CASIA OpenIR  > 中国科学院分子影像重点实验室
Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study
Feng, Lili1,5; Liu, Zhenyu6; Li, Chaofeng7; Li, Zhenhui9; Lou, Xiaoying2; Shao, Lizhi6,10; Wang, Yunlong1,5; Huang, Yan2; Chen, Haiyang1; Pang, Xiaolin1; Liu, Shuai1; He, Fang1; Zheng, Jian1; Meng, Xiaochun3; Xie, Peiyi3; Yang, Guanyu10; Ding, Yi11; Wei, Mingbiao1,5; Yun, Jingping8; Hung, Mien-Chie12,13,14,15; Zhou, Weihua16; Wahl, Dantel R.17; Lan, Ping4,5; Tian, Jie6; Wan, Xiangbo1,5
Source PublicationLANCET DIGITAL HEALTH
2022
Volume4Issue:1Pages:E8-E17
Corresponding AuthorTian, Jie(jie.tian@ia.ac.cn) ; Wan, Xiangbo(wanxbo@mail.sysu.edu.cn)
AbstractBackground Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. Methods In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Findings Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0.868 [95% CI 0.825-0.912]), and in validation cohort 1 (0.860 [0.828-0.892]) and validation cohort 2 (0.872 [0.810-0.934]). In the prospective validation study, RAPIDS had an AUC of 0.812 (95% CI 0-717-0.907), sensitivity of 0.888 (0.728-0.999), specificity of 0.740 (0.593-0.886), NPV of 0.929 (0.862-0.995), and PPV of 0.512 (0.313-0.710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0.630 [0.507-0.754] for the pathomics microenvironment model, 0.716 [0.580-0-852] for the radiomics MRI model, and 0.733 [0.620-0.845] for the pathomics nucleus model; all p<0.0001). Interpretation RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.
WOS KeywordARTIFICIAL-INTELLIGENCE ; RADIOMICS ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
Funding OrganizationNational Natural Science Foundation of China ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
WOS Research AreaMedical Informatics ; General & Internal Medicine
WOS SubjectMedical Informatics ; Medicine, General & Internal
WOS IDWOS:000736243900006
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47104
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie; Wan, Xiangbo
Affiliation1.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiat Oncol, Guangzhou 510000, Peoples R China
2.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Pathol, Guangzhou, Peoples R China
3.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Peoples R China
4.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Colorectal Surg, Guangzhou, Peoples R China
5.Guangdong Inst Gastroenterol, Guangdong Prov Key Lab Colorectal & Pelv Floor Di, Guangzhou, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
7.Sun Yat Sen Univ, State Key Lab Oncol South China, Collaborat Innovat Ctr Canc Med, Canc Ctr, Guangzhou, Peoples R China
8.Sun Yat Sen Univ, Dept Pathol, Canc Ctr, Guangzhou, Peoples R China
9.Kunming Med Univ, Yunnan Canc Hosp, Dept Radiol, Affiliated Hosp 3, Kunming, Yunnan, Peoples R China
10.Southeast Univ, Sch Comp Sci & Engn, Nanjing, Peoples R China
11.Southern Med Univ, Nanfang Hosp, Dept Radiat Oncol, Guangzhou, Peoples R China
12.Univ Texas MD Anderson Canc Ctr, Dept Mol & Cellular Oncol, Houston, TX 77030 USA
13.China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan
14.China Med Univ, Res Ctr Canc Biol & Mol Med, Taichung, Taiwan
15.Asia Univ, Dept Biotechnol, Taichung, Taiwan
16.Univ Michigan, Dept Radiat Oncol, Ann Arbor, MI 48109 USA
17.Univ Michigan, Canc Ctr, Ann Arbor, MI 48109 USA
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Feng, Lili,Liu, Zhenyu,Li, Chaofeng,et al. Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study[J]. LANCET DIGITAL HEALTH,2022,4(1):E8-E17.
APA Feng, Lili.,Liu, Zhenyu.,Li, Chaofeng.,Li, Zhenhui.,Lou, Xiaoying.,...&Wan, Xiangbo.(2022).Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study.LANCET DIGITAL HEALTH,4(1),E8-E17.
MLA Feng, Lili,et al."Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study".LANCET DIGITAL HEALTH 4.1(2022):E8-E17.
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