ArticleEuropean journal of nuclear medicine and molecular imaging2026
Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients.
Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
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9 authors.
Funding
Abstract
purposeSelective Internal Radiotherapy (SIRT) is an established treatment option for hepatocellular carcinoma (HCC). However, a major complication is radioembolization-induced liver disease (REILD).
methodsThis 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.
resultsAmong 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.
conclusionsBilirubin 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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