Purpose Recurrent disease following thermal ablation therapy is a frequently reported problem. Preoperative identification of patients with high risk of recurrent disease might enable individualized treatment based on patients' risk profile. The aim of the present work was to investigate the role of metabolic parameters derived from the pre-ablation 18 F-FDG PET/CT as imaging biomarkers for recurrent disease in patients with colorectal liver metastases (CLM). Methods Included in this retrospective study were all consecutive patients with CLM treated with percutaneous or open thermal ablation therapy who had a pre-treatment baseline 18 F-FDG PET/CT available. Multivariable cox regression for survival analysis was performed using different models for the metabolic parameters (SUL peak , SUL mean , SUL max , partial volume corrected SUL mean (cSUL mean ), and total lesion glycolysis (TLG)) corrected for tumour and procedure characteristics. The study endpoints were defined as local tumour progression free survival (LTP-FS), new intrahepatic recurrence free survival (NHR-FS) and extrahepatic recurrence free survival (EHR-FS). Clinical and imaging followup data was used as the reference standard. Results Fifty-four patients with 90 lesions were selected. Univariable cox regression analysis resulted in eight models. Multivariable analysis revealed that after adjusting for lesion size and the approach of the procedure, none of the metabolic parameters were associated with LTP-FS or EHR-FS. Percutaneous approach was significantly associated with a shorter LTP-FS. It was demonstrated that lower values of SUL peak , SUL max , SUL mean , and cSUL mean are associated with a significant better NHR-FS, independent of the lesion size and number and prior chemotherapy. Conclusion We found no association between the metabolic parameters on pre-ablation 18 F-FDG PET/CT and the LTP-FS. However, low values of the metabolic parameters were significantly associated with improved NHR-FS. The clinical implication of these findings might be the identification of high-risk patients who might benefit most from adjuvant or combined treatment strategies.