TY - JOUR
T1 - Variability and Standardization of Quantitative Imaging
T2 - Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence
AU - Hagiwara, Akifumi
AU - Fujita, Shohei
AU - Ohno, Yoshiharu
AU - Aoki, Shigeki
N1 - Funding Information:
Conflicts of interest and sources of funding: We have no conflict of interest to declare. This work was supported by AMED under grant number JP19lk1010025h9902; JSPS KAKENHI grant number 19K17150, 19K17177, 18H02772, and JP16H06280; Health, Labour and Welfare Policy Research Grants for Research on Region Medical; and a Grant-in-Aid for Special Research in Subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan.
Publisher Copyright:
© 2020 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
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U2 - 10.1097/RLI.0000000000000666
DO - 10.1097/RLI.0000000000000666
M3 - Review article
C2 - 32209816
AN - SCOPUS:85089301627
VL - 55
SP - 601
EP - 616
JO - Investigative Radiology
JF - Investigative Radiology
SN - 0020-9996
IS - 9
ER -