Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

被引：18

作者：

Ak, M. ^{[1
,2
]}

Toll, S. A. ^{[3
]}

Hein, K. Z. ^{[4
]}

Colen, R. R. ^{[1
,2
]}

Khatua, S. ^{[5
]}

机构：

[1] Univ Pittsburgh, Dept Radiol, Pittsburgh, PA 15260 USA

[2] Univ Pittsburgh, Med Ctr, Hillman Canc Ctr, Pittsburgh, PA USA

[3] Childrens Hosp Michigan, Dept Hematol Oncol, Detroit, MI 48201 USA

[4] Univ Texas MD Anderson Canc Ctr, Dept Leukemia, Houston, TX 77030 USA

[5] Mayo Clin, Dept Pediat Hematol Oncol, Rochester, MN USA

来源：

AMERICAN JOURNAL OF NEURORADIOLOGY | 2022年 / 43卷 / 06期

关键词：

GRADE GLIOMAS; IMAGING PREDICTOR; TEXTURE FEATURES; GLIOBLASTOMA; CLASSIFICATION; HETEROGENEITY; TUMORS; DIFFERENTIATION; METHYLATION; EPENDYMOMA;

D O I：

10.3174/ajnr.A7297

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

引用

页码：792 / 801

页数：10

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