Thermal ablation is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. However, early recurrence (ER) after treatment for HCC remains a significant challenge. This study aimed to (1) establish a robust and reproducible pipeline for prognostic model development, and (2) apply this pipeline to develop a preliminary model predicting ER in a Western HCC-cohort. Clinical data and contrast-enhanced pre- and post-ablation CT-scans from the Dutch multicentre PROMETHEUS cohort were used. Predictors included clinical characteristics, radiomics predictors, deep learning (DL) based predictors from two pre-trained models (ResNet18 and VGG16), and the minimal ablation margin (MAM). Cox regression models were constructed using principal component analysis (PCA) and regularised predictor selection (LASSO). Model performance was internally validated using bootstrapping and evaluated with the concordance index (C-index), calibration plots, and decision curve analysis. Using the developed pipeline, several individual and combination models were developed. The highest C-index in the validation sets was achieved by the model incorporating radiomics predictors and the MAM, which was trained on data from 68 patients (C-index = 0.63, 95% CI: 0.44 – 0.79). Despite promising performance in training sets, all models showed signs of overfitting and limited calibration in unseen data. The pipeline adheres to current guidelines and supports further refinement and validation. An externally validated prognostic model could help personalise treatment and follow-up strategies after thermal ablation in western HCC patients.