Radiomics with artificial intelligence for precision medicine in radiation therapy

被引：72

作者：

Arimura, Hidetaka ^{[1
]}

Soufi, Mazen ^{[2
]}

Kamezawa, Hidemi ^{[3
]}

Ninomiya, Kenta ^{[1
]}

Yamada, Masahiro ^{[1
]}

机构：

[1] Kyushu Univ, Grad Sch Med Sci, Dept Hlth Sci, Div Med Quantum Sci,Higashi Ku, 3-1-1 Maidashi, Fukuoka, Fukuoka 8128582, Japan

[2] Nara Inst Sci & Technol, Grad Sch Sci & Technol, Div Informat Sci, 8916-5 Takayama Cho, Nara 6300192, Japan

[3] Teikyo Univ, Fac Fukuoka Med Technol, Dept Radiol Technol, 6-22 Misaki Machi, Omuta, Fukuoka 8368505, Japan

来源：

JOURNAL OF RADIATION RESEARCH | 2019年 / 60卷 / 01期

关键词：

radiomics; artificial intelligence; precision medicine; radiation therapy; medical images; cancer traits; LYMPH-NODE METASTASIS; TEXTURAL FEATURES; SCANS CORRELATION; CANCER-PATIENTS; LUNG TEXTURE; COMPLICATIONS; BIOPSY; RISK;

D O I：

10.1093/jrr/rry077

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.

引用

页码：150 / 157

页数：8

共 52 条

[1] Cochlea CT radiomics predicts chemoradiotherapy induced sensorineural hearing loss in head and neck cancer patients: A machine learning and multi-variable modelling study [J].

Abdollahi, Hamid ;

Mostafaei, Shayan ;

Cheraghi, Susan ;

Shiri, Isaac ;

Mandavi, Seied Rabi ;

Kazemnejad, Anoshirvan .

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2018, 45 :192-197

[2] Towards precision medicine: from quantitative imaging to radiomics [J].

Acharya, U. Rajendra ;

Hagiwara, Yuki ;

Sudarshan, Vidya K. ;

Chan, Wai Yee ;

Ng, Kwan Hoong .

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE B, 2018, 19 (01) :6-24

[3] 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

[4] TEXTURAL FEATURES CORRESPONDING TO TEXTURAL PROPERTIES [J].

AMADASUN, M ;

KING, R .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1989, 19 (05) :1264-1274

[5]

[Anonymous], 1997, Wavelets and Filter Banks

[6]

[Anonymous], 1975, Comput. Graph. Image Process., DOI [DOI 10.1016/S0146-664X(75)80008-6, 10.1016/s0146-664x(75)80008-6]

[7]

Arimura H, 2018, MED IMAGING TECHNOL, V36, P81

[8] Imaging genotyping of functional signaling pathways in lung squamous cell carcinoma using a radiomics approach [J].

Bak, So Hyeon ;

Park, Hyunjin ;

Lee, Ho Yun ;

Kim, Youngwook ;

Kim, Hyung-Lae ;

Jung, Sin-Ho ;

Kim, Hyeseung ;

Kim, Jonghoon ;

Park, Keunchil .

SCIENTIFIC REPORTS, 2018, 8

[9] Radiation oncology in the era of precision medicine [J].

Baumann, Michael ;

Krause, Mechthild ;

Overgaard, Jens ;

Debus, Juergen ;

Bentzen, Soren M. ;

Daartz, Juliane ;

Richter, Christian ;

Zips, Daniel ;

Bortfeld, Thomas .

NATURE REVIEWS CANCER, 2016, 16 (04) :234-249

[10] Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function [J].

Cardenas, Carlos E. ;

McCarroll, Rachel E. ;

Court, Laurence E. ;

Elgohari, Baher A. ;

Elhalawani, Hesham ;

Fuller, Clifton D. ;

Kamal, Mona J. ;

Meheissen, Mohamed A. M. ;

Mohamed, Abdallah S. R. ;

Rao, Arvind ;

Williams, Bowman ;

Wong, Andrew ;

Yang, Jinzhong ;

Aristophanous, Michalis .

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2018, 101 (02) :468-478

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