Related references
Note: Only part of the references are listed.Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer
Matthias Dietzel et al.
SCIENTIFIC REPORTS (2020)
Radiomics and radiogenomics of prostate cancer
Clayton P. Smith et al.
ABDOMINAL RADIOLOGY (2019)
Radiogenomics: bridging imaging and genomics
Zuhir Bodalal et al.
ABDOMINAL RADIOLOGY (2019)
State of the Art: Machine Learning Applications in Glioma Imaging
Eyal Lotan et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2019)
Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results
Doris Leithner et al.
BREAST CANCER RESEARCH (2019)
Background, Current Role, and Potential Applications of Radiogenomics
Katja Pinker et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2018)
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Ashirbani Saha et al.
BRITISH JOURNAL OF CANCER (2018)
Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors
Ashirbani Saha et al.
MEDICAL PHYSICS (2018)
Computational Radiomics System to Decode the Radiographic Phenotype
Joost J. M. van Griethuysen et al.
CANCER RESEARCH (2017)
Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
Bin Zhang et al.
CANCER LETTERS (2017)
Applications and limitations of radiomics
Stephen S. F. Yip et al.
PHYSICS IN MEDICINE AND BIOLOGY (2016)
Radiomic phenotype features predict pathological response in non-small cell lung cancer
Thibaud P. Coroller et al.
RADIOTHERAPY AND ONCOLOGY (2016)
Ki-67 as a prognostic marker according to breast cancer molecular subtype
Nahed A. Soliman et al.
CANCER BIOLOGY & MEDICINE (2016)
Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms
Maciej A. Mazurowski et al.
EUROPEAN JOURNAL OF RADIOLOGY (2015)
Radiomics of Multi-Parametric Breast MRI in Breast Cancer Diagnosis: A Quantitative Investigation of Diffusion Weighted Imaging, Dynamic Contrast-Enhanced, and T2-Weighted Magnetic Resonance Imaging
N. Maforo et al.
MEDICAL PHYSICS (2015)
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar et al.
FRONTIERS IN ONCOLOGY (2015)
Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
Olya Grove et al.
PLOS ONE (2015)
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
Wentian Guo et al.
JOURNAL OF MEDICAL IMAGING (2015)
Breast Cancer: Early Prediction of Response to Neoadjuvant Chemotherapy Using Parametric Response Maps for MR Imaging
Nariya Cho et al.
RADIOLOGY (2014)
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
Maciej A. Mazurowski et al.
RADIOLOGY (2014)
Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles
Ahmed Bilal Ashraf et al.
RADIOLOGY (2014)
St. Gallen 2013: Brief Preliminary Summary of the Consensus Discussion
Nadia Herbeck et al.
BREAST CARE (2013)
Radiomics: Extracting more information from medical images using advanced feature analysis
Philippe Lambin et al.
EUROPEAN JOURNAL OF CANCER (2012)
Combined Use of T2-Weighted MRI and T1-Weighted Dynamic Contrast-Enhanced MRI in the Automated Analysis of Breast Lesions
Neha Bhooshan et al.
MAGNETIC RESONANCE IN MEDICINE (2011)
Breast Cancer Management: Opportunities and Barriers to an Individualized Approach
Edith A. Perez
ONCOLOGIST (2011)
Application of artificial neural networks for the prediction of lymph node metastases to the ipsilateral axilla - initial experience in 194 patients using magnetic resonance mammography
Matthias Dietzel et al.
ACTA RADIOLOGICA (2010)
Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer
Pascal A. T. Baltzer et al.
EUROPEAN RADIOLOGY (2010)
Texture-Based Classification of Focal Liver Lesions on MRI at 3.0 Tesla: A Feasibility Study in Cysts and Hemangiomas
Marius E. Mayerhoefer et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2010)
Quantitative Analysis of Lesion Morphology and Texture Features for Diagnostic Prediction in Breast MRI
Ke Nie et al.
ACADEMIC RADIOLOGY (2008)
Breast cancer: origins and evolution
Kornelia Polyak
JOURNAL OF CLINICAL INVESTIGATION (2007)
Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study
Lisa A. Carey et al.
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2006)
A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images
WJ Chen et al.
ACADEMIC RADIOLOGY (2006)