The endpoint of this study is to develop and validate algorithms, using artificial intelligence and machine learning, to optimize patient selection, treatment planning, treatment evaluation and outcome prediction in patients undergoing thermal ablation of a malignant liver tumor. The long-term objective is to establish thermal ablation as the treatment of choice for the vast majority of patients with a primary or secondary liver tumor by development of an accessible workflow that can be widely implemented in different centers performing thermal ablation. Over a time span of at least four years, data will be collected prospectively, encompassing patient information, tumor characteristics, and treatment details. Additionally, pre-, intra-, and post-procedural imaging will be systematically gathered.
This study is part of the IMAGIO (Imaging and advanced guidance for workflow optimization in interventional oncology) project. This project aims to leverage Interventional Oncology in the clinical setting to improve cancer survival outcomes, through minimally invasive, efficient and affordable care pathways for three disease states; liver cancer, lung cancer and sarcoma. In IMAGIO, top innovators in MedTech and Pharma and expert academic clinical centers will mature the next-generation interventional oncology imaging across the full spectrum, from pre-clinical developments to impact validation in clinical trials. The objective of this study, A-IMAGIO, is to develop a standardized, accessible, low-complex, end-to-end solution for patient selection, treatment planning, needle guidance and treatment evaluation for thermal liver ablation. One of the objectives is to integrate AI in the clinical workflow as a tool to assist operators in decision making throughout the entire process based on quantitative assessment. AI data analytics will be developed to guide decision making for personalized treatment together with algorithms that allow optimized treatment planning and automated quantitative treatment evaluation. Also, a computational model will be developed with input from radiomics and clinical data to identify patients at risk of recurrence after thermal ablation. The aim of the A-IMAGIO project is to conduct a large European observational cohort study and collect clinical and image data of patients treated with thermal ablation for liver tumors in order to develop and validate these AI algorithms. The database will be built by merging data from retrospective data and previous prospective clinical trials on thermal ablation of liver tumors. Further data will be collected through a prospective, multicenter, observational study. The long-term objective is to establish thermal ablation as the first line therapy for patients with both primary and secondary liver tumors. Therefore, we aim to develop a low-complexity-high-precision, accessible workflow that can be widely implemented in different centers performing thermal ablation.
Study Type
OBSERVATIONAL
Enrollment
1,500
Leiden University Medical Centre
Leiden, Netherlands
RECRUITINGEfficacy as assessed by local recurrence
Time frame: Throughout the entire four-year study period
Efficacy as assessed by time-to-recurrence
Time frame: Throughout the entire four-year study period
Efficacy as assessed by disease-free-survival
Time frame: Throughout the entire four-year study period
Efficacy as assessed by overall survival
Time frame: Throughout the entire four-year study period
Safety as assessed by complications according to CTCAE v6.0.
Treatment related adverse events are defined as grade 3-4 adverse events and serious
Time frame: Throughout the entire four-year study period
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