Cited by 629Radiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and ApplicationsHepatocellular Carcinoma Treatment and Prognosis

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Decoding global gene expression programs in liver cancer by noninvasive imaging

Segal, Eran, Sirlin, Claude B, Ooi, Clara, Adler, Adam S, Gollub, Jeremy, Chen, Xin, Chan, Bryan K, Matcuk, George R, Barry, Christopher T, Chang, Howard Y, Kuo, Michael D·2007·Nature Biotechnology

DOI 10.1038/nbt1306

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Abstract

Paralleling the diversity of genetic and protein activities, pathologic human tissues also exhibit diverse radiographic features. Here we show that dynamic imaging traits in non-invasive computed tomography (CT) systematically correlate with the global gene expression programs of primary human liver cancer. Combinations of twenty-eight imaging traits can reconstruct 78% of the global gene expression profiles, revealing cell proliferation, liver synthetic function, and patient prognosis. Thus, genomic activity of human liver cancers can be decoded by noninvasive imaging, thereby enabling noninvasive, serial and frequent molecular profiling for personalized medicine.

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“Because radiological phenotypes are fundamentally driven by the underlying pathophysiology, quantitative imaging analysis may reveal subtle relations between the two.(22) The feasibility of this idea has been demonstrated in previous studies.”
Background
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Self-citation
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Self-citation
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Authors: Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., Chan, B. K., Matcuk, G. R., Barry, C. T., Chang, H. Y., & Kuo, M. D.
Affiliations: Weizmann Institute of Science (IL)
University of California San Diego (US)
University of Hong Kong (HK)
Stanford University (US)
University of California, San Francisco (US)
University of Southern California (US)
Corresponding author: Chang, Howard Y
Kuo, Michael D
ORCID: Segal, Eran
Sirlin, Claude B
Adler, Adam S
Gollub, Jeremy
Chen, Xin
Chan, Bryan K
Matcuk, George R
Barry, Christopher T
Chang, Howard Y
Kuo, Michael D
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2007-05-21
Volume: 25
Issue: 6
Pages: 675-680
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt1306
OpenAlex: W2055575746
Cited by: 629
References: 22
Citations by year: 2026: 18 · 2025: 15 · 2024: 51 · 2023: 47 · 2022: 44 · 2021: 63 · 2020: 62 · 2019: 49 · 2018: 54 · 2017: 46 · 2016: 31 · 2015: 33 · 2014: 30 · 2013: 14 · 2012: 19
FWCI: 9.25
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
Keywords: liver cancer, hepatocellular carcinoma, gene expression profiling, computed tomography, noninvasive molecular imaging, cell proliferation, liver synthetic function, prognosis, personalized medicine
MeSH terms: Carcinoma, Hepatocellular, Humans, Liver, Liver Neoplasms, Neoplasm Proteins, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Biomarkers, Tumor, Gene Expression Profiling

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Decoding global gene expression programs in liver cancer by noninvasive imaging

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