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Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer
Wang, Xiaoxiao1; Ding, Yi1; Wang, Siwen2,3; Dong, Di2,3,4; Li, Hailin2,5; Chen, Jian6; Hu, Hui1; Lu, Chao1; Tian, Jie2,4,5,7; Shan, Xiuhong1
发表期刊CANCER IMAGING
ISSN1740-5025
2020-11-23
卷号20期号:1页码:1-10
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.

关键词Lauren classification Radiomics Peritumoral analysis Gastric cancer Computed tomography
DOI10.1186/s40644-020-00358-3
关键词[WOS]CT ; CARCINOMA ; NOMOGRAM
收录类别SCI
语种英语
WOS研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000595717300001
出版者BMC
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:23[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/173211/41682
专题中国科学院分子影像重点实验室
通讯作者Tian, Jie; Shan, Xiuhong
作者单位1.Jiangsu Univ, Affiliated Peoples Hosp, Dept Radiol, Zhenjiang, Jiangsu, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
6.Jiangsu Univ, Dept Med Imaging, Med Coll, Zhenjiang, Jiangsu, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Peoples R China
通讯作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Wang, Xiaoxiao,Ding, Yi,Wang, Siwen,et al. Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer[J]. CANCER IMAGING,2020,20(1):1-10.
APA Wang, Xiaoxiao.,Ding, Yi.,Wang, Siwen.,Dong, Di.,Li, Hailin.,...&Shan, Xiuhong.(2020).Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer.CANCER IMAGING,20(1),1-10.
MLA Wang, Xiaoxiao,et al."Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer".CANCER IMAGING 20.1(2020):1-10.
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