CASIA OpenIR  > 中国科学院分子影像重点实验室
Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study
Shao, Lizhi1,2; Liu, Zhenyu2,3; Feng, Lili4; Lou, Xiaoying5; Li, Zhenhui6; Zhang, Xiao-Yan7; Wan, Xiangbo4; Zhou, Xuezhi2,8; Sun, Kai2,8; Zhang, Da-Fu6; Wu, Lin9; Yang, Guanyu1,10; Sun, Ying-Shi7; Xu, Ruihua11; Fan, Xinjuan5; Tian, Jie2,3,8,12
Source PublicationANNALS OF SURGICAL ONCOLOGY
ISSN1068-9265
2020-07-29
Pages11
Corresponding AuthorXu, Ruihua(xurh@sysucc.org.cn)
AbstractBackground The aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning. Patients and Methods A total of 981 consecutive patients with evaluation of response according to tumor regression grade (TRG) who received nCRT were retrospectively recruited from four hospitals (primary cohort and external validation cohort 1-3); both pretreatment multiparametric MRI (mp-MRI) and whole slide image (WSI) of biopsy specimens were available for each patient. Quantitative image features were extracted from mp-MRI and WSI and used to construct a radiopathomics signature (RPS) powered by an artificial-intelligence model. Models based on mp-MRI or WSI alone were also constructed for comparison. Results The RPS showed overall accuracy of 79.66-87.66% in validation cohorts. The areas under the curve of RPS at specific response grades were 0.98 (TRG0), 0.93 (<= TRG1), and 0.84 (<= TRG2). RPS at each grade of pathological response revealed significant improvement compared with both signatures constructed without combining multiscale tumor information (P < 0.01). Moreover, RPS showed relevance to distinct probabilities of overall survival and disease-free survival in patients with LARC who underwent nCRT (P < 0.05). Conclusions The results of this study suggest that radiopathomics, combining both radiological information of the whole tumor and pathological information of local lesions from biopsy, could potentially predict discrepancies of pathological response prior to nCRT for better treatment planning.
DOI10.1245/s10434-020-08659-4
WOS KeywordRADIOMICS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; National Key R&D Program of China[2016YFA0100902] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB01030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Youth Innovation Promotion Association CAS[2019136]
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key R&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS Research AreaOncology ; Surgery
WOS SubjectOncology ; Surgery
WOS IDWOS:000553745500002
PublisherSPRINGER
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40281
Collection中国科学院分子影像重点实验室
Corresponding AuthorXu, Ruihua
Affiliation1.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Sun Yat Sen Univ, Dept Radiat Oncol, Affiliated Hosp 6, Guangzhou, Peoples R China
5.Sun Yat Sen Univ, Dept Pathol, Affiliated Hosp 6, Guangzhou, Guangdong, Peoples R China
6.Kunming Med Univ, Yunnan Canc Hosp, Yunnan Canc Ctr, Dept Radiol,Affiliated Hosp 3, Kunming, Yunnan, Peoples R China
7.Peking Univ, Dept Radiol, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing,Canc Hosp & Inst, Beijing, Peoples R China
8.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
9.Kunming Med Univ, Yunnan Canc Hosp, Yunnan Canc Ctr, Dept Pathol,Affiliated Hosp 3, Kunming, Yunnan, Peoples R China
10.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, LIST, Nanjing, Peoples R China
11.Sun Yat Sen Univ, Collaborat Innovat Ctr Canc Med, State Key Lab Oncol South China, Canc Ctr, Guangzhou, Peoples R China
12.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Shao, Lizhi,Liu, Zhenyu,Feng, Lili,et al. Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study[J]. ANNALS OF SURGICAL ONCOLOGY,2020:11.
APA Shao, Lizhi.,Liu, Zhenyu.,Feng, Lili.,Lou, Xiaoying.,Li, Zhenhui.,...&Tian, Jie.(2020).Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study.ANNALS OF SURGICAL ONCOLOGY,11.
MLA Shao, Lizhi,et al."Multiparametric MRI and Whole Slide Image-Based Pretreatment Prediction of Pathological Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer: A Multicenter Radiopathomic Study".ANNALS OF SURGICAL ONCOLOGY (2020):11.
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