External validation of the MSKCC nomogram to estimate five-year overall survival after surgery for stage I–III colon cancer in a Dutch population

Abstract

Introduction: The Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram has been developed to estimate five-year overall survival (OS) after curative-intent surgery of colon cancer based on age, sex, T stage, differentiation grade, number of positive and examined regional lymph nodes. This is the first evaluation of the performance of the MSKCC model in a European population regarding prediction of OS. Material and methods: Population-based data from patients with stage I–III colon cancer diagnosed between 2010 and 2016 were obtained from the Netherlands Cancer Registry (NCR) for external validation of the MSKCC prediction model. Five-year survival probabilities were estimated for all patients in our dataset by using the MSKCC prediction equation. Histogram density plots were created to depict the distribution of the estimated probability and prognostic index. The performance of the model was evaluated in terms of its overall performance, discrimination, and calibration. Results: A total of 39,805 patients were included. Five-year OS was 71.9% (95% CI 71.5; 72.3) (11,051 events) with a median follow up of 5.6 years (IQR 4.1; 7.7). The Brier score was 0.10 (95% CI 0.10; 0.10). The C-index was 0.75 (95% CI 0.75; 0.76). The calibration measures and plot indicated that the model slightly overestimated observed mortality (observed/expected ratio = 0.86 [95% CI 0.86; 0.87], calibration intercept = −0.14 [95% CI −0.16; −0.11], and slope 1.07 [95% CI 1.05; 1.09], ICI = 0.04, E50 = 0.04, and E90 = 0.05). Conclusions: The external validation of the MSKCC prediction nomogram in a large Dutch cohort supports the use of this practical tool in the European patient population. These personalised estimated survival probabilities may support clinicians when informing patients about prognosis. Adding potential relevant prognostic factors to the model, such as primary tumour location, might further improve the model.