Purpose: Selective Internal Radiotherapy (SIRT) is an established treatment option for hepatocellular carcinoma (#HCC ). However, a major complication is radioembolization-induced liver disease (REILD).
Methods: This retrospective study, analyzed patients treated with SIRT for HCC to identify clinical factors associated with REILD and to predict treatment response. Machine learning (#ML ) methods were applied to two distinct cohorts to determine predictors of toxicity and response.
Results: Among 138 patients analyzed for REILD, ML identified bilirubin as a key predictor. A refined threshold of 26.5 µmol/L (1.55 mg/dL) was associated with toxicity risk. In the response cohort (136 patients), predictive performance was limited, nevertheless tumor dose appeared as the most frequent feature selected by the models.
Conclusions: Bilirubin was confirmed as a critical factor for REILD prediction with the identification of a new threshold that may improve patient risk stratification. While tumor dose appears as the main predictor of treatment outcome, robust response models require additional features.
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