The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Decoding global gene expression programs in liver cancer by noninvasive imaging
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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In the citing paper
“Because radiological phenotypes are fundamentally driven by the underlying pathophysiology, quantitative imaging analysis may reveal subtle relations between the two.22”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [22]
In the citing paper
“A watershed article in this regard came from Segal et al, who, in an early example of radiogenomics, used a finite series of radiologist-scored quantitative features to predict gene expression patterns in hepatocellular carcinoma (40).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [40]
In the citing paper
“Seminal radiogenomic studies were the first to show a relationship between quantitative image features and gene expression patterns in patients with cancer (40,54,55).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [40]
Citations
Cites
1 retracted
RETRACTED: A Genomic Strategy to Refine Prognosis in Early-Stage Non–Small-Cell Lung Cancer
Potti, A., Mukherjee, S., Petersen, R. P., Dressman, H. K., Bild, A., Koontz, J. I., Kratzke, R. A., Watson, M. A., Kelley, M. J., Ginsburg, G. S., et al. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa060467 · Cited by 576
RetractedGene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,474
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321
Paik, S., Tang, G., Shak, S., Kim, C., Baker, J. B., Kim, W., Cronin, M. T., Baehner, F. L., Watson, D., Bryant, J., et al. (2006) · Journal of Clinical Oncology · doi:10.1200/jco.2005.04.7985 · Cited by 2,597
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,678
Self-citationGene expression profiling identifies clinically relevant subtypes of prostate cancer
Lapointe, J., Li, C., Higgins, J., van de Rijn, M., Bair, E., Montgomery, K. D., Ferrari, M. K., Egevad, L., Rayford, W., Bergerheim, U. S. R., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0304146101 · Cited by 1,282
Use of Gene-Expression Profiling to Identify Prognostic Subclasses in Adult Acute Myeloid Leukemia
Bullinger, L., Döhner, K., Bair, E., Fröhling, S., Schlenk, R. F., Tibshirani, R. J., Döhner, H., & Pollack, J. R. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa031046 · Cited by 992
Chang, H. Y., Nuyten, D. S. A., Sneddon, J. B., Hastie, T., Tibshirani, R., Sørlie, T., Dai, H., He, Y. D., van't Veer, L. J., Bartelink, H., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409462102 · Cited by 981
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Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Radiomics: the process and the challenges
Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258
“Following a complete manual extraction of numerous (N100) image features, a subset of 14 features was able to predict 80% of the gene expression pattern in hepatocellular carcinoma using computed tomographic (CT) images [12].”
Method
Metadata
- 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
- Venue
- Nature Biotechnology
- Published
- 2007-05-21
- Volume
- 25
- Issue
- 6
- Pages
- 675-680
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- 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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