CASIA OpenIR  > 中国科学院分子影像重点实验室
A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer
Li, Xue1; Wu, Meng2; Wu, Min3; Liu, Jie4; Song, Li5; Wang, Jiasi5; Zhou, Jun1,4; Li, Shilin1; Yang, Hang1; Zhang, Jun; Cui, Xinwu6; Liu, Zhenyu7,8; Zeng, Fanxin1
Source PublicationCARCINOGENESIS
ISSN0143-3334
2024-01-09
Pages11
Corresponding AuthorCui, Xinwu(cuixinwu@hustedu.cn) ; Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Zeng, Fanxin(zengfx@pku.edu.cn)
AbstractApproximately 50% of colorectal cancer (CRC) patients would develop metastasis with poor prognosis, therefore, it is necessary to effectively predict metastasis in clinical treatment. In this study, we aimed to establish a machine-learning model for predicting metastasis in CRC patients by considering radiomics and transcriptomics simultaneously. Here, 1023 patients with CRC from three centers were collected and divided into five queues (Dazhou Central Hospital n = 517, Nanchong Central Hospital n = 120 and the Cancer Genome Atlas (TCGA) n = 386). A total of 854 radiomics features were extracted from tumor lesions on CT images, and 217 differentially expressed genes were obtained from non-metastasis and metastasis tumor tissues using RNA sequencing. Based on radiotranscriptomic (RT) analysis, a novel RT model was developed and verified through genetic algorithms (GA). Interleukin (IL)-26, a biomarker in RT model, was verified for its biological function in CRC metastasis. Furthermore, 15 radiomics variables were screened through stepwise regression, which was highly correlated with the IL26 expression level. Finally, a radiomics model (RA) was established by combining GA and stepwise regression analysis with radiomics features. The RA model exhibited favorable discriminatory ability and accuracy for metastasis prediction in two independent verification cohorts. We designed multicenter, multi-scale cohorts to construct and verify novel combined radiomics and genomics models for predicting metastasis in CRC. Overall, RT model and RA model might help clinicians in directing personalized diagnosis and therapeutic regimen selection for patients with CRC. We presented a novel radiotranscriptomics (RT) model from genetic algorithms (GA)-based analysis and validated it in multicenter. Moreover, IL26 , as a ranked highest feature in RT model, is differentially expressed in various cohorts, linking to radiomics features. Graphical Abstract
DOI10.1093/carcin/bgad098
WOS KeywordGENETIC ALGORITHM ; FEATURES ; CHEMORADIOTHERAPY ; BIOMARKER ; IMAGES
Indexed BySCI
Language英语
Funding ProjectScientific Research Fund of Sichuan Health and Health Committee[21PJ085] ; Innovative Scientific Research Project of Medical Youth in Sichuan Province[Q20073] ; Dazhou-Sichuan University Intelligent Medical Laboratory in Dazhou[2021CDDZ-26] ; Dazhou-Sichuan University Intelligent Medical Laboratory in Dazhou[21ZDYF0029] ; Key Projects fund of Science & Technology Department of Sichuan Province[2022YFS0588] ; Key Projects fund of Science & Technology Department of Sichuan Province[2023YFS0469] ; Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund, Project[znpy2019090] ; Hubei Provincial Natural Science Foundation[2020CFB729] ; Health Commission of Hubei Province Youth Talent Project[WJ2021Q044]
Funding OrganizationScientific Research Fund of Sichuan Health and Health Committee ; Innovative Scientific Research Project of Medical Youth in Sichuan Province ; Dazhou-Sichuan University Intelligent Medical Laboratory in Dazhou ; Key Projects fund of Science & Technology Department of Sichuan Province ; Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund, Project ; Hubei Provincial Natural Science Foundation ; Health Commission of Hubei Province Youth Talent Project
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:001148492900001
PublisherOXFORD UNIV PRESS
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55456
Collection中国科学院分子影像重点实验室
Corresponding AuthorCui, Xinwu; Liu, Zhenyu; Zeng, Fanxin
Affiliation1.Dazhou Cent Hosp, Dept Clin Res Ctr, Dazhou 635000, Sichuan, Peoples R China
2.Wuhan Univ, Zhongnan Hosp, Dept Ultrasound, Wuhan 430071, Hubei, Peoples R China
3.Sichuan Univ, Huaxi MR Res Ctr HMRRC, Dept Radiol, West China Hosp, Chengdu 610041, Peoples R China
4.Dazhou Cent Hosp, Dept Gen Surg, Dazhou 635000, Sichuan, Peoples R China
5.Dazhou Cent Hosp, Dept Clin Lab, Dazhou 635000, Sichuan, Peoples R China
6.Huazhong Univ Sci & Technol, Tongji Hosp, Tongji Med Coll, Dept Med Ultrasound, 1095 Jiefang Rd, Wuhan 430030, Peoples R China
7.Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
8.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Li, Xue,Wu, Meng,Wu, Min,et al. A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer[J]. CARCINOGENESIS,2024:11.
APA Li, Xue.,Wu, Meng.,Wu, Min.,Liu, Jie.,Song, Li.,...&Zeng, Fanxin.(2024).A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer.CARCINOGENESIS,11.
MLA Li, Xue,et al."A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer".CARCINOGENESIS (2024):11.
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