Reevaluating Feature Selection in Machine Learning-Based Radiomics for Hepatocellular Carcinoma: Bridging the Gap Between Predictive Accuracy and Biological Relevance

IF 33 1区医学 Q1 GASTROENTEROLOGY & HEPATOLOGY

Journal of Hepatology Pub Date : 2025-05-29 DOI:10.1016/j.jhep.2025.05.025

Yoshiyasu Takefuji

引用次数: 0

Abstract

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This research has no fund.

Authors' contributions

Yoshiyasu Takefuji completed this research and wrote this article.

Declaration of Competing Interest

The author has no conflict of interest.Please refer to the accompanying ICMJE disclosure forms for further details.

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重新评估基于机器学习的肝细胞癌放射组学特征选择：弥合预测准确性和生物学相关性之间的差距

本研究没有资金支持。yoshiyasu Takefuji完成了这项研究并撰写了这篇文章。竞争利益声明作者不存在利益冲突。详情请参阅随附的ICMJE披露表格。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Hepatology 医学-胃肠肝病学

CiteScore

46.10

自引率

4.30%

发文量

2325

审稿时长

30 days

期刊介绍： The Journal of Hepatology is the official publication of the European Association for the Study of the Liver (EASL). It is dedicated to presenting clinical and basic research in the field of hepatology through original papers, reviews, case reports, and letters to the Editor. The Journal is published in English and may consider supplements that pass an editorial review.