Browsing by Author "Aerts, Hugo"

Now showing items 1-20 of 22

    • Assessment of pharmacogenomic agreement 

      Safikhani, Zhaleh; El-Hachem, Nehme; Quevedo, Rene; Smirnov, Petr; Goldenberg, Anna; Juul Birkbak, Nicolai; Mason, Christopher; Hatzis, Christos; Shi, Leming; Aerts, Hugo JWL; Quackenbush, John; Haibe-Kains, Benjamin (F1000Research, 2016)
      In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer ...

    • Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT 

      Huynh, Elizabeth; Coroller, Thibaud P.; Narayan, Vivek; Agrawal, Vishesh; Romano, John; Franco, Idalid; Parmar, Chintan; Hou, Ying; Mak, Raymond H.; Aerts, Hugo J. W. L. (Public Library of Science, 2017)
      Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) ...

    • 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 ...

    • Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer 

      Yip, Stephen; McCall, Keisha; Aristophanous, Michalis; Chen, Aileen B.; Aerts, Hugo J. W. L.; Berbeco, Ross (Public Library of Science, 2014)
      Background: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing ...

    • CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma 

      Coroller, Thibaud Patrick; Grossmann, Patrick; Hou, Ying; Rios Velazquez, Emmanuel; Leijenaar, Ralph T.H.; Hermann, Gretchen; Lambin, Philippe; Haibe-Kains, Benjamin; Mak, Raymond Heungwing; Aerts, Hugo (Elsevier BV, 2015)
      Background and Purpose: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic ...

    • 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 ...

    • diXa: a data infrastructure for chemical safety assessment 

      Hendrickx, Diana M.; Aerts, Hugo J.W.L.; Caiment, Florian; Clark, Dominic; Ebbels, Timothy M.D.; Evelo, Chris T.; Gmuender, Hans; Hebels, Dennie G.A.J.; Herwig, Ralf; Hescheler, Jürgen; Jennen, Danyel G.J.; Jetten, Marlon J.A.; Kanterakis, Stathis; Keun, Hector C.; Matser, Vera; Overington, John P.; Pilicheva, Ekaterina; Sarkans, Ugis; Segura-Lepe, Marcelo P.; Sotiriadou, Isaia; Wittenberger, Timo; Wittwehr, Clemens; Zanzi, Antonella; Kleinjans, Jos C.S. (Oxford University Press, 2014)
      Motivation: The field of toxicogenomics (the application of ‘-omics’ technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing ...

    • The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis 

      Leijenaar, Ralph T.H.; Nalbantov, Georgi; Carvalho, Sara; van Elmpt, Wouter J.C.; Troost, Esther G.C.; Boellaard, Ronald; Aerts, Hugo J.W.L; Gillies, Robert J.; Lambin, Philippe (Nature Publishing Group, 2015)
      FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled ...

    • 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 ...

    • Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features 

      Rios Velazquez, Emmanuel; Meier, Raphael; Dunn Jr, William D.; Alexander, Brian; Wiest, Roland; Bauer, Stefan; Gutman, David A.; Reyes, Mauricio; Aerts, Hugo J.W.L. (Nature Publishing Group, 2015)
      Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the ...

    • 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 ...

    • Importance of collection in gene set enrichment analysis of drug response in cancer cell lines 

      Bateman, Alain R.; El-Hachem, Nehme; Beck, Andrew H.; Aerts, Hugo J. W. L.; Haibe-Kains, Benjamin (Nature Publishing Group, 2014)
      Gene set enrichment analysis (GSEA) associates gene sets and phenotypes, its use is predicated on the choice of a pre-defined collection of sets. The defacto standard implementation of GSEA provides seven collections yet ...

    • Machine Learning methods for Quantitative Radiomic Biomarkers 

      Parmar, Chintan; Grossmann, Patrick; Bussink, Johan; Lambin, Philippe; Aerts, Hugo J. W. L. (Nature Publishing Group, 2015)
      Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications ...

    • Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma 

      Grove, Olya; Berglund, Anders E.; Schabath, Matthew B.; Aerts, Hugo J. W. L.; Dekker, Andre; Wang, Hua; Velazquez, Emmanuel Rios; Lambin, Philippe; Gu, Yuhua; Balagurunathan, Yoganand; Eikman, Edward; Gatenby, Robert A.; Eschrich, Steven; Gillies, Robert J. (Public Library of Science, 2015)
      Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained ...

    • Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer 

      Parmar, Chintan; Leijenaar, Ralph T. H.; Grossmann, Patrick; Rios Velazquez, Emmanuel; Bussink, Johan; Rietveld, Derek; Rietbergen, Michelle M.; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, Hugo J.W.L. (Nature Publishing Group, 2015)
      Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic ...

    • 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 ...

    • Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients 

      Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I. (Frontiers Media S.A., 2016)
      Purpose Although change in standardized uptake value (SUV) measures and PET-based textural features during treatment have shown promise in tumor response prediction, it is unclear which quantitative measure is the most ...

    • Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation 

      Parmar, Chintan; Rios Velazquez, Emmanuel; Leijenaar, Ralph; Jermoumi, Mohammed; Carvalho, Sara; Mak, Raymond H.; Mitra, Sushmita; Shankar, B. Uma; Kikinis, Ron; Haibe-Kains, Benjamin; Lambin, Philippe; Aerts, Hugo J. W. L. (Public Library of Science, 2014)
      Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable ...

    • 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 ...