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Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method

文献类型：期刊论文

作者Cheng, Jin5; Wei, Jingwei6,7,8; Tong, Tong9; Sheng, Weiqi1; Zhang, Yinli10; Han, Yuqi6,7,8; Gu, Dongsheng6,7,8; Hong, Nan5; Ye, Yingjiang4; Tian, Jie2,3,6,7,8
刊名ANNALS OF SURGICAL ONCOLOGY
出版日期2019-10-11
页码12
ISSN号1068-9265
DOI10.1245/s10434-019-07910-x
通讯作者Tian, Jie(tian@ieee.org)
英文摘要Objectives 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.
WOS关键词COMPUTED-TOMOGRAPHY ; TEXTURE ANALYSIS ; ANGIOGENESIS ; BEVACIZUMAB ; SURVIVAL
资助项目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]
WOS研究方向Oncology ; Surgery
语种英语
出版者SPRINGER
WOS记录号WOS:000492470800007
资助机构Natural 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
源URL[http://ir.ia.ac.cn/handle/173211/28908]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie
作者单位1.Fudan Univ, Shanghai Med Coll, Shanghai Canc Ctr, Dept Pathol,Dept Oncol, Shanghai, Peoples R China
2.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
4.Peking Univ, Peoples Hosp, Dept Gastrointestinal Surg, Beijing, Peoples R China
5.Peking Univ, Peoples Hosp, Dept Radiol, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
7.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
8.Univ Chinese Acad Sci, Beijing, Peoples R China
9.Fudan Univ, Shanghai Med Coll, Shanghai Canc Ctr, Dept Radiol,Dept Oncol, Shanghai, Peoples R China
10.Peking Univ, Peoples Hosp, Dept Pathol, Beijing, Peoples R China
推荐引用方式
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.

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来源：自动化研究所

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