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

文献类型：期刊论文

作者Han, Yuqi1,2,3; Chai, Fan4; Wei, Jingwei1,3; Yue, Yali5,6; Cheng, Jin4; Gu, Dongsheng1,3; Zhang, Yinli7; Tong, Tong5,6; Sheng, Weiqi6,8; Hong, Nan4
刊名FRONTIERS IN ONCOLOGY
出版日期2020-08-14
卷号10页码:12
ISSN号2234-943X
关键词colorectal cancer liver metastasis magnetic resonance histopathologic growth patterns radiomics
DOI10.3389/fonc.2020.01363
通讯作者Wang, Yi(wangyi@pkuph.edu.cn) ; Tian, Jie(tian@ieee.org)
英文摘要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.
WOS关键词MRI ; BIOMARKERS ; FEATURES ; TRIALS
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]
WOS研究方向Oncology
语种英语
出版者FRONTIERS MEDIA SA
WOS记录号WOS:000566226700001
资助机构Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Nature Science Foundation of Beijing
源URL[http://ir.ia.ac.cn/handle/173211/41520]  
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Wang, Yi; Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing, Peoples R China
2.Xidian Univ, Sch Life Sci & Technol, Xian, 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
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.

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来源：自动化研究所

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