Review
Prognostic models for outcome following liver resection for colorectal cancer metastases: A systematic review

https://doi.org/10.1016/j.ejso.2011.10.013Get rights and content

Abstract

Background

Liver resection provides the best chance for cure in colorectal cancer (CRC) liver metastases. A variety of factors that might influence survival and recurrence have been identified. Predictive models can help in risk stratification, to determine multidisciplinary treatment and follow-up for individual patients.

Aims

To systematically review available prognostic models described for outcome following resection of CRC liver metastases and to assess their differences and applicability.

Methods

The Pubmed, Embase and Cochrane Library databases were searched for articles proposing a prognostic model or risk stratification system for resection of CRC liver metastases. Search terms included ‘colorectal’, ‘liver’, ‘metastasis’, ‘resection’, ‘prognosis’ and ‘prediction’. The articles were systematically reviewed.

Results

Fifteen prognostic systems were identified, published between 1996 and 2009. The median study population was 305 patients and the median follow-up was 32 months. All studies used Cox proportional hazards for multi-variable analysis. No prognostic factor was common in all models, though there was a tendency towards the number of metastases, CRC spread to lymph nodes, maximum size of metastases, preoperative CEA level and extrahepatic spread as representing independent risk factors. Seven models assigned more weight to selected factors considered of higher predictive value.

Conclusion

The existing predictive models are diverse and their prognostic factors are often not weighed according to their impact. For the development of future predictive models, the complex relations within datasets and differences in relevance of individual factors should be taken into account, for example by using artificial neural networks.

Introduction

Yearly, about 1.2 million cases of colorectal cancer (CRC) occur worldwide, with approximately 195 200 newly diagnosed cases in North-Western Europe.1 It is one of the most common malignancies and the third leading cause of cancer-related mortality in both men and women.2 The general 5-year survival rate is estimated to be 65%, declining to 11% if distant metastases are present.3

Within three years after the diagnosis of CRC, 29% of the patients will develop liver metastases.4 This condition will be fatal if left untreated, with a median survival measured in months.5, 6 For patients with resectable liver metastases, a resection with curative intent is the treatment of choice, which renders a reported 5-year survival rate in the range of 38–58%.7, 8, 9, 10 By means of novel treatment strategies, such as portal vein embolisation (PVE), two-stage hepatectomy and preoperative down-sizing chemotherapy, together with more “aggressive” surgery due to increased safety of the procedure per se, the number of patients who are suitable for resection has gradually increased.11, 12, 13, 14, 15

Several previous study groups have examined prognostic factors for tumour recurrence and survival after liver resection for CRC metastases.16, 17, 18, 19, 20 The development of predictive models helps to stratify patients into risk categories for the selection of management strategies, to predict prognosis and possibly also to allow a more accurate evaluation of the influence and effectiveness of new therapeutic interventions. Currently, few such models are available, of which the most well known ones are those by Fong et al.,21 Rees et al.8 and Nordlinger et al.22 However, as is the case with these three, most models do not take into account that certain prognostic parameters are of greater influence than others. Furthermore, many of the available models are partly based on patients collected during the 1980s and even earlier,8, 22, 23, 24 making it questionable if these are applicable today. Hence there is need for the development of new predictive models, based on more recent study populations and especially with means to take the difference in impact of individual prognostic factors into account.

The aim of this systematic review was to evaluate currently available prognostic models for the outcome following resection of CRC liver metastases and to assess their differences and applicability in prognosis prediction and decision making.

Section snippets

Search strategy

A systematic review was performed to assess existing prognostic scoring systems and predictive models considering patients undergoing liver resection for colorectal cancer (CRC) metastases. The Pubmed, Embase and Cochrane Library databases were searched on December 18th 2010, using a syntax including synonyms for ‘colorectal’, ‘liver’, ‘metastasis’, ‘resection’, ‘prognosis’ and ‘prediction’ (Table 1). After removing duplicates, the remaining articles were available for screening.

Article selection

The abstracts

Search and selection

The described search yielded 1276 hits in Pubmed, 1586 in Embase and 29 in the Cochrane Library. After removing duplicates a total of 1597 articles remained for screening.

The first abstract screening left 302 articles for a more thorough screening, out of which 20 were deemed relevant. However, one study group did not describe how their model, named ‘a preliminary prognostic model’, was developed.26 Of the remaining 19 articles, 17 were published in 1990 or later, of which two were excluded

Discussion

This review evaluated the currently available prognostic models for predicting outcome following resection of CRC liver metastases. All the included studies deal with prognostic models that are based on risk factors predicting outcome after resection for colorectal cancer liver metastases. Fifteen prognostic systems were identified and there was a tendency towards that the number of liver metastases, CRC spread to lymph nodes, maximum size of metastases, preoperative CEA level, extrahepatic

Conclusion

Currently there are several different prognostic models to predict survival or recurrence after liver resection for colorectal cancer metastases. However, the identified factors vary considerably between models and only few systems take into account that different factors have different levels of prognostic significance. Artificial neural networks have not yet been applied to predict outcome for this patient group and would be a potentially interesting step forward in the future creation of a

Conflict of interest statement

There are no conflicts of interest to be declared. No financial support was requested or received in the production of this article.

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