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

2/5 보강

European journal of nuclear medicine and molecular imaging 📖 저널 OA 42.9% 2022~2026 2026 Vol.53(6) p. 3915-3924 Hepatocellular Carcinoma Treatment a

TL;DR Bilirubin was confirmed as a critical factor for REILD prediction with the identification of a new threshold that may improve patient risk stratification.

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문

P · Population 대상 환자/모집단

138 patients analyzed for REILD, ML identified bilirubin as a key predictor.

I · Intervention 중재 / 시술

추출되지 않음

C · Comparison 대조 / 비교

추출되지 않음

O · Outcome 결과 / 결론

[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.

OpenAlex 토픽 · Hepatocellular Carcinoma Treatment and Prognosis Liver Disease Diagnosis and Treatment Cholangiocarcinoma and Gallbladder Cancer Studies

Rivera I, Bourien H, Morel-Corlu E, Peinoit A, Le Sourd S, Rolland Y

PubMed ↗ DOI ↗ BibTeX ↓ RIS ↓

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