang, Kun(wangkun@gdph.org.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Introduction: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. Materials and methods: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsyproven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. Results: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). Conclusion: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.
WOS关键词SUBTYPES
资助项目National Key Research and Development Plan of China[2021YFF1201003] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[92059103] ; Youth Innovation Promotion Association CAS[2019136] ; Science and Technology Planning Project of Guangzhou City[202002030236] ; Beijing Medical Award Foundation[YXJL-2020-0941-0758] ; Science and Technology Special Fund of Guangdong Provincial People's Hospital[Y012018218] ; CSCO-Hengrui Cancer Research Fund[Y-HR2016-067] ; Guangdong Provincial Department of Education Characteristic Innovation Project[2015KTSCX080] ; 1.3.5 Project for Disciplines of Excellence[ZYGD18012] ; Technological Innovation Project of Chengdu New Industrial Technology Research Institute[2017-CY02-00026-GX]
WOS研究方向Oncology ; Obstetrics & Gynecology
语种英语
出版者CHURCHILL LIVINGSTONE
WOS记录号WOS:000878689800006
资助机构National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; Science and Technology Planning Project of Guangzhou City ; Beijing Medical Award Foundation ; Science and Technology Special Fund of Guangdong Provincial People's Hospital ; CSCO-Hengrui Cancer Research Fund ; Guangdong Provincial Department of Education Characteristic Innovation Project ; 1.3.5 Project for Disciplines of Excellence ; Technological Innovation Project of Chengdu New Industrial Technology Research Institute
源URL[http://ir.ia.ac.cn/handle/173211/50702]  
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Bu, Hong; Wang, Kun; Tian, Jie
作者单位1.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
3.Guangdong Prov Peoples Hosp, Guangzhou 510080, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
5.Shantou Cent Hosp, Diag & Treatment Ctr Breast Dis, Clin Res Ctr, Shantou 515000, Peoples R China
6.First Peoples Hosp Foshan, Foshan 528000, Peoples R China
7.Guangdong Acad Med Sci, Guangzhou 510080, Peoples R China
8.Guangdong Prov Peoples Hosp, Dept Pathol, Guangzhou 510080, Peoples R China
9.Sichuan Univ, West China Hosp, Inst Clin Pathol, Chengdu 610041, Peoples R China
10.Sichuan Univ, West China Hosp, Dept Pathol, Chengdu 610041, Peoples R China
推荐引用方式
GB/T 7714		 Li, Bao,Li, Fengling,Liu, Zhenyu,et al. Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer : A multicenter study[J]. BREAST,2022,66:183-190.
APA Li, Bao.,Li, Fengling.,Liu, Zhenyu.,Xu, FangPing.,Ye, Guolin.,...&Tian, Jie.(2022).Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer : A multicenter study.BREAST,66,183-190.
MLA Li, Bao,et al."Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer : A multicenter study".BREAST 66(2022):183-190.

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来源：自动化研究所

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