Related references
Note: Only part of the references are listed.Radiomics Nomogram for Differentiating Between Benign and Malignant Soft-Tissue Masses of the Extremities
Hexiang Wang et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2020)
Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study
Zhenyu Liu et al.
CLINICAL CANCER RESEARCH (2019)
Validation of a Method to Compensate Multicenter Effects Affecting CT Radiomics
Fanny Orlhac et al.
RADIOLOGY (2019)
Radiomic analysis for preoperative prediction of cervical lymph node metastasis in patients with papillary thyroid carcinoma
Wei Lu et al.
EUROPEAN JOURNAL OF RADIOLOGY (2019)
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
Yae Won Park et al.
EUROPEAN RADIOLOGY (2019)
Harmonization of cortical thickness measurements across scanners and sites
Jean-Philippe Fortin et al.
NEUROIMAGE (2018)
Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
Muhammad Shafiq-ul-Hassan et al.
MEDICAL PHYSICS (2017)
Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
Bin Zhang et al.
CANCER LETTERS (2017)
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
E. J. Limkin et al.
ANNALS OF ONCOLOGY (2017)
Identifying prognostic intratumor heterogeneity using pre-and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients
Yong Yue et al.
JOURNAL OF GASTROINTESTINAL ONCOLOGY (2017)
Radiogenomics - current status, challenges and future directions
Christian Nicolaj Andreassen et al.
CANCER LETTERS (2016)
Radiomics: Images Are More than Pictures, They Are Data
Robert J. Gillies et al.
RADIOLOGY (2016)
Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule
Lan He et al.
SCIENTIFIC REPORTS (2016)
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar et al.
FRONTIERS IN ONCOLOGY (2015)
Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities
Adrien Depeursinge et al.
MEDICAL IMAGE ANALYSIS (2014)
Can MR Imaging Be Used to Predict Tumor Grade in Soft-Tissue Sarcoma?
Fang Zhao et al.
RADIOLOGY (2014)
A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: A step toward individualized care and shared decision making
Cary Oberije et al.
RADIOTHERAPY AND ONCOLOGY (2014)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J. W. L. Aerts et al.
NATURE COMMUNICATIONS (2014)
MRI to differentiate benign from malignant soft-tissue tumours of the extremities: a simplified systematic imaging approach using depth, size and heterogeneity of signal intensity
W. J. Chung et al.
BRITISH JOURNAL OF RADIOLOGY (2012)
Soft tissue sarcomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up
P. G. Casali et al.
ANNALS OF ONCOLOGY (2010)
Machine Learning Study of Several Classifiers Trained With Texture Analysis Features to Differentiate Benign from Malignant Soft-Tissue Tumors in T1-MRI Images
Jaber Juntu et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2010)
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
Babak Mohammadzadeh Asl et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2008)
Soft tissue masses with cyst-like appearance on MR imaging: distinction of benign and malignant lesions
Srinivasan Harish et al.
EUROPEAN RADIOLOGY (2006)
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Paul A. Yushkevich et al.
NEUROIMAGE (2006)
Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data
J Gui et al.
BIOINFORMATICS (2005)
Accuracy of MRI in characterization of soft tissue tumors and tumor-like lesions. A prospective study in 548 patients
JLMA Gielen et al.
EUROPEAN RADIOLOGY (2004)
Influence of MRI acquisition protocols and image intensity normalization methods on texture classification
G Collewet et al.
MAGNETIC RESONANCE IMAGING (2004)
Textural analysis of contrast-enhanced MR images of the breast
P Gibbs et al.
MAGNETIC RESONANCE IN MEDICINE (2003)
Magnetic resonance imaging of soft tissue tumors
AM De Schepper et al.
EUROPEAN RADIOLOGY (2000)
Radiologic evaluation of soft-tissue masses: A current perspective
MJ Kransdorf et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2000)