Radiomics in hepatocellular carcinoma: A state-of-the-art review

被引：21

作者：

Yao, Shan ^{[1
]}

Ye, Zheng ^{[1
]}

Wei, Yi ^{[1
]}

Jiang, Han-Yu ^{[1
]}

Song, Bin ^{[1
]}

机构：

[1] Sichuan Univ, West China Hosp, Dept Radiol, 37 Guoxue Alley, Chengdu 610041, Sichuan, Peoples R China

来源：

WORLD JOURNAL OF GASTROINTESTINAL ONCOLOGY | 2021年 / 13卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Hepatocellular carcinoma; Radiomics; Deep learning; Artificial intelligence; Medical imaging; Predictive modeling; CONVOLUTIONAL NEURAL-NETWORK; CONTRAST-ENHANCED CT; MICROVASCULAR INVASION; SURGICAL RESECTION; PREOPERATIVE PREDICTION; RECURRENCE; SEGMENTATION; MANAGEMENT; DIAGNOSIS; NOMOGRAM;

D O I：

10.4251/wjgo.v13.i11.1599

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.

引用

页码：1599 / 1615

页数：17

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