Cancer Res Treat.  2015 Apr;47(2):242-250. 10.4143/crt.2014.066.

Novel Methods for Clinical Risk Stratification in Patients with Colorectal Liver Metastases

• Kim KY
• Kim NK
• Cha IH
• Ahn JB
• Choi JS
• Choi GH
• Lim JS
• Lee KY
• Baik SH
• Min BS
• Hur H
• Roh JK
• Shin SJ

• KMID: 2132802
• DOI: http://doi.org/10.4143/crt.2014.066

Abstract

PURPOSE
Colorectal cancer patients with liver-confined metastases are classified as stage IV, but their prognoses can differ from metastases at other sites. In this study, we suggest a novel method for risk stratification using clinically effective factors.
MATERIALS AND METHODS
Data on 566 consecutive patients with colorectal liver metastasis (CLM) between 1989 and 2010 were analyzed. This analysis was based on principal component analysis (PCA).
RESULTS
The survival rate was affected by carcinoembryonic antigen (CEA) level (p < 0.001; risk ratio, 1.90), distribution of liver metastasis (p=0.014; risk ratio, 1.46), and disease-free interval (DFI; p < 0.001; risk ratio, 1.98). When patients were divided into three groups according to PCA score using significantly affected factors, they showed significantly different survival patterns (p < 0.001).
CONCLUSION
The PCA scoring system based on CEA level, distribution of liver metastasis, and DFI may be useful for preoperatively determining prognoses in order to assist in clinical decisionmaking and designing future clinical trials for CLM treatment.

Keyword

Risk calculation; Patient stratification; Colorectal neoplasms; Liver metastasis

MeSH Terms

Carcinoembryonic Antigen
Colorectal Neoplasms
Humans
Liver*
Neoplasm Metastasis*
Odds Ratio
Passive Cutaneous Anaphylaxis
Principal Component Analysis
Prognosis
Survival Rate
Carcinoembryonic Antigen

Figure

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  Fig. 1. Comparison of accuracy, specificity, and sensitivity in training and test data sets. Results are from 500 rounds of simulation. The sizes of training and test data sets were 396 (70%) and 170 (30%), respectively. The symbol×indicates accuracy, specificity, and sensitivity of Fong’s criteria.

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  Fig. 2. Kaplan-Meier survival curves of risk groups defined by principal component analysis scores. Comparison of three risk groups (A); group 1 vs. group 2 (B); and group 2 vs. group 3 (C).

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  Fig. 3. Kaplan-Meier survival curves of risk groups defined by Fong’s score. (A) Comparison of survival patterns between two vs. six groups. (B) Pairwise comparisons.

Reference

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