Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients.

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Title: Machine learning models classifiers enable a strong prediction of radioembolization-induced liver disease, and define a new bilirubin threshold for selection of patients.
Authors: Rivera I; Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France., Bourien H; Department of Oncology, CLCC Eugène Marquis, Rennes, 35033, France., Morel-Corlu E; Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France., Peinoit A; Department of Oncology, CLCC Eugène Marquis, Rennes, 35033, France., Le Sourd S; Department of Oncology, CLCC Eugène Marquis, Rennes, 35033, France., Rolland Y; Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France., Garin E; Department of Nuclear Medicine, CLCC Eugène Marquis, Rennes, 35033, France., Acosta O; Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, Rennes, F-35000, France., Edeline J; Department of Oncology, CLCC Eugène Marquis, Rennes, 35033, France. j.edeline@rennes.unicancer.fr.
Source: European journal of nuclear medicine and molecular imaging [Eur J Nucl Med Mol Imaging] 2026 May; Vol. 53 (6), pp. 3915-3924. Date of Electronic Publication: 2026 Feb 17.
Publication Type: Journal Article
Journal Info: Publisher: Springer-Verlag Berlin Country of Publication: Germany NLM ID: 101140988 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1619-7089 (Electronic) Linking ISSN: 16197070 NLM ISO Abbreviation: Eur J Nucl Med Mol Imaging Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1619-7089
DOI:10.1007/s00259-026-07803-8