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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Radiological phenotypes are fundamentally driven by the underlying pathophysiology.
“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]
A watershed article came from Segal et al, who used a finite series of radiologist-scored quantitative features to predict gene expression patterns in hepatocellular carcinoma.
“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]
Seminal radiogenomic studies were the first to show a relationship between quantitative image features and gene expression patterns in patients with cancer.
“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]
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 · see citing works
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 · see citing works
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 · see citing works
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 6,411 · see citing works
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,009 · see citing works
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 2,576 · see citing works
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,686 · see citing works
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 1,292 · see citing works
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 990 · see citing works
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 · see citing works
Self-citationGene Expression Patterns in Human Liver Cancers
Chen, X., Cheung, S., So, S. K., Fan, S. T., Barry, C. T., Higgins, J., Lai, K.-M., Ji, J., Dudoit, S., Ng, I. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0023 · Cited by 879 · see citing works
Self-citationCommon markers of proliferation
Whitfield, M. L., George, L. K., Grant, G. D., & Perou, C. M. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1802 · Cited by 650 · cited 1× · see citing works
“The 'venous invasion signature' comprises genes involved in cell proliferation ( CDK4 , CDC20 , MCM5 ) and matrix invasion ( ADAMTS1 , MMP14, SPARCL1 )—genes known to be induced in other HCC data sets and indeed in many types of cancer”
BackgroundFrom signatures to models: understanding cancer using microarrays
Segal, E., Friedman, N., Kaminski, N., Regev, A., & Koller, D. (2005) · Nature Genetics · doi:10.1038/ng1561 · Cited by 380 · see citing works
Self-citationThe prognostic molecular markers in hepatocellular carcinoma
Qin, L. (2002) · World Journal of Gastroenterology · doi:10.3748/wjg.v8.i3.385 · Cited by 311 · see citing works
Molecular portraits and the family tree of cancer
Chung, C. H., Bernard, P. S., & Perou, C. M. (2002) · Nature Genetics · doi:10.1038/ng1038 · Cited by 264 · see citing works
Focal Liver Lesions: Pattern-based Classification Scheme for Enhancement at Arterial Phase CT
Nino‐Murcia, M., Olcott, E. W., Jeffrey, R. B., Lamm, R. L., Beaulieu, C. F., & Jain, K. A. (2000) · Radiology · doi:10.1148/radiology.215.3.r00jn03746 · Cited by 197 · see citing works
Clinical role of MMP‐2/TIMP‐2 imbalance in hepatocellular carcinoma
Giannelli, G., Bergamini, C., Marinosci, F., Fransvea, E., Quaranta, M., Lupo, L., Schiraldi, O., & Antonaci, S. (2001) · International Journal of Cancer · doi:10.1002/ijc.1635 · Cited by 187 · see citing works
Opportunities for Targeted Therapies in Hepatocellular Carcinoma
Thomas, M. B., & Abbruzzese, J. L. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2004.00.1537 · Cited by 172 · see citing works
Clinical Significance of Thrombospondin 1 Expression in Hepatocellular Carcinoma
Poon, R. T. P., Chung, K. K., Cheung, S., Lau, C. P.-Y., Tong, S. W., Leung, K. L., Yu, W. C., Tuszynski, G. P., & Fan, S. T. (2004) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-03-0435 · Cited by 98 · see citing works
Targeting angiogenesis in cancer: clinical development of bevacizumab
Kerr, R. S. (2004) · Nature Clinical Practice Oncology · doi:10.1038/ncponc0026 · Cited by 97 · see citing works
Radiology an Illustrated History
Eisenberg, R. L. (1995) · Medical Entomology and Zoology · Cited by 88 · see citing works
Radiology: An Illustrated History
Clark, J. M. (1992) · JAMA · doi:10.1001/jama.1992.03490010144043 · Cited by 17 · see citing works
Flagged first, then by citations
Cited by
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
“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].”
