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Clinical scoring systems for predicting outcome after surgery for colorectal liver metastases: towards a better multidisciplinary approach

The treatment of colorectal liver metastases continues to evolve rapidly. Improvements in chemotherapeutic regimens coupled with a multidisciplinary approach have resulted in a greater number of patients undergoing liver resections. This, in turn, has led to ever-increasing surgical challenges (1, 2). The selection of patients and the decision of when to operate is now a tumour board decision rather than a ‘one-man show’, i.e. a surgeon’s job. Chemotherapeutic injury to the liver mandates preservation of liver tissue to minimize post-operative liver failure (3, 4), and the potential for re-resections of recurrent disease requires optimization of anatomical relationships (5, 6). Furthermore, there are now multiple options on ‘how to operate’, since parenchyma-preserving liver resections were shown to have no detrimental effect on resection margins (R0 resection). As the indications for resection of colorectal liver metastases broaden, so does the debate determining which patients benefit the most from it. Although several long-term prognostic factors have already been evaluated by univariate and multivariable statistical analyses (5, 7), there is only limited information on prognostic scoring systems (8–15). Prognostic scoring systems have the advantage of imparting quality, reproducibility, clinical applicability and external validation. Only eight prognostic scoring systems have been proposed so far, the first one dating to 1996 and the most recent one to 2009 (Table 1). These studies, encompassing from 81 to 1568 patients, were based in Europe (n = 4), the US (n = 2) and Japan (n = 2). With the exception of the series by Fong and colleagues and by Lise and colleagues, where the scoring systems addressed disease-free survival, all other studies focused on patient survival (8, 13–15) or a combination of disease-free and overall survival (9, 11). Median survival ranged from 19 to 52 months. All studies used the Cox proportional hazards model, the most convenient multivariable method for these data, supplemented in some cases by likelihood ratio tests or stepwise procedures. Risk groups, ranging from two to six, were partially very small because all series were retrospective analyses of data collected prospectively, and power analysis could not be undertaken. The ‘perfect’ study does not exist; all these scoring studies have some limitations when evaluating the results. Only Nordlinger and colleagues and Rees and colleagues assessed the stability of the Cox proportional hazard model by internal validation with bootstrapping. Nagashima and colleagues and Schindl and colleagues had external but no internal validation. The remaining four studies showed no data validation. Nordlinger et al. (8) considered age (P = 0.06) for the model but excluded adjuvant chemotherapy (P = 0.05). Carcinoembryonic antigen (CEA), a prognostic factor, replaced age in their prognostic scoring system. Iwatsuki et al. (9) excluded patients with either positive resection margins or extrahepatic metastases (including lymph nodes). Fong et al. (10) removed patients with ‘positive resection margins’ and ‘extrahepatic disease’ from their pre-operative scoring system. The study of Schindl et al. (14) was the only one where scoring also included non-resectable cases. All referred patients, rather than only those who underwent resection, were considered. Rees et al. (15) gave no information on the number of patients per risk group. The seven risk factors detected in their multivariable analysis were classified into three categories: (a) factors reflecting tumour load (4 3 liver metastases, tumour diameter Z5 cm, CEA level Z60 ng/ml, extrahepatic disease), (b) factors reflecting tumour biology (node positive primary, poorly differentiated primary) and (c) factors reflecting surgical quality (resection margin). Negative resection margin was the strongest prognostic variable. Unfortunately, bootstrapping methods for internal validation were only sporadically performed in the above-mentioned studies (8, 15). Bootstrapping is the recommended method for internal validation because it gives reasonably valid estimates of the expected optimism in predictive performance, provided that any selection of predictors is taken into account. The bootstrap method is conceptually easy to grasp when considered in a series of steps. (i) The bootstrap method begins with an original sample taken from the population, and then calculates sample statistics. (ii) The bootstrap copies the original sample several times to create a pseudopopulation. (iii) From the pseudopopulation, the bootstrap draws several samples with replacement. (iv) For the sample that was drawn, the bootstrap method calculates and stores the results (the results can be of any statistic that the statistician desires). (v) The bootstrap method repeats this process a desired number of times, usually in thousands. (vi) The stored results are averaged, standard errors are calculated and confidence intervals are computed for the averages and for interpretation. The strength of the bootstrap is its ability to develop a sample that is the same size as that of the original sample that may include an observation several times while omitting other observations. Random sampling with replacement provides different samples from the original sample. As the bootstrap draws the samples with replacement, it calculates statistics for each sample. The bootstrap stores these statistics, creating their distribution for further analysis (16). In this issue, in a prospective multivariable analysis of 201 patients, Konopke et al. (17) identified synchronous manifestation of primary colorectal carcinoma and liver metastases, the presence of four or more metastases and CEA levels Z200 ng/ ml as individual criteria predictive of both recurrence-free and overall survival after potentially curative liver resections for colorectal liver metastases (R0 resections) (Table 1). Survival curves were appropriately estimated by the Kaplan–Meier method. Gehan’s Wilcoxon’s test was applied to determine the univariate influence of prognostic parameters on recurrencefree and overall survival. Multivariable analysis of parameters was performed using Cox proportional hazards analysis with backward elimination. The stability of the Cox proportional