Now showing items 1-9 of 9

    • Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms 

      El-Hachem, Nehme; Grossmann, Patrick; Blanchet-Cohen, Alexis; Bateman, Alain R.; Bouchard, Nicolas; Archambault, Jacques; Aerts, Hugo J.W.L.; Haibe-Kains, Benjamin (National Institute of Environmental Health Sciences, 2015)
      Background: Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project–Genomics Assisted Toxicity ...
    • Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach 

      Aerts, Hugo J. W. L.; Velazquez, Emmanuel Rios; Leijenaar, Ralph T. H.; Parmar, Chintan; Grossmann, Patrick; Cavalho, Sara; Bussink, Johan; Monshouwer, René; Haibe-Kains, Benjamin; Rietveld, Derek; Hoebers, Frank; Rietbergen, Michelle M.; Leemans, C. René; Dekker, Andre; Quackenbush, John; Gillies, Robert J.; Lambin, Philippe (Nature Pub. Group, 2014)
      Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative ...
    • Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC 

      Aerts, Hugo J. W. L.; Grossmann, Patrick; Tan, Yongqiang; Oxnard, Geoffrey G.; Rizvi, Naiyer; Schwartz, Lawrence H.; Zhao, Binsheng (Nature Publishing Group, 2016)
      Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization ...
    • Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology 

      Wu, Weimiao; Parmar, Chintan; Grossmann, Patrick; Quackenbush, John; Lambin, Philippe; Bussink, Johan; Mak, Raymond; Aerts, Hugo J. W. L. (Frontiers Media S.A., 2016)
      Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between ...
    • Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma 

      Grossmann, Patrick; Gutman, David A.; Dunn, William D.; Holder, Chad A.; Aerts, Hugo J. W. L. (BioMed Central, 2016)
      Background: Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely ...
    • Integration of TP53, DREAM, MMB-FOXM1 and RB-E2F target gene analyses identifies cell cycle gene regulatory networks 

      Fischer, Martin; Grossmann, Patrick; Padi, Megha; DeCaprio, James A. (Oxford University Press, 2016)
      Cell cycle (CC) and TP53 regulatory networks are frequently deregulated in cancer. While numerous genome-wide studies of TP53 and CC-regulated genes have been performed, significant variation between studies has made it ...
    • Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer 

      Parmar, Chintan; Grossmann, Patrick; Rietveld, Derek; Rietbergen, Michelle M.; Lambin, Philippe; Aerts, Hugo J. W. L. (Frontiers Media S.A., 2015)
      Introduction: “Radiomics” extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic ...
    • Recurrent hormone-binding domain truncated ESR1 amplifications in primary endometrial cancers suggest their implication in hormone independent growth 

      Holst, Frederik; Hoivik, Erling A.; Gibson, William J.; Taylor-Weiner, Amaro; Schumacher, Steven E.; Asmann, Yan W.; Grossmann, Patrick; Trovik, Jone; Necela, Brian M.; Thompson, E. Aubrey; Meyerson, Matthew; Beroukhim, Rameen; Salvesen, Helga B.; Cherniack, Andrew D. (Nature Publishing Group, 2016)
      The estrogen receptor alpha (ERα) is highly expressed in both endometrial and breast cancers, and represents the most prevalent therapeutic target in breast cancer. However, anti-estrogen therapy has not been shown to be ...
    • Somatic mutations associated with MRI-derived volumetric features in glioblastoma 

      Gutman, David A.; Dunn, William D.; Grossmann, Patrick; Cooper, Lee A. D.; Holder, Chad A.; Ligon, Keith L.; Alexander, Brian M.; Aerts, Hugo J. W. L. (Springer Berlin Heidelberg, 2015)
      Introduction: MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor ...