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Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

文献类型：期刊论文

作者Wang, Xiao-Xiao2; Ding, Yi2; Wang, Si-Wen3,4; Dong, Di3,4,5; Li, Hai-Lin3,6; Chen, Jian1; Hu, Hui2; Lu, Chao2; Tian, Jie3,5,6,7; Shan, Xiu-Hong2
刊名CANCER IMAGING
出版日期2020-12-23
卷号20期号:1页码:10
关键词Lauren classification Radiomics Peritumoral analysis Gastric cancer Computed tomography
ISSN号1740-5025
DOI10.1186/s40644-020-00358-3
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Shan, Xiu-Hong(13913433095@163.com)
英文摘要Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663-0.767) in the training cohort and 0.714 (95% CI, 0.636-0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696-0.795) and a validation AUC of 0.758 (95% CI, 0.685-0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.
WOS关键词CT ; CARCINOMA ; NOMOGRAM
资助项目National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[6202790004] ; National Natural Science Foundation of China[81930053] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175] ; Key Research Program of the Chinese Academy of Sciences[KGZD-EW-T03] ; Zhenjiang Innovation Capacity Building Program (technological infrastructure) -R&D project of China[SS2015023] ; Zhenjiang first people's Hospital Fund[Y2019016-S] ; Jiangsu Provincial Key Research and Development Special Fund[BE2015666] ; Jiangsu Innovative team leading talent fund[CXTDC2016006] ; Jiangsu six high peak talent fund[WSW-205] ; Jiangsu 333 talent fund[BRA2020016]
WOS研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000595717300001
出版者BMC
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai) ; Youth Innovation Promotion Association CAS ; Key Research Program of the Chinese Academy of Sciences ; Zhenjiang Innovation Capacity Building Program (technological infrastructure) -R&D project of China ; Zhenjiang first people's Hospital Fund ; Jiangsu Provincial Key Research and Development Special Fund ; Jiangsu Innovative team leading talent fund ; Jiangsu six high peak talent fund ; Jiangsu 333 talent fund
源URL[http://ir.ia.ac.cn/handle/173211/41681]  
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; Shan, Xiu-Hong
作者单位1.Jiangsu Univ, Dept Med Imaging, Med Coll, Zhenjiang, Jiangsu, Peoples R China
2.Jiangsu Univ, Affiliated Peoples Hosp, Dept Radiol, Zhenjiang, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
5.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Xiao-Xiao,Ding, Yi,Wang, Si-Wen,et al. Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer[J]. CANCER IMAGING,2020,20(1):10.
APA Wang, Xiao-Xiao.,Ding, Yi.,Wang, Si-Wen.,Dong, Di.,Li, Hai-Lin.,...&Wang, Siwen.(2020).Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer.CANCER IMAGING,20(1),10.
MLA Wang, Xiao-Xiao,et al."Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer".CANCER IMAGING 20.1(2020):10.

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