Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
| Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer : A multicenter study | |
Li, Bao1,2; Li, Fengling3,4; Liu, Zhenyu2,9 ; Xu, FangPing5,6; Ye, Guolin7; Li, Wei7; Zhang, Yimin8; Zhu, Teng6,10; Shao, Lizhi2; Chen, Chi2,11; Sun, Caixia2,11; Qiu, Bensheng1; Bu, Hong3,4; Wang, Kun6,10; Tian, Jie1,2,11,12
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| Source Publication | BREAST
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| ISSN | 0960-9776 |
| 2022-12-01 | |
| Volume | 66Pages:183-190 |
| Corresponding Author | Bu, Hong(hongbu@scu.edu.cn) ; Wang, Kun(wangkun@gdph.org.cn) ; Tian, Jie(jie.tian@ia.ac.cn) |
| Abstract | 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. |
| Keyword | Breast cancer Neoadjuvant chemotherapy Pathological complete response Whole-slide image Deep learning |
| DOI | 10.1016/j.breast.2022.10.004 |
| WOS Keyword | SUBTYPES |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Oncology ; Obstetrics & Gynecology |
| WOS Subject | Oncology ; Obstetrics & Gynecology |
| WOS ID | WOS:000878689800006 |
| Publisher | CHURCHILL LIVINGSTONE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50703 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Bu, Hong; Wang, Kun; Tian, Jie |
| Affiliation | 1.Univ Sci & Technol China, Ctr Biomed Imaging, Hefei 230026, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing 100190, Peoples R China 3.Sichuan Univ, West China Hosp, Dept Pathol, Chengdu 610041, Peoples R China 4.Sichuan Univ, West China Hosp, Inst Clin Pathol, Chengdu 610041, Peoples R China 5.Guangdong Prov Peoples Hosp, Dept Pathol, Guangzhou 510080, Peoples R China 6.Guangdong Acad Med Sci, Guangzhou 510080, Peoples R China 7.First Peoples Hosp Foshan, Foshan 528000, Peoples R China 8.Shantou Cent Hosp, Diag & Treatment Ctr Breast Dis, Clin Res Ctr, Shantou 515000, Peoples R China 9.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China 10.Guangdong Prov Peoples Hosp, Guangzhou 510080, Peoples R China 11.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China 12.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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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