Integrating Radio Imaging With Gene Expressions Toward a Personalized Management of Cancer
暂无分享,去创建一个
[1] A. Padhani,et al. Multiparametric imaging of tumor response to therapy. , 2010, Radiology.
[2] B. Wouters. Proteomics: methodologies and applications in oncology. , 2008, Seminars in radiation oncology.
[3] Rangaraj M. Rangayyan,et al. Content-based retrieval and analysis of mammographic masses , 2005, J. Electronic Imaging.
[4] K. Aldape,et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.
[5] S. Atlas,et al. Magnetic resonance image–guided proteomics of human glioblastoma multiforme , 2003, Journal of magnetic resonance imaging : JMRI.
[6] Pau-Choo Chung,et al. Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules , 2003, IEEE Transactions on Information Technology in Biomedicine.
[7] Ash A. Alizadeh,et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.
[8] A. Rutman,et al. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. , 2009, European journal of radiology.
[9] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[10] D. Mankoff,et al. Imaging Tumor Phenotype: 1 Plus 1 Is More Than 2 , 2009, Journal of Nuclear Medicine.
[11] Sushmita Mitra,et al. Evolutionary Rough Feature Selection in Gene Expression Data , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[12] Olivier Gevaert,et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.
[13] Issam El-Naqa,et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..
[14] S G Russell,et al. The value of image-guided intensity-modulated radiotherapy in challenging clinical settings. , 2013, The British journal of radiology.
[15] R. Tibshirani,et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[16] J. Bradley,et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.
[17] P. Lambin,et al. Predicting outcomes in radiation oncology—multifactorial decision support systems , 2013, Nature Reviews Clinical Oncology.
[18] N G Burnet,et al. The genomics revolution and radiotherapy. , 2007, Clinical oncology (Royal College of Radiologists (Great Britain)).
[19] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[20] Karin Haustermans,et al. PET-based treatment planning in radiotherapy: a new standard? , 2007, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.
[21] Anders Eklund,et al. Medical image processing on the GPU - Past, present and future , 2013, Medical Image Anal..
[22] C. Jaffe. Imaging and genomics: is there a synergy? , 2012, Radiology.
[23] D. D. Maki,et al. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. , 2012, AJR. American journal of roentgenology.
[24] Michael E. Phelps,et al. Noninvasive prediction of tumor responses to gemcitabine using positron emission tomography , 2009, Proceedings of the National Academy of Sciences.
[25] L. Schwartz,et al. Promise and pitfalls of quantitative imaging in oncology clinical trials. , 2012, Magnetic resonance imaging.
[26] A Rao,et al. CT Imaging Correlates of Genomic Expression for Oral Cavity Squamous Cell Carcinoma , 2013, American Journal of Neuroradiology.
[27] R. Tibshirani,et al. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. , 2004, The New England journal of medicine.
[28] Baris Turkbey,et al. Review of functional/anatomical imaging in oncology , 2012, Nuclear medicine communications.
[29] Hans-Peter Meinzer,et al. Statistical shape models for 3D medical image segmentation: A review , 2009, Medical Image Anal..
[30] Shitong Wang,et al. Advanced fuzzy cellular neural network: Application to CT liver images , 2007, Artif. Intell. Medicine.
[31] Paul S Mischel,et al. Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. , 2008, Radiology.
[32] Rangaraj M. Rangayyan,et al. Errata: Content-based retrieval and analysis of mammographic masses , 2007, J. Electronic Imaging.
[33] Xiaolei Huang,et al. Medical Image Segmentation , 2009 .
[34] R. Tibshirani,et al. Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[35] M. Hatt,et al. Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.
[36] Sankar K. Pal,et al. Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing , 1999 .
[37] B. Nicolas Bloch,et al. An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer , 2008, Magnetic Resonance Materials in Physics, Biology and Medicine.
[38] Ahmet Yardimci,et al. Soft computing in medicine , 2009, Appl. Soft Comput..
[39] S. Mukherjee,et al. A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. , 2006, The New England journal of medicine.
[40] Howard Y. Chang,et al. Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.
[41] K. Miles,et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.
[42] Xinjian Chen,et al. Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images , 2013, Medical Image Anal..
[43] J. Alison Noble,et al. Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.
[44] Rafael García,et al. Computerized analysis of pigmented skin lesions: A review , 2012, Artif. Intell. Medicine.
[45] S. Mitra,et al. Bioinformatics with soft computing , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[46] H. Hricak,et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. , 2013, Radiology.
[47] D. Botstein,et al. Gene expression profiling reveals molecularly and clinically distinct subtypes of glioblastoma multiforme. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[48] S. Thorgeirsson,et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling , 2004, Hepatology.
[49] Abdul Rahman Ramli,et al. Review of brain MRI image segmentation methods , 2010, Artificial Intelligence Review.
[50] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[51] M. Kuo,et al. Radiogenomic analysis to identify imaging phenotypes associated with drug response gene expression programs in hepatocellular carcinoma. , 2007, Journal of vascular and interventional radiology : JVIR.
[52] Baris Turkbey,et al. Review of Functional / Anatomic Imaging in Oncology , 2012 .
[53] Anke Meyer-Bäse,et al. Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis , 2004, IEEE Transactions on Information Technology in Biomedicine.
[54] Sushmita Mitra,et al. Genetic Networks and Soft Computing , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[55] Olivier Gevaert,et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. , 2012, Cancer research.
[56] Annette Sterr,et al. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.
[57] Leyun Pan,et al. Fusion of Positron Emission Tomography (PET) and Gene Array Data: A New Approach for the Correlative Analysis of Molecular Biological and Clinical Data , 2007, IEEE Transactions on Medical Imaging.
[58] F. Jolesz,et al. Correction: Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2012, PLoS ONE.
[59] Aura Conci,et al. Breast thermography from an image processing viewpoint: A survey , 2013, Signal Process..
[60] J. Jakić-Razumović,et al. Morphometry of tumor cells in different grades and types of breast cancer. , 2010, Collegium antropologicum.
[61] J M Hoffman,et al. Molecular imaging in cancer: future directions and goals of the National Cancer Institute. , 2000, Academic radiology.
[62] Kenji Suzuki. A review of computer-aided diagnosis in thoracic and colonic imaging. , 2012, Quantitative imaging in medicine and surgery.
[63] A. Jemal,et al. Global Cancer Statistics , 2011 .
[64] Ferenc A. Jolesz,et al. Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme , 2011, PloS one.
[65] Balaji Ganeshan,et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.