Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis

doi:10.3389/fonc.2020.01363

	Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis
	Han, Yuqi 1,2,3; Chai, Fan 4; Wei, Jingwei2,3 ; Yue, Yali 5,6; Cheng, Jin 4; Gu, Dongsheng2,3 ; Zhang, Yinli 7; Tong, Tong 5,6; Sheng, Weiqi 6,8; Hong, Nan 4; Ye, Yingjiang 9; Wang, Yi 4; Tian, Jie 2,3,10,11
发表期刊	FRONTIERS IN ONCOLOGY
ISSN	2234-943X
	2020-08-14
卷号	10 页码:12
通讯作者	Wang, Yi(wangyi@pkuph.edu.cn) ; Tian, Jie(tian@ieee.org)
摘要	Purpose:Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs). Materials and Methods:In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness. Results:The fused radiomics(tumor)and radiomics(TLI)models showed better performance than any single sequence and clinical model. In addition, the radiomics(TLI)model exhibited better performance than radiomics(tumor)model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively. Conclusion:MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.
关键词	colorectal cancer liver metastasis magnetic resonance histopathologic growth patterns radiomics
DOI	10.3389/fonc.2020.01363
关键词[WOS]	MRI ; BIOMARKERS ; FEATURES ; TRIALS
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[2016YFC0103803] ; Ministry of Science and Technology of China[2016YFA0201401] ; Ministry of Science and Technology of China[2016YFC0103702] ; Ministry of Science and Technology of China[2016YFC0103001] ; Ministry of Science and Technology of China[2017YFC1308700] ; Ministry of Science and Technology of China[2017YFC1309100] ; Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81771924] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSWJSC005] ; Nature Science Foundation of Beijing[7172226] ; Nature Science Foundation of Beijing[7202217]
项目资助者	Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Nature Science Foundation of Beijing
WOS研究方向	Oncology
WOS类目	Oncology
WOS记录号	WOS:000566226700001
出版者	FRONTIERS MEDIA SA
七大方向——子方向分类	医学影像处理与分析
引用统计	被引频次：29[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/41521
专题	中国科学院分子影像重点实验室
通讯作者	Wang, Yi; Tian, Jie
作者单位	1.Xidian Univ, Sch Life Sci & Technol, Xian, 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.Peking Univ Peoples Hosp, Dept Radiol, Beijing, Peoples R China 5.Fudan Univ, Dept Radiol, Shanghai Canc Ctr, Shanghai, Peoples R China 6.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China 7.Peking Univ Peoples Hosp, Dept Pathol, Beijing, Peoples R China 8.Fudan Univ, Dept Pathol, Shanghai Canc Ctr, Shanghai, Peoples R China 9.Peking Univ People Hosp, Dept Gastrointestinal Surg, Beijing, Peoples R China 10.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China 11.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian, Peoples R China
第一作者单位	中国科学院分子影像重点实验室
通讯作者单位	中国科学院分子影像重点实验室
推荐引用方式 GB/T 7714	Han, Yuqi,Chai, Fan,Wei, Jingwei,et al. Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis[J]. FRONTIERS IN ONCOLOGY,2020,10:12.
APA	Han, Yuqi.,Chai, Fan.,Wei, Jingwei.,Yue, Yali.,Cheng, Jin.,...&Tian, Jie.(2020).Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis.FRONTIERS IN ONCOLOGY,10,12.
MLA	Han, Yuqi,et al."Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis".FRONTIERS IN ONCOLOGY 10(2020):12.