Cited by 168Radiomics and Machine Learning in Medical ImagingHepatocellular Carcinoma Treatment and PrognosisMedical Imaging Techniques and Applications

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Radiogenomic Analysis to Identify Imaging Phenotypes Associated with Drug Response Gene Expression Programs in Hepatocellular Carcinoma

Open access

Kuo, Michael D., Gollub, Jeremy, Sirlin, Claude B., Ooi, Clara, Chen, Xin·2007·Journal of Vascular and Interventional Radiology

DOI 10.1016/j.jvir.2007.04.031

Open this paper in CoChat — free account Open PDF

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.jvir.2007.04.031

Cites

31 of 31 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Molecular portraits of human breast tumours
Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
In silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609
EGFR Mutations in Lung Cancer: Correlation with Clinical Response to Gefitinib Therapy
Paez, J. G., Jänne, P. A., Lee, J. C., Tracy, S., Greulich, H., Gabriel, S., Herman, P., Kaye, F. J., Lindeman, N., Boggon, T. J., et al. (2004) · Science · doi:10.1126/science.1099314 · Cited by 9,368
Multidrug resistance in cancer: role of ATP–dependent transporters
Gottesman, M. M., Fojo, T., & Bates, S. E. (2002) · Nature Reviews Cancer · doi:10.1038/nrc706 · Cited by 5,536