MethodMachine Learning methods for Quantitative Radiomic Biomarkers
Parmar, C., Großmann, P., Bussink, J., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep13087 · Cited by 1,024 · see citing works
Vallières, M., Freeman, C. R., Skamene, S., & Naqa, I. E. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/14/5471 · Cited by 892 · see citing works
Tumour evolution in hepatocellular carcinoma
Craig, A. J., von Felden, J., García‐Lezana, T., Sarcognato, S., & Villanueva, A. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0229-4 · Cited by 825 · see citing works
Limkin, E. J., Sun, R., Dercle, L., Zacharaki, E. I., Robert, C., Reuzé, S., Schernberg, A., Nikos Paragios, A., Deutsch, É., & Ferté, C. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx034 · Cited by 756 · see citing works
Tixier, F., Rest, C. C. L., Hatt, M., Albarghach, N. M., Pradier, O., Metges, J., Corcos, L., & Visvikis, D. (2011) · Journal of Nuclear Medicine · doi:10.2967/jnumed.110.082404 · Cited by 695 · see citing works
Measuring Computed Tomography Scanner Variability of Radiomics Features
Mackin, D., Fave, X. J., Zhang, L., Fried, D., Yang, J., Taylor, B. A., Rodriguez-Rivera, E., Dodge, C. T., Jones, A. K., & Court, L. E. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000180 · Cited by 649 · see citing works
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Parmar, C., Velazquez, E. R., Leijenaar, R. T. H., Jermoumi, M., Carvalho, S., Mak, R. H., Mitra, S., Shankar, B. U., Kikinis, R., Haibe‐Kains, B., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102107 · Cited by 611 · see citing works
Fernández, M., Semela, D., Bruix, J., Colle, I. O., Pinzani, M., & Bosch, J. (2009) · Journal of Hepatology · doi:10.1016/j.jhep.2008.12.011 · Cited by 598 · see citing works
The Biological Meaning of Radiomic Features
Tomaszewski, M. R., & Gillies, R. J. (2021) · Radiology · doi:10.1148/radiol.2021202553 · Cited by 568 · see citing works
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Vallières, M., Kay‐Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen‐Tan, P. F., Wang, C.-S., Sultanem, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10371-5 · Cited by 543 · see citing works
Quantitative Imaging in Cancer Evolution and Ecology
Gatenby, R. A., Grove, O., & Gillies, R. J. (2013) · Radiology · doi:10.1148/radiol.13122697 · Cited by 515 · see citing works
Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis
Chicklore, S., Goh, V., Siddique, M. M., Roy, A. S., Marsden, P. K., & Cook, G. (2012) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-012-2247-0 · Cited by 485 · see citing works
Characterization of PET/CT images using texture analysis: the past, the present… any future?
Hatt, M., Tixier, F., Pierce, L. A., Kinahan, P. E., Rest, C. C. L., & Visvikis, D. (2016) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-016-3427-0 · Cited by 480 · see citing works
New approaches for imaging tumour responses to treatment
Brindle, K. M. (2008) · Nature reviews. Cancer · doi:10.1038/nrc2289 · Cited by 447 · see citing works
Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
Diehn, M., Nardini, C., Wang, D. S., McGovern, S., Jayaraman, M., Liang, Y., Aldape, K., Cha, S., & Kuo, M. D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801279105 · Cited by 440 · see citing works
Self-citationRadiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Parmar, C., Leijenaar, R. T. H., Großmann, P., Velazquez, E. R., Bussink, J., Rietveld, D. H. F., Rietbergen, M. M., Haibe‐Kains, B., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep11044 · Cited by 433 · see citing works
Prognostic and Therapeutic Implications of Microvascular Invasion in Hepatocellular Carcinoma
Erstad, D. J., & Tanabe, K. K. (2019) · Annals of Surgical Oncology · doi:10.1245/s10434-019-07227-9 · Cited by 423 · see citing works
Eccles, S. A., Aboagye, E. O., Ali, S., Anderson, A. S., Armes, J., Berditchevski, F., Blaydes, J. P., Brennan, K. R., Brown, N. J., Bryant, H. E., et al. (2013) · Breast Cancer Research · doi:10.1186/bcr3493 · Cited by 416 · see citing works
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Parmar, C., Großmann, P., Rietveld, D. H. F., Rietbergen, M. M., Lambin, P., & Aerts, H. J. W. L. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00272 · Cited by 410 · see citing works
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 · see citing works
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 395 · see citing works
Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0070 · Cited by 388 · see citing works
Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin
Milano, A., Pendergrass, S. A., Sargent, J. L., George, L. K., McCalmont, T. H., Connolly, M. K., & Whitfield, M. L. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002696 · Cited by 384 · see citing works
Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology
Wu, W., Parmar, C., Großmann, P., Quackenbush, J. F., Lambin, P., Bussink, J., Mak, R. H., & Aerts, H. J. W. L. (2016) · Frontiers in Oncology · doi:10.3389/fonc.2016.00071 · Cited by 378 · see citing works
Renzulli, M., Brocchi, S., Cucchetti, A., Mazzotti, F., Mosconi, C., Sportoletti, C., Brandi, G., Pinna, A. D., & Golfieri, R. (2015) · Radiology · doi:10.1148/radiol.2015150998 · Cited by 374 · see citing works
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 · see citing works
Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife · doi:10.7554/elife.23421 · Cited by 365 · see citing works
Larue, R. T. H. M., Defraene, G., Ruysscher, D. D., Lambin, P., & van Elmpt, W. (2016) · British Journal of Radiology · doi:10.1259/bjr.20160665 · Cited by 361 · see citing works
Banerjee, S., Wang, D. S., Kim, H. J., Sirlin, C. B., Chan, M. G., Korn, R. L., Rutman, A. M., Siripongsakun, S., Lu, D., Imanbayev, G., et al. (2015) · Hepatology · doi:10.1002/hep.27877 · Cited by 354 · see citing works
Self-citationThe Biological Meaning of Radiomic Features
Tomaszewski, M. R., & Gillies, R. J. (2021) · Radiology · doi:10.1148/radiol.2021219005 · Cited by 327 · see citing works
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., & Plevritis, S. K. (2014) · Radiology · doi:10.1148/radiol.14131731 · Cited by 319 · see citing works
A radiogenomic dataset of non-small cell lung cancer
Bakr, S. H., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A. N. C., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.202 · Cited by 319 · see citing works
ibex : An open infrastructure software platform to facilitate collaborative work in radiomics
Zhang, L., Fried, D., Fave, X. J., Hunter, L. A., Yang, J., & Court, L. E. (2015) · Medical Physics · doi:10.1118/1.4908210 · Cited by 316 · see citing works
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 314 · see citing works
Self-citationRadiogenomics: 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 · see citing works
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images
Balagurunathan, Y., Gu, Y., Wang, H., Kumar, V., Grove, O., Hawkins, S. K., Kim, J., Goldgof, D. B., Hall, L., Gatenby, R. A., et al. (2014) · Translational Oncology · doi:10.1593/tlo.13844 · Cited by 305 · see citing works
Grove, O., Berglund, A. E., Schabath, M. B., Aerts, H. J. W. L., Dekker, A., Wang, H., Velazquez, E. R., Lambin, P., Gu, Y., Balagurunathan, Y., et al. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118261 · Cited by 263 · see citing works
Test–Retest Reproducibility Analysis of Lung CT Image Features
Balagurunathan, Y., Kumar, V., Gu, Y., Kim, J., Wang, H., Liu, Y., Goldgof, D. B., Hall, L., Korn, R., Zhao, B., et al. (2014) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-014-9716-x · Cited by 257 · see citing works
Kuo, M. D., & Jamshidi, N. (2014) · Radiology · doi:10.1148/radiol.13132195 · Cited by 238 · see citing works
Self-citationQuantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Napel, S. A., Mu, W., Jardim‐Perassi, B. V., Aerts, H. J. W. L., & Gillies, R. J. (2018) · Cancer · doi:10.1002/cncr.31630 · Cited by 229 · see citing works
Zhou, Y., He, L., Huang, Y., Chen, S., Wu, P., Ye, W., Liu, Z., & Liang, C. (2017) · Abdominal Radiology · doi:10.1007/s00261-017-1072-0 · Cited by 224 · see citing works
Multiparametric Imaging of Tumor Response to Therapy
Padhani, A. R., & Miles, K. A. (2010) · Radiology · doi:10.1148/radiol.10091760 · Cited by 219 · see citing works
CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer
Huynh, E., Coroller, T. P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.05.024 · Cited by 215 · see citing works
Lu, H., Arshad, M., Thornton, A., Avesani, G., Cunnea, P., Curry, E. W., Kanavati, F., Liang, J., Nixon, K., Williams, S. T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08718-9 · Cited by 213 · see citing works
Radiomics and artificial intelligence for precision medicine in lung cancer treatment
Chen, M., Copley, S. J., Viola, P., Lu, H., & Aboagye, E. O. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.05.004 · Cited by 213 · see citing works
