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e-Poster Display Session

87P - Predictive risk factors and online nomograms for colon cancer with synchronous liver metastasis


22 Nov 2020


e-Poster Display Session


Tumour Site

Colon and Rectal Cancer


Yajuan Zhu


Annals of Oncology (2020) 31 (suppl_6): S1273-S1286. 10.1016/annonc/annonc355


Y. Zhu, J. Liu

Author affiliations

  • Department Of Biotherapy And Cancer Center, State Key Laboratory Of Biotherapy, West China School of Medicine/West China Hospital of Sichuan University, 610041 - Chengdu/CN


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Abstract 87P


Liver metastases were observed in more than 25% of colon cancer patients when initially diagnosed. We aim to perform a retrospective analysis to investigate the pathological characteristics and treatment experience of synchronous liver metastasis (CLM) using data from the Population-based Surveillance, Epidemiology, and End Results (SEER) database. Furthermore, we intend to identify potential prognostic factors and build origin predictive models for evaluating 3-year and 5-year cancer-specific survival (CSS) and overall survival (OS) of CLM.


CLM patients were collected from SEER database between 2010 and 2015. Univariate and multivariate cox regression analyses were conducted to identify the potential predictors of patient’s survival outcomes. The selected variables were integrated to create predictive nomograms via R tools. Furthermore, the concordance index Harrell’s C statistic (C-index) was calculated to describe the discrimination of nomograms. Calibration (1000 bootstrap resamples) curves were plotted to compare predictions of the nomogram and observed outcomes. Subsequently, Decision Curve Analysis (DCA) and clinical impact curves were performed to evaluate the clinical effects of nomograms.


Total 11,812 CLM patients were included after eliminating those with missing information. Tumor primary site, tumor size, histological grade, T /N stage, surgery of other regions, bone/lung metastasis, CEA level, tumor deposits, regional positive nodes, and chemotherapy were used to construct the predictive models of CSS and OS of CLM. Final nomograms indicated relatively good discrimination (C-index = 0.74 for OS and C-index = 0.73 for CSS). The calibration curves of CSS and OS suggested a good agreement. In addition, DCAs and clinical impact curves reflected favorable potential clinical effects. Online webserver of our nomograms was established for convenient utilization (https://predictivetools.shinyapps.io/CSSDynNomapp/;https://predictivetools.shinyapps.io/OS-DynNomapp/).


The nomograms were built on the webserver and validated to effectively and timely predict the CSS and OS of colon cancer patients with synchronous liver metastasis.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.


Has not received any funding.


All authors have declared no conflicts of interest.

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