Show all 31 references

Sorted by citations

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
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
Huang, Y., Liang, C., He, L., Tian, J., Liang, C., Chen, X., Ma, Z., & Liu, Z. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.65.9128 · Cited by 1,753
Self-citation
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., Liang, C., Tian, J., & Liang, C. (2016) · Radiology · doi:10.1148/radiol.2016152234 · Cited by 753
Self-citation
Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
Shafiq‐ul‐Hassan, M., Zhang, G. G., Latifi, K., Ullah, G., Hunt, D., Balagurunathan, Y., Abdalah, M. A., Schabath, M. B., Goldgof, D. B., Mackin, D., et al. (2017) · Medical Physics · doi:10.1002/mp.12123 · Cited by 587
Reproducibility of radiomics for deciphering tumor phenotype with imaging
Zhao, B., Tan, Y., Tsai, W., Qi, J., Xie, C., Lü, L., & Schwartz, L. H. (2016) · Scientific Reports · doi:10.1038/srep23428 · Cited by 509
Beyond imaging: The promise of radiomics
Avanzo, M., Stancanello, J., & Naqa, I. E. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.05.071 · Cited by 499
Predicting outcomes in radiation oncology—multifactorial decision support systems
Lambin, P., van Stiphout, R. G. P. M., Starmans, M. H. W., Velazquez, E. R., Nalbantov, G. I., Aerts, H. J. W. L., Roelofs, E., van Elmpt, W., Boutros, P. C., Granone, P. M., et al. (2012) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2012.196 · Cited by 396
Non–Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data—Methods and Preliminary Results
Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N. C., Xu, Y., Quon, A. S. W., Rubin, D. L., Napel, S. A., & Plevritis, S. K. (2012) · Radiology · doi:10.1148/radiol.12111607 · Cited by 400
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging
Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A. (2016) · Clinical Radiology · doi:10.1016/j.crad.2016.09.013 · Cited by 369
Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging
Rutman, A. M., & Kuo, M. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2009.01.050 · Cited by 316
Self-citation
Radiogenomics: bridging imaging and genomics
Bodalal, Z., Trebeschi, S., Nguyen-Kim, T. D. L., Schats, W., & Beets-Tan, R. (2019) · Abdominal Radiology · doi:10.1007/s00261-019-02028-w · Cited by 310
Radiogenomics: What It Is and Why It Is Important
Mazurowski, M. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.04.019 · Cited by 292
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Park, H., Lim, Y., Ko, E. S., Cho, H.-H., Lee, J. E., Han, B., Ko, E. Y., Choi, J. S., & Park, K. W. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3783 · Cited by 243
Behind the Numbers: Decoding Molecular Phenotypes with Radiogenomics—Guiding Principles and Technical Considerations
Kuo, M. D., & Jamshidi, N. (2014) · Radiology · doi:10.1148/radiol.13132195 · Cited by 238
Self-citation
Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma
Hu, H.-T., Wang, Z., Huang, X.-W., Chen, S., Zheng, X., Ruan, S., Xie, X., Lu, M., Yu, J., Tian, J., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5797-0 · Cited by 190
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
Jiang, Y., Chen, C., Xie, J., Wang, W., Zha, X., Lv, W., Chen, H., Hu, Y., Li, T., Yu, J., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.09.007 · Cited by 184
A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma
Peng, J., Zhang, J., Zhang, Q., Xu, Y., Zhou, J., & Liu, L. (2018) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2018.17467 · Cited by 179
Radiogenomic Analysis of Breast Cancer Using MRI: A Preliminary Study to Define the Landscape
Yamamoto, S., Maki, D. D., Korn, R. L., & Kuo, M. (2012) · American Journal of Roentgenology · doi:10.2214/ajr.11.7824 · Cited by 172
Self-citation
ALKMolecular Phenotype in Non–Small Cell Lung Cancer: CT Radiogenomic Characterization
