A newly published paper, developed as part of the IMAGIO project, reviews research on tools designed to predict whether liver cancer (hepatocellular carcinoma) will come back after treatment with thermal ablation. Thermal ablation is a minimally invasive procedure that uses heat to destroy cancer cells.
Liver cancer often recurs even after seemingly successful treatment. Better prediction tools would help doctors to adjust follow-up planning for high-risk patients, consider additional or alternative treatments, and provide more personalized care.
The research team reviewed 16 studies, all from East Asia, on 39 prediction models for cancer recurrence. These models use different types of information – such as blood tests, patient characteristics, and imaging from MRI, CT, or ultrasound – to estimate risk. The modeling methods used both traditional statistical approaches and advanced computational techniques, including artificial intelligence.
Key Insights
- Hybrid models that combine clinical and imaging data performed best.
- Inconsistencies in how recurrence was defined made comparisons difficult.
- Many models were only tested in the hospitals where they were created, limiting their reliability for broader use.
- While some models look promising, none are ready yet for routine use in clinics, due to methodological shortcomings.
This review lays the foundation for the next phase of the IMAGIO Project: creating a prognostic model that combines clinical and imaging data with IMAGIO’s datasets, and then testing the model using data from several hospitals.
Read the full article here: Clinical and Imaging-Based Prognostic Models for Recurrence and Local Tumor Progression Following Thermal Ablation of Hepatocellular Carcinoma: A Systematic Review