Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging

被引：7

作者：

Nakaura, Takeshi ^{[1
,2
]}

Kobayashi, Naoki ^{[1
]}

Yoshida, Naofumi ^{[1
]}

Shiraishi, Kaori ^{[1
]}

Uetani, Hiroyuki ^{[1
]}

Nagayama, Yasunori ^{[1
]}

Kidoh, Masafumi ^{[1
]}

Hirai, Toshinori ^{[1
]}

机构：

[1] Kumamoto Univ, Grad Sch Med Sci, Dept Diagnost Radiol, Kumamoto, Kumamoto, Japan

[2] Kumamoto Univ Hosp, Radiol, 1-1-1 Honjo,Chuo Ku, Kumamoto, Kumamoto 8608556, Japan

来源：

MAGNETIC RESONANCE IN MEDICAL SCIENCES | 2023年 / 22卷 / 02期

关键词：

artificial intelligence; deep learning; machine learning; magnetic resonance imaging; CONVOLUTIONAL NEURAL-NETWORK; RADIOMICS; APPROXIMATE; TUMOR; MODEL;

D O I：

10.2463/mrms.rev.2022-0102

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponen-tially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.

引用

收藏

页码：147 / 156

页数：10

相关论文

共 72 条

[1] Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J].

Aerts, Hugo J. W. L. ;

Velazquez, Emmanuel Rios ;

Leijenaar, Ralph T. H. ;

Parmar, Chintan ;

Grossmann, Patrick ;

Cavalho, Sara ;

Bussink, Johan ;

Monshouwer, Rene ;

Haibe-Kains, Benjamin ;

Rietveld, Derek ;

Hoebers, Frank ;

Rietbergen, Michelle M. ;

Leemans, C. Rene ;

Dekker, Andre ;

Quackenbush, John ;

Gillies, Robert J. ;

Lambin, Philippe .

NATURE COMMUNICATIONS, 2014, 5

[2] Data mining with decision trees and decision rules [J].

Apte, C ;

Weiss, S .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 1997, 13 (2-3) :197-210

[3] An optimal algorithm for approximate nearest neighbor searching in fixed dimensions [J].

Arya, S ;

Mount, DM ;

Netanyahu, NS ;

Silverman, R ;

Wu, AY .

JOURNAL OF THE ACM, 1998, 45 (06) :891-923

[4] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[5] SUPPORT-VECTOR NETWORKS [J].

CORTES, C ;

VAPNIK, V .

MACHINE LEARNING, 1995, 20 (03) :273-297

[6] Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer [J].

Daye, Dania ;

Tabari, Azadeh ;

Kim, Hyunji ;

Chang, Ken ;

Kamran, Sophia C. ;

Hong, Theodore S. ;

Kalpathy-Cramer, Jayashree ;

Gee, Michael S. .

EUROPEAN RADIOLOGY, 2021, 31 (08) :5759-5767

[7] Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI-A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making [J].

Fehrenbach, Uli ;

Xin, Siyi ;

Hartenstein, Alexander ;

Auer, Timo Alexander ;

Drager, Franziska ;

Frobose, Konrad ;

Jann, Henning ;

Mogl, Martina ;

Amthauer, Holger ;

Geisel, Dominik ;

Denecke, Timm ;

Wiedenmann, Bertram ;

Penzkofer, Tobias .

CANCERS, 2021, 13 (11)

[8] Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI [J].

Feng, Shi-Ting ;

Jia, Yingmei ;

Liao, Bing ;

Huang, Bingsheng ;

Zhou, Qian ;

Li, Xin ;

Wei, Kaikai ;

Chen, Lili ;

Li, Bin ;

Wang, Wei ;

Chen, Shuling ;

He, Xiaofang ;

Wang, Haibo ;

Peng, Sui ;

Chen, Ze-Bin ;

Tang, Mimi ;

Chen, Zhihang ;

Hou, Yang ;

Peng, Zhenwei ;

Kuang, Ming .

EUROPEAN RADIOLOGY, 2019, 29 (09) :4648-4659

[9] Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network [J].

Fujioka, Tomoyuki ;

Kubota, Kazunori ;

Mori, Mio ;

Kikuchi, Yuka ;

Katsuta, Leona ;

Kasahara, Mai ;

Oda, Goshi ;

Ishiba, Toshiyuki ;

Nakagawa, Tsuyoshi ;

Tateishi, Ukihide .

JAPANESE JOURNAL OF RADIOLOGY, 2019, 37 (06) :466-472

[10] NEOCOGNITRON - A SELF-ORGANIZING NEURAL NETWORK MODEL FOR A MECHANISM OF PATTERN-RECOGNITION UNAFFECTED BY SHIFT IN POSITION [J].

FUKUSHIMA, K .

BIOLOGICAL CYBERNETICS, 1980, 36 (04) :193-202

← 1 2 3 4 5 6 7 8 →