Yamamoto, S., Korn, R. L., Öklü, R., Migdal, C., Gotway, M. B., Weiss, G. J., Iafrate, A. J., Kim, D., & Kuo, M. (2014) · Radiology · doi:10.1148/radiol.14140789 · Cited by 159
Self-citation
Decision support systems for personalized and participative radiation oncology
Lambin, P., Zindler, J. D., Vanneste, B. G. L., Voorde, L. V. D., Eekers, D. B. P., Compter, I., Panth, K. M., Peerlings, J., Larue, R. T. H. M., Deist, T. M., et al. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.006 · Cited by 162
Joint EANM/SNMMI guideline on radiomics in nuclear medicine
Hatt, M., Krizsan, A. K., Rahmim, A., Bradshaw, T., Costa, P. F., Forgács, A., Seifert, R., Zwanenburg, A., Naqa, I. E., Kinahan, P. E., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-06001-6 · Cited by 164
Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles
Ashraf, A. B., Daye, D., Gavenonis, S. C., Mies, C., Feldman, M. D., Rosen, M. A., & Despina Kontos, B. (2014) · Radiology · doi:10.1148/radiol.14131375 · Cited by 152
Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients
Zheng, B.-H., Liu, L., Zhang, Z.-Z., Shi, J.-Y., Dong, L., Tian, L., Ding, Z., Ji, Y., Rao, S., Zhou, J., et al. (2018) · BMC Cancer · doi:10.1186/s12885-018-5024-z · Cited by 148
Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma
Harding-Theobald, E. R., Louissaint, J., Maraj, B., Cuaresma, E., Townsend, W. A., Mendiratta‐Lala, M., Singal, A. G., Su, G. L., Lok, A. S.-F., & Parikh, N. D. (2021) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.16563 · Cited by 147
Artificial intelligence in diagnostic and interventional radiology: Where are we now?
Boeken, T., Feydy, J., Lecler, A., Soyer, P. A., Feydy, A., Barat, M., & Duron, L. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.11.004 · Cited by 152
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology
Shui, L., Ren, H., Yang, X., Li, J., Chen, Z., Cheng, Y., Zhu, H., & Shui, P. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.570465 · Cited by 150
Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE
Kong, C., Zhao, Z., Chen, W., Lv, X., Shu, G., Ye, M., Song, J., Ying, X., Weng, Q., Weng, W., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07910-0 · Cited by 145
Is there a causal relationship between genetic changes and radiomics-based image features? an in vivo preclinical experiment with doxycycline inducible gadd34 tumor cells
Panth, K. M., Leijenaar, R. T. H., Carvalho, S., Lieuwes, N. G., Yaromina, A., Dubois, L., & Lambin, P. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.06.013 · Cited by 133
Radiogenomic Analysis of Oncological Data: A Technical Survey
Incoronato, M., Aiello, M., Infante, T., Cavaliere, C., Grimaldi, A., Mirabelli, P., Monti, S., & Salvatore, M. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18040805 · Cited by 138
Fluorescent imaging of cancerous tissues for targeted surgery
Bu, L., Shen, B., & Cheng, Z. (2014) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2014.07.008 · Cited by 141
Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study
Zhao, B., Tan, Y., Tsai, W. Y., Schwartz, L. H., & Lü, L. (2014) · Translational Oncology · doi:10.1593/tlo.13865 · Cited by 133
Radiomics in hepatocellular carcinoma: a quantitative review
Wakabayashi, T., Ouhmich, F., Gonzalez-Cabrera, C., Felli, E., Saviano, A., Agnus, V., Savadjiev, P., Baumert, T. F., Pessaux, P., Marescaux, J., et al. (2019) · Hepatology International · doi:10.1007/s12072-019-09973-0 · Cited by 138
Radiogenomics and Imaging Phenotypes in Glioblastoma: Novel Observations and Correlation with Molecular Characteristics
Ellingson, B. M. (2015) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-014-0506-0 · Cited by 132
Illuminating Radiogenomic Characteristics of Glioblastoma Multiforme through Integration of MR Imaging, Messenger RNA Expression, and DNA Copy Number Variation
Jamshidi, N., Diehn, M., Bredel, M., & Kuo, M. (2013) · Radiology · doi:10.1148/radiol.13130078 · Cited by 131
Self-citation
A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication
Wei, J., Yang, G., Hao, X., Gu, D., Tan, Y. Q., Wang, X., Dong, D., Zhang, S., Wang, L., Zhang, H., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5575-z · Cited by 131
Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms
Permuth, J. B., Collaborative, T. F. P., Choi, J., Balarunathan, Y., Kim, J., Chen, D.-T., Chen, L., Orcutt, S., Doepker, M. P., Gage, K., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.11768 · Cited by 127
Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS
Luo, W., Huang, Q.-X., Huang, X.-W., Hu, H.-T., Zeng, F.-Q., & Wang, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48488-4 · Cited by 127
Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis
Yamamoto, S., Han, W., Kim, Y., Du, L., Jamshidi, N., Huang, D., Kim, J. H., & Kuo, M. (2015) · Radiology · doi:10.1148/radiol.15142698 · Cited by 120
Self-citation
Hepatocellular carcinoma: radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction
Zhang, Z., Jiang, H., Chen, J., Wei, Y., Cao, L., Ye, Z., Li, X., Ma, L., & Song, B. (2019) · Cancer Imaging · doi:10.1186/s40644-019-0209-5 · Cited by 119
Image-Guided Biopsy in the Era of Personalized Cancer Care: Proceedings from the Society of Interventional Radiology Research Consensus Panel
Tam, A. L., Lim, H. J., Wistuba, I. I., Tamrazi, A., Kuo, M. D., Ziv, E., Wong, S. T. C., Shih, A. J., Webster, R. J., III, Fischer, G. S., et al. (2015) · Journal of Vascular and Interventional Radiology · doi:10.1016/j.jvir.2015.10.019 · Cited by 120
Self-citation
Imaging genomics in cancer research: limitations and promises
Bai, H. X., Lee, A. M., Yang, L., Zhang, P. J., Davatzikos, C. A., Maris, J. M., & Diskin, S. J. (2016) · British Journal of Radiology · doi:10.1259/bjr.20151030 · Cited by 114
Radiogenomics: a key component of precision cancer medicine
Liu, Z.-Q., Duan, T., Zhang, Y.-Y., Weng, S.-Y., Xu, H.-X., Ren, Y.-Q., Zhang, Z.-Y., & Han, X.-W. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02317-8 · Cited by 98
Identifying the mesenchymal molecular subtype of glioblastoma using quantitative volumetric analysis of anatomic magnetic resonance images
Naeini, K. M., Pope, W. B., Cloughesy, T. F., Harris, R. J., Lai, A., Eskin, A., Chowdhury, R., PHILLIPS, H. S., Nghiemphu, P. L., Behbahanian, Y., et al. (2013) · Neuro-Oncology · doi:10.1093/neuonc/not008 · Cited by 100
Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging
Hectors, S. J. C. G., Wagner, M., Bane, O., Besa, C., Lewis, S. C., Remark, R., Chen, N., Fiel, M. I., Zhu, H., Gnjatic, S., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-02706-z · Cited by 94
Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy
Wu, J., Mayer, A. T., & Li, R. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.12.005 · Cited by 94
Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies
Moskowitz, C. S., Welch, M. L., Jacobs, M. A., Kurland, B. F., & Simpson, A. L. (2022) · Radiology · doi:10.1148/radiol.211597 · Cited by 83
Antiangiogenic Therapy for Primary Liver Cancer: Correlation of Changes in Dynamic Contrast-Enhanced Magnetic Resonance Imaging with Tissue Hypoxia Markers and Clinical Response
Yopp, A. C., Schwartz, L. H., Kemeny, N. E., Gültekin, D. H., Gönen, M., Bamboat, Z. M., Shia, J., Haviland, D., D'Angelica, M. I., Fong, Y., et al. (2011) · Annals of Surgical Oncology · doi:10.1245/s10434-011-1570-1 · Cited by 80
Gene Signatures in the Management of Hepatocellular Carcinoma
Hoshida, Y., Moeini, A., Alsinet, C., Kojima, K., & Villanueva, A. (2012) · Seminars in Oncology · doi:10.1053/j.seminoncol.2012.05.003 · Cited by 74
Radiogenomics and Radiomics in Liver Cancers
Saini, A., Breen, I. D., Pershad, Y., Naidu, S., Knuttinen, M. G., Alzubaidi, S. J., Sheth, R. A., Albadawi, H., Kuo, M., & Öklü, R. (2018) · Diagnostics · doi:10.3390/diagnostics9010004 · Cited by 73
Self-citation
A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer
Zhang, Y., He, K., Guo, Y., Liu, X.-C., Yang, Q., Zhang, C.-Y., Xie, Y., Mu, S., Guo, Y., Fu, Y., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00457 · Cited by 64
