Computational medical imaging (radiomics) and potential for immuno-oncology

被引:12
作者
Sun, R. [1 ,2 ]
Limkin, E. J. [1 ,2 ]
Dercle, L. [3 ,4 ]
Reuze, S. [1 ,5 ]
Zacharaki, E. I. [6 ]
Chargari, C. [1 ,2 ,7 ,8 ]
Schernberg, A. [1 ,2 ]
Dirand, A. S. [1 ]
Alexis, A. [1 ]
Paragios, N. [4 ,8 ]
Deutsch, E. [1 ,2 ,9 ]
Ferte, C. [1 ,10 ]
Robert, C. [1 ,2 ,5 ,9 ]
机构
[1] Gustave Roussy, Radiom Team, INSERM, U1030, 114 Rue Edouard Vaillant, F-94805 Villejuif, France
[2] Univ Paris Sud Paris Saclay, Dept Radiotherapie, 114 Rue Edouard Vaillant, F-94805 Villejuif, France
[3] Univ Paris Sud Paris Saclay, Gustave Roussy, Dept Med Nucl, 114 Rue Edouard Vaillant, F-94805 Villejuif, France
[4] INSERM, U1015, 114 Rue Edouard Vaillant, F-94805 Villejuif, France
[5] Univ Paris Sud Paris Saclay, Dept Phys Med, 114 Rue Eclouard Vaillant, F-94805 Villejuif, France
[6] TheraPanacea, 24 Rue Faubourg St Jacques, F-75014 Paris, France
[7] Inst Rech Biomed Armees, D19, F-91220 Bretigny Sur Orge, France
[8] Ecole Val de Grace, Serv Sante Armees, 1 Pl Alphonse Laveran, F-75005 Paris, France
[9] Univ Paris Sud Paris Saclay, Fac Med, 63 Rue Gabriel Peri, F-94270 Le Kremlin Bicetre, France
[10] Univ Paris Sud Paris Saclay, Dept Cancerol ORL, 114 Rue Edouard Vaillant, F-94805 Villejuif, France
来源
CANCER RADIOTHERAPIE | 2017年 / 21卷 / 6-7期
关键词
Radiomics; Immunology; Oncology; Computational medical imaging; TUMOR-INFILTRATING LYMPHOCYTES; CELL LUNG-CANCER; NIVOLUMAB PLUS IPILIMUMAB; TEXTURAL FEATURES; PET IMAGES; CLASSIFICATION; SELECTION; THERAPY; REPRODUCIBILITY; DIMENSIONALITY;
D O I
10.1016/j.canrad.2017.07.035
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
The arrival of immunotherapy has profoundly changed the management of multiple cancers, obtaining unexpected tumour responses. However, until now, the majority of patients do not respond to these new treatments. The identification of biomarkers to determine precociously responding patients is a major challenge. Computational medical imaging (also known as radiomics) is a promising and rapidly growing discipline. This new approach consists in the analysis of high-dimensional data extracted from medical imaging, to further describe tumour phenotypes. This approach has the advantages of being non-invasive, capable of evaluating the tumour and its microenvironment in their entirety, thus characterising spatial heterogeneity, and being easily repeatable over time. The end goal of radiomics is to determine imaging biomarkers as decision support tools for clinical practice and to facilitate better understanding of cancer biology, allowing the assessment of the changes throughout the evolution of the disease and the therapeutic sequence. This review will develop the process of computational imaging analysis and present its potential in immuno-oncology. (C) 2017 Societe francaise de radiotherapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:648 / 654
页数:7
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