Dual-phase contrast-enhanced CT-based intratumoral and peritumoral radiomics for preoperative prediction of lymph node metastasis in gastric cancer

被引:0
|
作者
Zhou, Yun-hui [1 ,4 ]
Chen, Xiao-li [2 ]
Zhang, Xin [3 ]
Pu, Hong [1 ]
Li, Hang [1 ]
机构
[1] Univ Elect Sci & Technol China, Sichuan Prov Peoples Hosp, Dept Radiol, 32 Second Sect First Ring Rd, Chengdu 610072, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Sichuan Canc Hosp, Affiliated Canc Hosp, Dept Radiol,Med Sch, Chengdu 610000, Peoples R China
[3] GE Healthcare China, 1 Tongji South Rd, Beijing 100176, Peoples R China
[4] Chengdu Pidu Dist Peoples Hosp, Chengdu Med Coll, Dept Radiol, Affiliated Hosp 3, 666 Second Sect Deyuan North Rd, Chengdu 611730, Sichuan, Peoples R China
关键词
Radiomics; Lymph node metastasis; Prognosis; Gastric cancer; 8TH EDITION; TUMOR; DIAGNOSIS; SYSTEM;
D O I
10.1186/s12876-025-03728-y
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Objective To determine whether intratumoral and peritumoral radiomics derived from dual-phase contrast-enhanced CT imaging could predict lymph node metastasis (LNM) in gastric cancer. Methods Patients with gastric cancer from January 2017 to January 2022 were retrospectively collected and were randomly divided into training cohort (n = 287) and test cohort (n = 121) with a ratio of 7: 3. Clinical features and traditional radiological features were analyzed to construct clinical model. Radiomics features based on intratumoral (ITV) and peritumoral volumetric (PTV) regions of the tumor were extracted and screened to construct radiomics models. Clinical-radiomics combined model was constructed by the most predictive radiomics features and clinical independent predictors. The correlation between LNM predicted by the best model and 2-year disease-free survival (DFS) was evaluated by the Kaplan-Meier analysis. Results CT-LNM and CT-T stage were independent predictors of LNM. Compared with other radiomics models, ITV + PTV on atrial and venous phase (ITV + PTV-AP + VP) radiomics model presented moderate AUCs of 0.679 and 0.670 in the training cohort and validation cohort, respectively. Among the models, clinical-radiomics combined model achieved the highest AUC of 0.894 and 0.872 in the training and test cohorts, and 0.744 and 0.784 in the T1-2 and T3-4 subgroups, respectively. Clinical-radiomics combined model based LNM could stratify patients into high-risk and low-risk groups, and 2-year DFS of high-risk group was significantly lower than that of low-risk group (p < 0.001). Conclusion Clinical-radiomics combined model integrating CT-LNM, CT-T stage, and ITV-PTV-AP + VP radiomics features could predict LNM, and this combined model based LNM was associated with 2-year DFS.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer
    Li, Jing
    Dong, Di
    Fang, Mengjie
    Wang, Rui
    Tian, Jie
    Li, Hailiang
    Gao, Jianbo
    EUROPEAN RADIOLOGY, 2020, 30 (04) : 2324 - 2333
  • [22] Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma
    Zeng, Piaoe
    Qu, Chao
    Liu, Jianfang
    Cui, Jingjing
    Liu, Xiaoming
    Xiu, Dianrong
    Yuan, Huishu
    ACTA RADIOLOGICA, 2023, 64 (07) : 2221 - 2228
  • [23] A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI
    Ma, Mingming
    Jiang, Yuan
    Qin, Naishan
    Zhang, Xiaodong
    Zhang, Yaofeng
    Wang, Xiangpeng
    Wang, Xiaoying
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [24] CT-based radiomics nomogram for preoperative prediction of No.10 lymph nodes metastasis in advanced proximal gastric cancer
    Wang, Lili
    Gong, Jing
    Huang, Xinming
    Lin, Guifang
    Zheng, Bin
    Chen, Jingming
    Xie, Jiangao
    Lin, Ruolan
    Duan, Qing
    Lin, Weiwen
    EJSO, 2021, 47 (06): : 1458 - 1465
  • [25] Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
    Li, Junmeng
    Zhang, Chao
    Wei, Jia
    Zheng, Peiming
    Zhang, Hui
    Xie, Yi
    Bai, Junwei
    Zhu, Zhonglin
    Zhou, Kangneng
    Liang, Xiaokun
    Xie, Yaoqin
    Qin, Tao
    FRONTIERS IN ONCOLOGY, 2020, 10
  • [26] CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer
    Lu, Tianyu
    Ma, Jianbing
    Zou, Jiajun
    Jiang, Chenxu
    Li, Yangyang
    Han, Jun
    JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY, 2024, 32 (03) : 597 - 609
  • [27] Clinical-Radiomics Nomogram Based on Contrast-Enhanced Ultrasound for Preoperative Prediction of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma
    Jiang, Liqing
    Zhang, Zijian
    Guo, Shiyan
    Zhao, Yongfeng
    Zhou, Ping
    CANCERS, 2023, 15 (05)
  • [28] Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer
    Meng, Fan-xiu
    Zhang, Jian-xin
    Guo, Ya-rong
    Wang, Ling-jie
    Zhang, He-zhao
    Shao, Wen-hao
    Xu, Jun
    ACADEMIC RADIOLOGY, 2024, 31 (06) : 2356 - 2366
  • [29] Building CT Radiomics Based Nomogram for Preoperative Esophageal Cancer Patients Lymph Node Metastasis Prediction
    Shen, Chen
    Liu, Zhenyu
    Wang, Zhaoqi
    Guo, Jia
    Zhang, Hongkai
    Wang, Yingshu
    Qin, Jianjun
    Li, Hailiang
    Fang, Mengjie
    Tang, Zhenchao
    Li, Yin
    Qu, Jinrong
    Tian, Jie
    TRANSLATIONAL ONCOLOGY, 2018, 11 (03): : 815 - 824
  • [30] A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients
    Sun, Chao
    Gong, Xuantong
    Hou, Lu
    Yang, Di
    Li, Qian
    Li, Lin
    Wang, Yong
    FRONTIERS IN ONCOLOGY, 2024, 14