Radiomics of hepatocellular carcinoma
Lewis, S. C., Hectors, S. J. C. G., & Taouli, B. A. (2020) · Abdominal Radiology · doi:10.1007/s00261-019-02378-5 · Cited by 70
Non-Invasive Tumor Genotyping Using Radiogenomic Biomarkers, a Systematic Review and Oncology-Wide Pathway Analysis
Jansen, R. W., van Amstel, P., Martens, R. M., Kooi, I. E., Wesseling, P., de Langen, A. J., Menke-Van der Houven van Oordt, C. W., Jansen, B. H. E., Moll, A. C., Dorsman, J. C., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.24893 · Cited by 63
Radiology’s Value Chain
Enzmann, D. R. (2012) · Radiology · doi:10.1148/radiol.12110227 · Cited by 65
The Radiogenomic Risk Score: Construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma
Jamshidi, N., Jonasch, E. W., Zapala, M. A., Korn, R. L., Aganović, L., Zhao, H., Sitaram, R. T., Tibshirani, R. J., Banerjee, S., Brooks, J. D., et al. (2015) · Radiology · doi:10.1148/radiol.2015150800 · Cited by 64
Self-citation
NCTN Assessment on Current Applications of Radiomics in Oncology
Nie, K., Al‐Hallaq, H., Li, X. A., Benedict, S., Sohn, J. W., Moran, J. M., Yong Fan, A., Huang, M., Knopp, M. V., Michalski, J. M., et al. (2019) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2019.01.087 · Cited by 63
Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research.
Saltz, J. H., Almeida, J. S., Gao, Y., Sharma, A., Bremer, E., DiPrima, T., Saltz, M. M., Kalpathy–Cramer, J., & Kurç, T. (2017) · PubMed Central · Cited by 60
Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma
Blanc‐Durand, P., Gucht, A. V. D., Jreige, M., Lalonde, M. N., Silva-Monteiro, M., Prior, J. O., Denys, A. L., Depeursinge, A., & Schaefer, N. G. (2017) · Oncotarget · doi:10.18632/oncotarget.23423 · Cited by 56
Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results
Taouli, B. A., Hoshida, Y., Kakite, S., Chen, X., Tan, P. S., Sun, X., Kihira, S., Kojima, K., Toffanin, S., Fiel, M. I., et al. (2017) · European Radiology · doi:10.1007/s00330-017-4844-6 · Cited by 55
Self-citation
Association of a CT-Based Clinical and Radiomics Score of Non-Small Cell Lung Cancer (NSCLC) with Lymph Node Status and Overall Survival
Botta, F., Raimondi, S., Rinaldi, L. M., Bellerba, F., Corso, F., Bagnardi, V., Origgi, D., Minelli, R., Pitoni, G., Petrella, F., et al. (2020) · Cancers · doi:10.3390/cancers12061432 · Cited by 53
Radiomic analysis of magnetic resonance fingerprinting in adult brain tumors
Dastmalchian, S., Kilinç, Ö., Onyewadume, L., Tippareddy, C., McGivney, D., Ma, D., Griswold, M. A., Sunshine, J. L., Gulani, V., Barnholtz‐Sloan, J. S., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05037-w · Cited by 50
Hepatocellular Carcinoma: State of the Art Imaging and Recent Advances
Navin, P. J., & Venkatesh, S. K. (2019) · Journal of Clinical and Translational Hepatology · doi:10.14218/jcth.2018.00032 · Cited by 49
Radiogenomics in renal cell carcinoma
Alessandrino, F., Shinagare, A. B., Bossé, D., Choueiri, T. K., & Krajewski, K. M. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1624-y · Cited by 46
Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis
Wang, Y., Ma, L., Yin, X., & Gao, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2021.689509 · Cited by 45
Prime Time for Artificial Intelligence in Interventional Radiology
Seah, J. C. Y., Boeken, T., Sapoval, M. R., & Goh, G. S. (2022) · CardioVascular and Interventional Radiology · doi:10.1007/s00270-021-03044-4 · Cited by 46
From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non‐Invasive Precision Medicine in Cancer Patients
Guo, Y., Li, T., Gong, B., Hu, Y., Wang, S., Yang, L., & Zheng, C. (2024) · Advanced Science · doi:10.1002/advs.202408069 · Cited by 49
Radiogenomic Analysis Demonstrates Associations between18F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non–Small Cell Lung Cancer
Yamamoto, S., Huang, D., Du, L., Korn, R. L., Jamshidi, N., Burnette, B., & Kuo, M. (2016) · Radiology · doi:10.1148/radiol.2016160259 · Cited by 43
