Background: Piessevaux et al. (ESMO 2010) proposed a decrease in RECIST-based tumor size of 20% at week 8 following 1st-line therapy for mCRC as a predictor of clinical outcome (PFS and OS). We expanded upon this idea (ASCO GI 2012). A tumor volume algorithm was developed providing a better approximation to true tumor volume by using both the longest and the longest orthogonal diameters of a target lesion. In this study we compare the quality of early prediction-based individual patient prognosis based on tumor change according to either RECIST or the tumor volume algorithm. Methods: The prognostic method is combined with the volume algorithm and applied to the data from 2 studies (OPUS, n=337; CRYSTAL, n=1198) and 4 treatment regimens (FOLFOX4 +/- cetuximab and FOLFIRI +/- cetuximab), in patients with mCRC. The influence of the treatment regimen on early PFS and OS prognosis is studied by joint modeling. The quality of early prognosis depending on the tumor size assessment used is compared by the logarithmic scoring rule. Results: Individual volume shrinkage and baseline volume are considered prognostic factors. Individual predictions depend on the chemotherapy administered and whether cetuximab is added. Equivalent tumor baseline volumes and early volume changes, predict an up to 20% higher 1 year (y) PFS and 2y OS rate for FOLFIRI than for FOLFOX. The addition of cetuximab to standard chemotherapy (CT) translates into a mean increase in shrinkage of 10% resulting in an improvement in 1y PFS rates of 23% and in 2y OS rates of 18%. The quality of individual prediction over the 4 regimens is combined in one logarithmic (log) score. The log score for the RECIST-based prediction is 2.45 which is significantly higher than that for the volume-based prediction of 1.97 (p<0.0001). Lower scores correspond to a better prediction. Conclusions: The tumor volume algorithm enables a more precise prediction of individual patient PFS and OS than RECIST-based tumor assessments. Based on early tumor changes for CT, +/- cetuximab, it is possible to calculate quantitative information on a patient’s prognosis. The model has high potential to guide individual clinical decision making for mCRC patients.