CASIA OpenIR  > 中国科学院分子影像重点实验室
Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method
Cheng, Jin1; Wei, Jingwei2,3,4; Tong, Tong5; Sheng, Weiqi6; Zhang, Yinli7; Han, Yuqi2,3,4; Gu, Dongsheng2,3,4; Hong, Nan1; Ye, Yingjiang8; Tian, Jie2,3,4,9,10; Wang, Yi1
Source PublicationANNALS OF SURGICAL ONCOLOGY
ISSN1068-9265
2019-10-11
Pages12
Corresponding AuthorTian, Jie(tian@ieee.org)
AbstractObjectives To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. Methods Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients' sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor-liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types. Results A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively). Conclusions A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.
DOI10.1245/s10434-019-07910-x
WOS KeywordCOMPUTED-TOMOGRAPHY ; TEXTURE ANALYSIS ; ANGIOGENESIS ; BEVACIZUMAB ; SURVIVAL
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of Beijing[7172226] ; Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Key International Cooperation Projects of the Chinese Academy of Sciences[173211KYSB20160053] ; Natural Science Foundation of Beijing[7172226] ; Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Key International Cooperation Projects of the Chinese Academy of Sciences[173211KYSB20160053]
Funding OrganizationNatural Science Foundation of Beijing ; Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission ; Key International Cooperation Projects of the Chinese Academy of Sciences
WOS Research AreaOncology ; Surgery
WOS SubjectOncology ; Surgery
WOS IDWOS:000492470800007
PublisherSPRINGER
Citation statistics
Cited Times:23[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28909
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.Peking Univ, Peoples Hosp, Dept Radiol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
3.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Fudan Univ, Shanghai Med Coll, Shanghai Canc Ctr, Dept Radiol,Dept Oncol, Shanghai, Peoples R China
6.Fudan Univ, Shanghai Med Coll, Shanghai Canc Ctr, Dept Pathol,Dept Oncol, Shanghai, Peoples R China
7.Peking Univ, Peoples Hosp, Dept Pathol, Beijing, Peoples R China
8.Peking Univ, Peoples Hosp, Dept Gastrointestinal Surg, Beijing, Peoples R China
9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
10.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Cheng, Jin,Wei, Jingwei,Tong, Tong,et al. Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method[J]. ANNALS OF SURGICAL ONCOLOGY,2019:12.
APA Cheng, Jin.,Wei, Jingwei.,Tong, Tong.,Sheng, Weiqi.,Zhang, Yinli.,...&Wang, Yi.(2019).Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method.ANNALS OF SURGICAL ONCOLOGY,12.
MLA Cheng, Jin,et al."Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method".ANNALS OF SURGICAL ONCOLOGY (2019):12.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Cheng, Jin]'s Articles
[Wei, Jingwei]'s Articles
[Tong, Tong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Cheng, Jin]'s Articles
[Wei, Jingwei]'s Articles
[Tong, Tong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cheng, Jin]'s Articles
[Wei, Jingwei]'s Articles
[Tong, Tong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.