Computed Tomography-based Radiomics Nomogram for the Preoperative Prediction of Tumor Deposits and Clinical Outcomes in Colon Cancer: a Multicenter Study

被引:4
|
作者
Li, Manman [1 ]
Xu, Guodong [2 ]
Chen, Qiaoling [1 ]
Xue, Ting [1 ]
Peng, Hui [1 ]
Wang, Yuwei
Shi, Hui [1 ]
Duan, Shaofeng [3 ]
Feng, Feng [1 ]
机构
[1] Nantong Univ, Dept Radiol, Affiliated Tumor Hosp, Nantong 226361, Jiangsu, Peoples R China
[2] Nantong Univ, Dept Radiol, Affiliated Hosp, Nantong, Jiangsu, Peoples R China
[3] Nantong Univ, Dept Record room, Affiliated Tumor Hosp, Nantong, Jiangsu, Peoples R China
关键词
Colon cancer; Tumor deposits; Computed tomography; Radiomics; Nomogram; COLORECTAL-CANCER; RECTAL-CANCER; PROGNOSTIC INDICATOR; POOR-PROGNOSIS; RESECTION;
D O I
10.1016/j.acra.2022.11.005
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Rationale and Objectives: To develop and validate a computed tomography (CT)-based radiomics nomogram for the preoperative prediction of tumor deposits (TDs) and clinical outcomes in patients with colon cancer. Materials and Methods: This retrospective study included 383 consecutive patients with colon cancer from two centers. Radiomics features were extracted from portal venous phase CT images. Least absolute shrinkage and selection operator regression was applied for feature selection and radiomics signature construction. The multivariate logistic regression model was used to establish a radiomics nomogram. The performance of the nomogram was assessed by using receiver operating characteristic curves, calibration curves and decision curve analysis. Kaplan-Meier survival analysis was used to assess the difference of the overall survival (OS) in the TDs-positive Results: The radiomics signature was composed of 11 TDs status related features. The AUCs of the radiomics model in the training cohort, internal validation and external validation cohorts were 0.82, 0.78 and 0.78, respectively. The radiomics nomogram that incorporated the radiomics signature and clinical independent predictors (CT-N, CEA and CA199) showed good calibration and discrimination with AUCs of 0.88, 0.80 and 0.81 in the training cohort, internal validation and external validation cohorts, respectively. The radiomics nomogram-predicted high-risk groups had a worse OS than the low-risk groups (p < 0.001). The radiomics nomogram-predicted TDs was an independent preoperative predictor of OS. Conclusion: The radiomics nomogram based on CT radiomics features and clinical independent predictors could effectively predict the preoperative TDs status and OS of colon cancer. Important Findings: CT-based radiomics nomogram may be applied in the individual preoperative prediction of TDs status in colon cancer. Additionally, there was a significant difference in OS between the high-risk and low-risk groups defined by the radiomics nomogram, in which patients with high-risk TDs had a significantly worse OS, compared with those with low-risk TDs.
引用
收藏
页码:1572 / 1583
页数:12
相关论文
共 50 条
  • [21] Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors
    Ai, Yao
    Zhang, Jindi
    Jin, Juebin
    Zhang, Ji
    Zhu, Haiyan
    Jin, Xiance
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [22] Computed tomography-based radiomics nomogram model for predicting adherent perinephric fat
    Ma, Teng
    Cong, Lin
    Xu, Jingxu
    Huang, Chencui
    Ma, Qianli
    Hua, Qianqian
    Li, Xiaojiao
    Huang, Zhaoqin
    Wang, Ximing
    Chen, Yunchao
    JOURNAL OF CANCER RESEARCH AND THERAPEUTICS, 2022, 18 (02) : 336 - 344
  • [23] CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study
    Li, M.
    Xu, G.
    Cui, Y.
    Wang, M.
    Wang, H.
    Xu, X.
    Duan, S.
    Shi, J.
    Feng, F.
    CLINICAL RADIOLOGY, 2023, 78 (10) : E741 - E751
  • [24] Radiomics in addition to computed tomography-based body composition nomogram may improve the prediction of postoperative complications in gastric cancer patients
    Lan, Qiaoqing
    Guan, Xuechun
    Lu, Shunzu
    Yuan, Wenzhao
    Jiang, Zijian
    Lin, Huashan
    Long, Liling
    ANNALS OF NUTRITION AND METABOLISM, 2022, 78 (06) : 316 - 327
  • [25] Computed Tomography-Based Radiomics Model to Preoperatively Predict Microsatellite Instability Status in Colorectal Cancer: A Multicenter Study
    Li, Zhi
    Zhong, Qi
    Zhang, Liang
    Wang, Minhong
    Xiao, Wenbo
    Cui, Feng
    Yu, Fang
    Huang, Chencui
    Feng, Zhan
    FRONTIERS IN ONCOLOGY, 2021, 11
  • [26] Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer
    Osman, Sarah O. S.
    Leijenaar, Ralph T. H.
    Cole, Aidan J.
    Lyons, Ciara A.
    Hounsell, Alan R.
    Prise, Kevin M.
    O'Sullivan, Joe M.
    Lambin, Philippe
    McGarry, Conor K.
    Jain, Suneil
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2019, 105 (02): : 448 - 456
  • [27] Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study
    Tang, Bin
    Wu, Fan
    Peng, Lin
    Leng, Xuefeng
    Han, Yongtao
    Wang, Qifeng
    Wu, Junxiang
    Orlandini, Lucia Clara
    CANCER IMAGING, 2024, 24 (01)
  • [28] Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study
    Wu, Peiyan
    Jiang, Yan
    Xing, Hanshuo
    Song, Wenbo
    Cui, Xinwu
    Wu, Xing Long
    Xu, Guoping
    PHYSICS IN MEDICINE AND BIOLOGY, 2023, 68 (17):
  • [29] Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study
    Sun, Zepang
    Jiang, Yuming
    Chen, Chuanli
    Zheng, Huan
    Huang, Weicai
    Xu, Benjamin
    Tang, Weijing
    Yuan, Qingyu
    Zhou, Kangneng
    Liang, Xiaokun
    Chen, Hao
    Han, Zhen
    Feng, Hao
    Yu, Shitong
    Hu, Yanfeng
    Yu, Jiang
    Zhou, Zhiwei
    Wang, Wei
    Xu, Yikai
    Li, Guoxin
    RADIOTHERAPY AND ONCOLOGY, 2021, 165 : 179 - 190
  • [30] Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism
    Yang, Fei
    Chen, Rong
    Yang, Yue
    Yang, Zhixiang
    Su, Yaying
    Ji, Mengmeng
    Pang, Zhiying
    Wang, Dawei
    JOURNAL OF THROMBOSIS AND THROMBOLYSIS, 2024, 57 (03) : 428 - 436