The objective is to construct a prognostic index that incorporates radiomic information with the validated prognostic index (Sarculator) provided by the Fondazione IRCCS Istituto Nazionale dei Tumori di Milano. A Bayesian approach was employed, utilising a Weibull model. Vague prior distributions were elicited for the shape parameter, the intercept, and the Sarculator. A multivariate Gaussian prior was elicited for the 2,144 radiomic parameters, incorporating a penalty factor, λ. A total of 100 penalty values were considered. A new, ad hoc adaptive version of the pre-conditioned Metropolis adjusted Langevin algorithm (A-MALA) was proposed for sampling. Bayesian Model Averaging (BMA) was employed to yield a composite of the 100 models. A Bayesian hypothesis test was constructed to evaluate the superiority of the BMA prognostic index relative to the Sarculator. The five-year AUC posterior mean was 0.809, with a 95% credible interval (CI) of (0.768, 0.851). The posterior mean of the C-index was 0.804 (95% CI, 0.764, 0.845) for the BMA, 0.743 (95% CI, 0.713, 0.771) for the best model log λ = 10.39 and 0.735 (95% CI, 0.674, 0.761) for the Sarculator. The results suggest that radiomic variables should be included in the model.