Drug Delivery Nanoparticles Formulation and Characterization
(2016) · CRC Press eBooks · doi:10.3109/9781420078053 · Cited by 205 · see citing works
Feng, S., Jia, Y., Liao, B., Huang, B., Zhou, Q., Li, X., Wei, K., Chen, L., Li, B., Wang, W., et al. (2019) · European Radiology · doi:10.1007/s00330-018-5935-8 · Cited by 199 · see citing works
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Gustafsson, M., Nestor, C. E., Zhang, H., Barabási, A.-L., Baranzini, S., Brunak, S., Chung, K. F., Federoff, H. J., Gavin, A.-C., Meehan, R. R., et al. (2014) · Genome Medicine · doi:10.1186/s13073-014-0082-6 · Cited by 194 · see citing works
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 · see citing works
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 · see citing works
Background, current role, and potential applications of radiogenomics
Pinker, K., Shitano, F., Sala, E., Do, R. K., Young, R. J., Wibmer, A. G., Hricak, H., Sutton, E. J., & Morris, E. A. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25870 · Cited by 184 · see citing works
Combining molecular and imaging metrics in cancer: radiogenomics
Gullo, R. L., Naranjo, I. D., Morris, E. A., & Pinker, K. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0795-6 · Cited by 181 · see citing works
Xie, C., Yang, P., Zhang, X., Xu, L., Wang, X., Li, X., Zhang, L., Xie, R., Yang, L., Zhao, J., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.05.023 · Cited by 178 · see citing works
European Society of Radiology (ESR) (2015) · Insights into Imaging · doi:10.1007/s13244-015-0394-0 · Cited by 178 · see citing works
The biology underlying molecular imaging in oncology: from genome to anatome and back again
Gillies, R. J., Anderson, A. R. A., Gatenby, R. A., & Morse, D. L. (2010) · Clinical Radiology · doi:10.1016/j.crad.2010.04.005 · Cited by 170 · see citing works
Visvikis, D., Rest, C. C. L., Jaouen, V., & Hatt, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04373-w · Cited by 168 · see citing works
Predictive radiogenomics modeling of EGFR mutation status in lung cancer
Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., et al. (2017) · Scientific Reports · doi:10.1038/srep41674 · Cited by 160 · see citing works
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 · see citing works
Self-citationUnderstanding Sources of Variation to Improve the Reproducibility of Radiomics
Zhao, B. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.633176 · Cited by 158 · see citing works
Chen, S., Feng, S., Wei, J., Liu, F., Li, B., Li, X., Hou, Y., Gu, D., Tang, M., Xiao, H., et al. (2019) · European Radiology · doi:10.1007/s00330-018-5986-x · Cited by 157 · see citing works
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 156 · see citing works
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 154 · see citing works
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 151 · see citing works
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 · see citing works
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 143 · see citing works
Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges
Bashir, U., Siddique, M. M., McLean, E., Goh, V., & Cook, G. (2016) · American Journal of Roentgenology · doi:10.2214/ajr.15.15864 · Cited by 142 · see citing works
Rhee, H., Cho, E., Nahm, J. H., Jang, M., Chung, Y. E., Baek, S., Lee, S., Kim, M., Park, M., Han, D. H., et al. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.08.013 · Cited by 140 · see citing works
Halle, C., Andersen, E. K. F., Lando, M., Aarnes, E., Hasvold, G., Holden, M., Syljuåsen, R. G., Sundfør, K., Kristensen, G. B., Holm, R., et al. (2012) · Cancer Research · doi:10.1158/0008-5472.can-12-1085 · Cited by 138 · see citing works
Introduction to radiomics for a clinical audience
McCague, C., Ramlee, S., Reinius, M., SELBY, I. A., Hulse, D., Piyatissa, P., Bura, V., Crispin‐Ortuzar, M., Sala, E., & Woitek, R. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.08.149 · Cited by 137 · see citing works
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 136 · see citing works
Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features
Hawkins, S. H., Korecki, J. N., Balagurunathan, Y., Gu, Y., Kumar, V., Basu, S., Hall, L., Goldgof, D. B., Gatenby, R. A., & Gillies, R. J. (2014) · IEEE Access · doi:10.1109/access.2014.2373335 · Cited by 135 · see citing works
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 134 · see citing works
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 · see citing works
Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Buscarino, V., Colarieti, A., Tomao, F., Aletti, G. D., Zanagnolo, V. L., Grande, M. D., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5389-z · Cited by 131 · see citing works
Radiomics in Oncological PET/CT: a Methodological Overview
Ha, S., Choi, H., Paeng, J. C., & Cheon, G. J. (2019) · Nuclear Medicine and Molecular Imaging · doi:10.1007/s13139-019-00571-4 · Cited by 129 · see citing works
Radiomics in PET: principles and applications
Cook, G., Siddique, M. M., Taylor, B. P., Yip, C., Chicklore, S., & Goh, V. (2014) · Clinical and Translational Imaging · doi:10.1007/s40336-014-0064-0 · Cited by 128 · see citing works
Hu, X., Sun, J., Li, F., Li, R., Wu, J., He, J., Wang, N., Liu, J., Wang, S., Zhou, F., et al. (2018) · Nano Letters · doi:10.1021/acs.nanolett.7b04741 · Cited by 128 · see citing works
Cicinnati, V. R., Shen, Q., Sotiropoulos, G. C., Radtke, A., Gerken, G., & Beckebaum, S. (2008) · BMC Cancer · doi:10.1186/1471-2407-8-350 · Cited by 128 · see citing works
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 · see citing works
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 · see citing works
Prostate cancer radiomics and the promise of radiogenomics
Stoyanova, R. S., Takhar, M. M., Tschudi, Y., Ford, J. C., Solórzano, G. A. A., Erho, N., Balagurunathan, Y., Punnen, S. P., Davicioni, E., Gillies, R. J., et al. (2016) · Translational Cancer Research · doi:10.21037/tcr.2016.06.20 · Cited by 126 · see citing works
MRI features can predict EGFR expression in lower grade gliomas: A voxel-based radiomic analysis
Li, Y., Liu, X., Xu, K., Qian, Z., Wang, K., Fan, X., Li, S., Wang, Y., & Jiang, T. (2017) · European Radiology · doi:10.1007/s00330-017-4964-z · Cited by 126 · see citing works
Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers
Giraud, P., Giraud, P., Gasnier, A., Ayachy, R. E., Kreps, S., Foy, J., Durdux, C., Huguet, F., Burgun, A., & Bibault, J. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00174 · Cited by 125 · see citing works
Reuzé, S., Orlhac, F., Chargari, C., Nioche, C., Limkin, E. J., Riet, F. G., Escande, A., Haie-Méder, C., Dercle, L., Gouy, S., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.17856 · Cited by 124 · see citing works
Quantitative multimodality imaging in cancer research and therapy
Yankeelov, T. E., Abramson, R. G., & Quarles, C. C. (2014) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2014.134 · Cited by 122 · see citing works
Vargas, H. A., Veeraraghavan, H., Miccò, M., Nougaret, S., Lakhman, Y., Meier, A. A., Sosa, R. E., Soslow, R. A., Levine, D. A., Weigelt, B., et al. (2017) · European Radiology · doi:10.1007/s00330-017-4779-y · Cited by 121 · see citing works
Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101146 · Cited by 120 · see citing works
“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.”
BackgroundYamamoto, 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 · see citing works
Self-citationRadiomics in liver diseases: Current progress and future opportunities
Wei, J., Jiang, H., Gu, D., Niu, M., Fu, F., Han, Y., Song, B., & Tian, J. (2020) · Liver International · doi:10.1111/liv.14555 · Cited by 120 · see citing works
Novel Nomogram for Preoperative Prediction of Early Recurrence in Intrahepatic Cholangiocarcinoma
Liang, W., Xu, L., Yang, P., Zhang, L., Wan, D., Huang, Q., Niu, T., & Chen, F. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00360 · Cited by 119 · see citing works
Identification of high-risk carotid plaque with MRI-based radiomics and machine learning
Zhang, R., Zhang, Q., Ji, A., Lv, P., Zhang, J., Fu, C., & Lin, J. (2020) · European Radiology · doi:10.1007/s00330-020-07361-z · Cited by 118 · see citing works
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 117 · see citing works
Self-citationMorin, O., Vallières, M., Jochems, A., Woodruff, H. C., Valdés, G., Braunstein, S. E., Wildberger, J. E., Villanueva-Meyer, J. E., Kearney, V. P., Yom, S. S., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.08.032 · Cited by 116 · see citing works
Mehta, N. J., Dodge, J. L., Roberts, J. P., & Yao, F. Y. (2017) · American Journal of Transplantation · doi:10.1111/ajt.14549 · Cited by 115 · see citing works
Wu, J., Cui, Y., Sun, X., Cao, G., Li, B., Ikeda, D. M., Kurian, A. W., & Li, R. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-2415 · Cited by 114 · see citing works
- 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
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
