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Radiogenomic Analysis to Identify Imaging Phenotypes Associated with Drug Response Gene Expression Programs in Hepatocellular Carcinoma
Open accessAbstract
Purpose To determine whether conventional contrast-enhanced computed tomography (CT) could be used to identify imaging phenotypes associated with a doxorubicin drug response gene expression program in hepatocellular carcinoma (HCC) by using an integrated imaging-genomic approach.Materials and methods Thirty HCCs were analyzed and scored individually across six predefined imaging phenotypes. Unsupervised and supervised bioinformatics analyses were performed to correlate the imaging scores with the corresponding tumor microarray data (each microarray contained gene expression measurements across approximately 18,000 genes) to identify relationships between the imaging traits and underlying tumor gene expression. Enrichment for a predefined doxorubicin-response gene expression program was then performed against the imaging phenotype-associated genes and enrichment determined.Results An imaging phenotype related to tumor margins on arterial phase images demonstrated significant correlation with the doxorubicin-response transcriptional program (P < .05, q < 0.1). It was also significantly associated with HCC venous invasion and tumor stage (P < .05, q < 0.1). Tumors with higher tumor margin scores were more strongly associated with the doxorubicin resistance transcriptional program and had a greater prevalence of venous invasion and worse stage. Tumors with lower tumor margin scores, however, demonstrated a converse relationship.Conclusions It is possible to identify HCC imaging phenotypes at CT that correlate with a doxorubicin drug response gene expression program. Given the role of doxorubicin in regional therapies for HCC management, it is possible that such an approach could be used to guide HCC therapy on a tumor-by-tumor basis on the basis of underlying tumor gene expression patterns.
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“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 [55]
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“In a seminal study, Kuo et al identified hepatocellular carcinoma imaging phenotypes that correlated with a doxorubicin drug response gene expression program (55).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [55]
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Metadata
- Authors
- Kuo, M. D., Gollub, J., Sirlin, C. B., Ooi, C., & Chen, X.
- Affiliations
- University of California San Diego Medical Center (US)
- Stanford University (US)
- University of Hong Kong (HK)
- University of California, San Francisco (US)
- Corresponding author
- Kuo, Michael D.
- Venue
- Journal of Vascular and Interventional Radiology
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 18
- Issue
- 7
- Pages
- 821-830
- ISSN
- 1051-0443 (ISSN-L)
- OpenAlex
- W2052040374
- Cited by
- 168
- References
- 31
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 8 · 2022: 14 · 2021: 19 · 2020: 12 · 2019: 13 · 2018: 21 · 2017: 15 · 2016: 14 · 2015: 9 · 2014: 14 · 2013: 3 · 2012: 8
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Vascular and Interventional Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- hepatocellular carcinoma, radiogenomics, imaging phenotypes, contrast-enhanced CT, tumor margin, venous invasion, tumor stage, doxorubicin resistance, personalized treatment
- MeSH terms
- Antibiotics, Antineoplastic, Contrast Media, Doxorubicin, Carcinoma, Hepatocellular, Humans, Iopamidol, Liver Neoplasms, Phenotype, Tomography, X-Ray Computed, Gene Expression Regulation, Neoplastic, Least-Squares Analysis, Drug Resistance, Neoplasm, Gene Expression Profiling, Microarray Analysis
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