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Development of a Nomogram Combining Clinical Risk Factors and Dual-Energy Spectral CT Parameters for the Preoperative Prediction of Lymph Node Metastasis in Patients With Colorectal Cancer

  • Cao, Yuntai1, 2, 3, 4, 5
  • Zhang, Jing6
  • Bao, Haihua1
  • Zhang, Guojin7
  • Yan, Xiaohong8
  • Wang, Zhan9
  • Ren, Jialiang10
  • Chai, Yanjun3
  • Zhao, Zhiyong2, 3, 4, 5
  • Zhou, Junlin3, 4, 5
  • 1 Department of Radiology, Affiliated Hospital of Qinghai University, Xining , (China)
  • 2 Second Clinical School, Lanzhou University, Lanzhou , (China)
  • 3 Department of Radiology, Lanzhou University Second Hospital, Lanzhou , (China)
  • 4 Key Laboratory of Medical Imaging of Gansu Province, Lanzhou , (China)
  • 5 Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou , (China)
  • 6 Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai , (China)
  • 7 Department of Radiology, Sichuan Provincial People’s Hospital, Chengdu , (China)
  • 8 Department of Critical Medicine, Affiliated Hospital of Qinghai University, Xining , (China)
  • 9 Department of Hepatopancreatobiliary Surgery, Affiliated Hospital of Qinghai University, Xining , (China)
  • 10 Department of Pharmaceuticals Diagnosis, General Electrics (GE) Healthcare, Beijing , (China)
Published Article
Frontiers in Oncology
Frontiers Media SA
Publication Date
Sep 22, 2021
DOI: 10.3389/fonc.2021.689176
PMCID: PMC8493878
PubMed Central
  • Oncology
  • Original Research


Objective This study aimed to develop a dual-energy spectral computed tomography (DESCT) nomogram that incorporated both clinical factors and DESCT parameters for individual preoperative prediction of lymph node metastasis (LNM) in patients with colorectal cancer (CRC). Material and Methods We retrospectively reviewed 167 pathologically confirmed patients with CRC who underwent enhanced DESCT preoperatively, and these patients were categorized into training ( n  = 117) and validation cohorts ( n  = 50). The monochromatic CT value, iodine concentration value (IC), and effective atomic number (Eff-Z) of the primary tumors were measured independently in the arterial phase (AP) and venous phase (VP) by two radiologists. DESCT parameters together with clinical factors were input into the prediction model for predicting LNM in patients with CRC. Logistic regression analyses were performed to screen for significant predictors of LNM, and these predictors were presented as an easy-to-use nomogram. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the clinical usefulness of the nomogram. Results The logistic regression analysis showed that carcinoembryonic antigen, carbohydrate antigen 199, pericolorectal fat invasion, ICAP, ICVP, and Eff-ZVP were independent predictors in the predictive model. Based on these predictors, a quantitative nomogram was developed to predict individual LNM probability. The area under the curve (AUC) values of the nomogram were 0.876 in the training cohort and 0.852 in the validation cohort, respectively. DCA showed that our nomogram has outstanding clinical utility. Conclusions This study presents a clinical nomogram that incorporates clinical factors and DESCT parameters and can potentially be used as a clinical tool for individual preoperative prediction of LNM in patients with CRC.

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