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Updated: Jun 17, 2025
Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones
Published on: November 30, 2016
A nomogram for predicting colorectal cancer liver metastasis using circulating tumor cells from the first drainage
Xiaoyu Yang1, Zhongguo Zhang1, Xue Bi1
1Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, PR China.
Purpose:
To use circulating tumor cells (CTC) from the first drainage vein (FDV) of the primary lesion and other clinically relevant parameters to construct a nomogram for predicting liver metastasis in colorectal cancer (CRC) patients, and to provide a theoretical basis for clinical diagnosis and treatment.
Methods:
Information from 343 CRC patients was collected and a database was established. Multivariate logistic analysis was used to identify independent factors for colorectal cancer liver metastasis(mCRC) and nomograms were constructed. Receiver operating characteristic curves(ROC), calibration plots, and decision curve analysis (DCA) were used to assess discrimination, agreement with actual risk, and the clinical utility of the prediction model, respectively.
Result:
CTC levels in FDV were significantly higher in patients with liver metastasis than in those without liver metastasis. Logistic multivariate analysis showed that vascular invasion, T stage, carcinoembryonic antigen (CEA), CA19-9, and CTC could be used as predictors to construct nomograms. The nomograms showed good discriminatory ability in predicting mCRC, with area under the curve (AUC) values of 0.871 [95 % CI: 0.817-0.924) and 0.891 (95 % CI: 0.817-0.964) for the training and validation sets, respectively.] The calibration curves of both the training and validation sets showed that the model was effective in predicting the probability of mCRC. DCA was used to evaluate this predictive model and showed good net clinical benefit.
Conclusion:
We developed and validated a nomogram model based on the combination of CTC in the FDV with other clinical parameters to better predict the occurrence of mCRC.
