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. 2021 Oct 12:11:604882.
doi: 10.3389/fonc.2021.604882. eCollection 2021.

Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study

Le Kuai  1   2 , Ying Zhang  1   2 , Ying Luo  1   2 , Wei Li  3 , Xiao-Dong Li  4   5 , Hui-Ping Zhang  6 , Tai-Yi Liu  6 , Shuang-Yi Yin  3 , Bin Li  1   2   7
Affiliations

Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study

Le Kuai et al. Front Oncol. .

Abstract

Objective: A proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure.

Methods: Using the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves.

Results: Histological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system.

Conclusion: This study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.

Keywords: colon cancer; database analysis; liver metastasis; nomogram; prognosis model.

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Conflict of interest statement

Authors H-PZ and T-YL were employed by Research and Development Center, Shanghai Applied Protein Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram for predicting OS of CLM patients. OS, overall survival; CLM, colon cancer liver metastases.
Figure 2
Figure 2
Histogram of the age distribution of all samples, modeling set, and validation set.
Figure 3
Figure 3
Nomogram for predicting 1- and 2-year OS of CLM patients. SD, Standard Deviation; OS, overall survival; CLM, colon cancer liver metastases.
Figure 4
Figure 4
Distribution plot for score (A), 1-year (B), and 2-year (C) survival rate of CLM patients. CLM, colon cancer liver metastases.
Figure 5
Figure 5
Calibration plots of the nomogram for 1-year and 2-year prediction for OS of CLM patients. OS, overall survival; CLM, colon cancer liver metastases.
Figure 6
Figure 6
ROC of the nomogram for 1-year and 2-year internal prediction. ROC, receiver operating curves, AUC, area under the curve; FP, false positive; TP, true positive.
Figure 7
Figure 7
External validation for 1-year and 2-year of the model. Distribution plot for score (A), 1-year and 2-year survival rate (B). ROC of the nomogram for 1-year and 2-year external validation (C). AUC, area under the curve; FP, false positive; TP, true positive.
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