无线电技术
计算机科学
特征(语言学)
人工智能
模式识别(心理学)
医学
语言学
哲学
作者
Philippe Lambin,Emmanuel Rios-Velazquez,Ralph T.H. Leijenaar,Sara Carvalho,Ruud G.P.M. van Stiphout,Patrick V. Granton,Catharina M.L. Zegers,Robert J. Gillies,Ronald Boellard,André Dekker,Hugo J.W.L. Aerts
标识
DOI:10.1016/j.ejca.2011.11.036
摘要
Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
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