Self-citation
Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images
Abrol, S., Kotrotsou, A., Salem, A., Zinn, P. O., & Colen, R. R. (2017) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000117 · Cited by 41
New Paradigm for Management of Hepatocellular Carcinoma by Imaging
Joo, I., & Choi, B. I. (2012) · Liver Cancer · doi:10.1159/000342404 · Cited by 38
Next Generation Radiologic-Pathologic Correlation in Oncology: Rad-Path 2.0
Kuo, M., & Yamamoto, S. (2011) · American Journal of Roentgenology · doi:10.2214/ajr.11.7163 · Cited by 35
Self-citation
The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial
Jamshidi, N., Jonasch, E. W., Zapala, M. A., Korn, R. L., Brooks, J. D., Ljungberg, B., & Kuo, M. (2015) · European Radiology · doi:10.1007/s00330-015-4082-8 · Cited by 36
Self-citation
Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib
Fu, S., Chen, S., Liang, C., Liu, Z., Zhu, Y., Li, Y., & Lu, L. (2016) · Oncotarget · doi:10.18632/oncotarget.13675 · Cited by 35
A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation: A Pilot Study on a Novel Biomarker
Ivanics, T., Salinas-Miranda, E., Abreu, P., Khalvati, F., Namdar, K., Dong, X., Deniffel, D., Gorgen, A., Erdman, L., Jhaveri, K. S., et al. (2021) · Transplantation · doi:10.1097/tp.0000000000003605 · Cited by 34
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication
Lee, G., Park, H., Sohn, I., Lee, S.-H., Song, S. H., Kim, H. S., Lee, K. S., Shim, Y. M., & Lee, H. Y. (2018) · The Oncologist · doi:10.1634/theoncologist.2017-0538 · Cited by 33
Radiomics and imaging genomics in precision medicine
Lee, G., Lee, H. Y., Ko, E. S., & Jeong, W. K. (2017) · Precision and Future Medicine · doi:10.23838/pfm.2017.00101 · Cited by 36
From genotype to phenotype: Are there imaging characteristics associated with lung adenocarcinomas harboring RET and ROS1 rearrangements?
Plodkowski, A. J., Drilon, A. E. D. C., Halpenny, D. F., O’Driscoll, D., Blair, D., Litvak, A. M., Zheng, J., Moskowitz, C. S., & Ginsberg, M. S. (2015) · Lung Cancer · doi:10.1016/j.lungcan.2015.09.018 · Cited by 31
An Assessment of Imaging Informatics for Precision Medicine in Cancer
Chennubhotla, C., Clarke, L., Fedorov, A., Foran, D. J., Harris, G. J., Helton, E. D., Nordstrom, R. J., Prior, F., Rubin, D. L., Saltz, J. H., et al. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-041 · Cited by 27
Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models
Kayadibi, Y., Koçak, B., Uçar, N., Akan, Y. N., Akbas, P., & Bektaş, S. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.02.001 · Cited by 28
Applying Precision Oncology Principles in Radiation Oncology
Kamran, S. C., & Mouw, K. W. (2018) · JCO Precision Oncology · doi:10.1200/po.18.00034 · Cited by 26
Machine learning with multimodal data for COVID-19
Chen, W., Sá, R. C., Bai, Y., Napel, S. A., Gevaert, O., Lauderdale, D. S., & Giger, M. L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17934 · Cited by 27
Cancer Genomics and Important Oncologic Mutations: A Contemporary Guide for Body Imagers
Cox, V. L., Bhosale, P., Varadhachary, G. R., Wagner‐Bartak, N. A., Glitza, I. C., Gold, K. A., Atkins, J. T., Soliman, P. T., Hong, D. S., & Qayyum, A. (2017) · Radiology · doi:10.1148/radiol.2017152224 · Cited by 27
A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer
Zhang, K., Ren, Y., Xu, S., Lu, W., Xie, S., Qu, J., Wang, X., Shen, B., Pang, P., Cai, X., et al. (2021) · Medical Physics · doi:10.1002/mp.15001 · Cited by 26
Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer
Mitra, S., & Shankar, B. U. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2325744 · Cited by 25
Radiogenomics in Interventional Oncology
Moussa, A. M., & Ziv, E. (2021) · Current Oncology Reports · doi:10.1007/s11912-020-00994-9 · Cited by 26
Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response
Borhani, A. A., Catania, R., Velichko, Y., Hectors, S. J. C. G., Taouli, B. A., & Lewis, S. C. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-03085-w · Cited by 25
Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features
Hong, Z.-L., Chen, S., Peng, X.-R., Li, J., Yang, J.-C., & Wu, S. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.894476 · Cited by 25
Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer
Wang, Y., Xie, B., Wang, K., Zou, W., Liu, A., Xue, Z., Liu, M., & Ma, Y. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.02.009 · Cited by 24
Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research
Mancini, M., Summers, P. E., Faita, F., Brunetto, M. R., Callea, F., Nicola, A. D., Lascio, N. D., Farinati, F., Gastaldelli, A., Gridelli, B. G., et al. (2018) · World Journal of Hepatology · doi:10.4254/wjh.v10.i2.231 · Cited by 24
Hepatocellular Carcinoma
Hesketh, R. L., Zhu, A. X., & Öklü, R. (2014) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000000123 · Cited by 23
Integrative radiogenomic analysis for multicentric radiophenotype in glioblastoma
Kong, D., Kim, J., Lee, I., Kim, S. T., Seol, H. J., Lee, J.-I., Park, W., Ryu, G.-H., Wang, Z., Ma’ayan, A., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.7115 · Cited by 22
State of the art in magnetic resonance imaging of hepatocellular carcinoma
Horvat, N. D. S. M. R., Monti, S., Oliveira, B. C., Rocha, C. C. T., Giancipoli, R. G., & Mannelli, L. (2018) · Radiology and Oncology · doi:10.2478/raon-2018-0044 · Cited by 22
Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules
Shi, S., An, X., & Li, Y. (2022) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16078 · Cited by 24
Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study
Xie, C., Hu, Y., Ho, J. W. K., Han, L., Yang, H., Wen, J., Lam, K. O., Wong, Y., Law, S. Y.-K., Chiu, K. W.-H., et al. (2021) · Cancers · doi:10.3390/cancers13092145 · Cited by 22
Radiomics and circulating tumor cells: personalized care in hepatocellular carcinoma?
Hesketh, R. L., Zhu, A. X., & Öklü, R. (2014) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2014.14237 · Cited by 21
Radiogenomics of gastroenterological cancer: The dawn of personalized medicine with artificial intelligence‐based image analysis
Hoshino, I., & Yokota, H. (2021) · Annals of Gastroenterological Surgery · doi:10.1002/ags3.12437 · Cited by 19
Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade
Pulvirenti, A., Yamashita, R., Chakraborty, J., Horvat, N. D. S. M. R., Seier, K., McIntyre, C. A., Lawrence, S., Midya, A., Koszalka, M. A., Gönen, M., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00121 · Cited by 18
A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast‐Enhanced MRI Radiomics
Cao, X., Yang, H., Luo, X., Zou, L., Zhang, Q., Li, Q., Zhang, J., Xiang-feng, L., Shi, Y. X., & Jin, C. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28725 · Cited by 18
Feasibility of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer
Guo, X., Yang, W.-Q., Yang, Q., Yuan, Z., Liu, Y., Niu, X., & Xu, H. (2020) · Current Medical Science · doi:10.1007/s11596-020-2298-6 · Cited by 19
Tumor compactness improves the preoperative volumetry-based prediction of the pathological complete response of rectal cancer after preoperative concurrent chemoradiotherapy
Hsu, C.-Y., Wang, C., Kuo, C., Chen, Y., Lan, K.-H., Cheng, A., & Kuo, S. (2016) · Oncotarget · doi:10.18632/oncotarget.13855 · Cited by 17
Detection and Analysis of Lung Cancer Using Radiomic Approach
Shallu, Nanglia, P., Kumar, S., & Luhach, A. K. (2019) · Springer eBooks · doi:10.1007/978-981-13-6295-8_2 · Cited by 17

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.
ORCID: Kuo, Michael D.
Gollub, Jeremy
Sirlin, Claude B.
Chen, Xin
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)
DOI: 10.1016/j.jvir.2007.04.031
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Radiogenomic Analysis to Identify Imaging Phenotypes Associated with Drug Response Gene Expression Programs in Hepatocellular Carcinoma

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks