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

doi:10.1245/s10434-019-07910-x

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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, Jin 9; Wei, Jingwei 6,7,8; Tong, Tong 1; Sheng, Weiqi 5; Zhang, Yinli 4; Han, Yuqi 6,7,8; Gu, Dongsheng 6,7,8; Hong, Nan 9; Ye, Yingjiang 3; Tian, Jie 2,6,7,8,10
刊名	ANNALS OF SURGICAL ONCOLOGY
	2019-10-11
页码	12
ISSN号	1068-9265
DOI	10.1245/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.
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关键词	COMPUTED-TOMOGRAPHY ; TEXTURE ANALYSIS ; ANGIOGENESIS ; BEVACIZUMAB ; SURVIVAL
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
内容类型	期刊论文
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 Radiol,Dept Oncol, Shanghai, Peoples R China 2.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 3.Peking Univ, Peoples Hosp, Dept Gastrointestinal Surg, Beijing, Peoples R China 4.Peking Univ, Peoples Hosp, Dept Pathol, Beijing, Peoples R China 5.Fudan Univ, Shanghai Med Coll, Shanghai Canc Ctr, Dept Pathol,Dept Oncol, Shanghai, Peoples R China 6.Univ Chinese Acad Sci, Beijing, Peoples R China 7.Beijing Key Lab Mol Imaging, Beijing, Peoples R China 8.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China 9.Peking Univ, Peoples Hosp, Dept Radiol, Beijing, Peoples R China 10.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian, Shaanxi, 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.