Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Akifumi Hagiwara, Shohei Fujita, Yoshiharu Ohno, Shigeki Aoki

研究成果: Review article査読

15 被引用数 (Scopus)

抄録

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

本文言語English
ページ(範囲)601-616
ページ数16
ジャーナルInvestigative Radiology
55
9
DOI
出版ステータスPublished - 01-09-2020

All Science Journal Classification (ASJC) codes

  • 放射線学、核医学およびイメージング

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