Establishment of a Dynamic Nomogram for Predicting the Risk of Lymph Node Metastasis in T1 Stage Colorectal Cancer

被引:4
|
作者
Liu, Zitao [1 ]
Huang, Chao [1 ]
Tian, Huakai [1 ]
Liu, Yu [1 ]
Huang, Yongshan [1 ]
Zhu, Zhengming [1 ]
机构
[1] Nanchang Univ, Dept Gastrointestinal Surg, Affiliated Hosp 2, Nanchang, Jiangxi, Peoples R China
来源
FRONTIERS IN SURGERY | 2022年 / 9卷
关键词
T1 stage colorectal cancer; lymph node metastasis (LNM); random forest; LASSO regression algorithm; dynamic nomogram; COLON-CANCER; ARTIFICIAL-INTELLIGENCE; COMPUTED-TOMOGRAPHY; SUBMUCOSAL INVASION; RECTAL-CANCER; CARCINOMA; COLONOSCOPY; GUIDELINES; MANAGEMENT; SURGERY;
D O I
10.3389/fsurg.2022.845666
中图分类号
R61 [外科手术学];
学科分类号
摘要
BackgroundAccurate prediction of the risk of lymph node metastasis in patients with stage T1 colorectal cancer is crucial for the formulation of treatment plans for additional surgery and lymph node dissection after endoscopic resection. The purpose of this study was to establish a predictive model for evaluating the risk of LNM in patients with stage T1 colorectal cancer. MethodsThe clinicopathological and imaging data of 179 patients with T1 stage colorectal cancer who underwent radical resection of colorectal cancer were collected. LASSO regression and a random forest algorithm were used to screen the important risk factors for LNM, and a multivariate logistic regression equation and dynamic nomogram were constructed. The C index, Calibration curve, and area under the ROC curve were used to evaluate the discriminant and prediction ability of the nomogram. The net reclassification index (NRI), comprehensive discriminant improvement index (IDI), and clinical decision curve (DCA) were compared with traditional ESMO criteria to evaluate the accuracy, net benefit, and clinical practicability of the model. ResultsThe probability of lymph node metastasis in patients with T1 colorectal cancer was 11.17% (20/179). Multivariate analysis showed that the independent risk factors for LNM in T1 colorectal cancer were submucosal invasion depth, histological grade, CEA, lymphovascular invasion, and imaging results. The dynamic nomogram model constructed with independent risk factors has good discrimination and prediction capabilities. The C index was 0.914, the corrected C index was 0.890, the area under the ROC curve was 0.914, and the accuracy, sensitivity, and specificity were 93.3, 80.0, and 91.8%, respectively. The NRI, IDI, and DCA show that this model is superior to the ESMO standard. ConclusionThis study establishes a dynamic nomogram that can effectively predict the risk of lymph node metastasis in patients with stage T1 colorectal cancer, which will provide certain help for the formulation of subsequent treatment plans for patients with stage T1 CRC after endoscopic resection.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Nomogram Development and External Validation for Predicting the Risk of Lymph Node Metastasis in T1 Colorectal Cancer
    Oh, Jung Ryul
    Park, Boram
    Lee, Seongdae
    Han, Kyung Su
    Youk, Eui-Gon
    Lee, Doo-Han
    Kim, Do-Sun
    Lee, Doo-Seok
    Hong, Chang Won
    Kim, Byung Chang
    Kim, Bun
    Kim, Min Jung
    Park, Sung Chan
    Sohn, Dae Kyung
    Chang, Hee Jin
    Oh, Jae Hwan
    CANCER RESEARCH AND TREATMENT, 2019, 51 (04): : 1275 - 1284
  • [2] CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer
    Li, Suyun
    Li, Zhenhui
    Wang, Li
    Wu, Mimi
    Chen, Xiaobo
    He, Chutong
    Xu, Yao
    Dong, Mengyi
    Liang, Yanting
    Chen, Xin
    Liu, Zaiyi
    EUROPEAN RADIOLOGY, 2023, 33 (10) : 6861 - 6871
  • [3] CT morphological features for predicting the risk of lymph node metastasis in T1 colorectal cancer
    Suyun Li
    Zhenhui Li
    Li Wang
    Mimi Wu
    Xiaobo Chen
    Chutong He
    Yao Xu
    Mengyi Dong
    Yanting Liang
    Xin Chen
    Zaiyi Liu
    European Radiology, 2023, 33 : 6861 - 6871
  • [4] Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer
    Kenta Kasahara
    Kenji Katsumata
    Akira Saito
    Tetsuo Ishizaki
    Masanobu Enomoto
    Junichi Mazaki
    Tomoya Tago
    Yuichi Nagakawa
    Jun Matsubayashi
    Toshitaka Nagao
    Hiroshi Hirano
    Masahiko Kuroda
    Akihiko Tsuchida
    International Journal of Clinical Oncology, 2022, 27 : 1570 - 1579
  • [5] Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer
    Kasahara, Kenta
    Katsumata, Kenji
    Saito, Akira
    Ishizaki, Tetsuo
    Enomoto, Masanobu
    Mazaki, Junichi
    Tago, Tomoya
    Nagakawa, Yuichi
    Matsubayashi, Jun
    Nagao, Toshitaka
    Hirano, Hiroshi
    Kuroda, Masahiko
    Tsuchida, Akihiko
    INTERNATIONAL JOURNAL OF CLINICAL ONCOLOGY, 2022, 27 (10) : 1570 - 1579
  • [6] LYMPH NODE METASTASIS IN T1 COLORECTAL CANCER: ARE WE UNDERESTIMATING RISK?
    Heffler, M.
    Durie, N.
    LeVea, C.
    Anne, N.
    Smith, J.
    Dunn, K. Bullard
    DISEASES OF THE COLON & RECTUM, 2011, 54 (05) : E144 - E145
  • [7] Nomogram for Predicting Lymph Node Metastasis for Patients With T1 Esophageal Carcinoma
    Yang, Su
    CHEST, 2015, 148 (04)
  • [8] The risk of lymph node metastasis in T1 colorectal carcinoma
    Yamamoto, S
    Watanabe, M
    Hasegawa, H
    Baba, H
    Yoshinare, K
    Shiraishi, J
    Kitajima, M
    HEPATO-GASTROENTEROLOGY, 2004, 51 (58) : 998 - 1000
  • [9] A new clinical model for predicting lymph node metastasis in T1 colorectal cancer
    Wang, Kai
    He, Hui
    Lin, Yanyun
    Zhang, Yanhong
    Chen, Junguo
    Hu, Jiancong
    He, Xiaosheng
    INTERNATIONAL JOURNAL OF COLORECTAL DISEASE, 2024, 39 (01)
  • [10] Establishment and verification of a nomogram for predicting the risk of lymph node metastasis in early gastric cancer
    Wang, Zhengbing
    Liu, Jiangtao
    Luo, Yi
    Xu, Yinjie
    Liu, Xuan
    Wei, Lifu
    Zhu, Qiaobo
    REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS, 2021, 113 (06) : 411 - 417