Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study

Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

(2020) Sung Mo Ryu et al.   BMC Medical Informatics and Decision Making

MRI radiomics-based machine-learning classification of bone chondrosarcoma

(2020) Salvatore Gitto et al.   EUROPEAN JOURNAL OF RADIOLOGY

Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results

(2019) Arnaldo Stanzione et al.   ACADEMIC RADIOLOGY

Predictors of in-hospital length of stay among cardiac patients: A machine learning approach

(2019) Tahani A. Daghistani et al.   INTERNATIONAL JOURNAL OF CARDIOLOGY

Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study

(2019) Quyen N. Do et al.   PLoS One

Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI

(2019) Ning Lang et al.   MAGNETIC RESONANCE IMAGING

Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa

(2019) Valeria Romeo et al.   MAGNETIC RESONANCE IMAGING

Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines

(2019) Eo-Jin Hwang et al.   Scientific Reports

Repeatability of Multiparametric Prostate MRI Radiomics Features

(2019) Michael Schwier et al.   Scientific Reports

Deep Learning in Neuroradiology

(2018) G. Zaharchuk et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach

(2018) Valeria Romeo et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment?

(2018) Massimo Imbriaco et al.   RADIOLOGY

Differential diagnostic value of 18F-FDG PET/CT for benign and malignant vertebral compression fractures: comparison with magnetic resonance imaging

(2018) Xiaojiang He et al.   Cancer Management and Research

Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study

(2018) Laura Filograna et al.   Radiologia Medica

Diagnostic Outcome of Image-Guided Percutaneous Core Needle Biopsy of Sclerotic Bone Lesions: A Meta-Analysis

(2018) Chong Hyun Suh et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

The value of MR textural analysis in prostate cancer

(2018) N. Patel et al.   CLINICAL RADIOLOGY

Solid bone tumors of the spine: Diagnostic performance of apparent diffusion coefficient measured using diffusion-weighted MRI using histology as a reference standard

(2017) Grazia Pozzi et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Diffusion-weighted imaging for the cellularity assessment and matrix characterization of soft tissue tumour

(2017) Tiziana Robba et al.   Radiologia Medica

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

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

(2015) Ralph T.H. Leijenaar et al.   Scientific Reports

An improved multiclass LogitBoost using adaptive-one-vs-one

(2014) Peng Sun et al.   MACHINE LEARNING

3D Slicer as an image computing platform for the Quantitative Imaging Network

(2012) Andriy Fedorov et al.   MAGNETIC RESONANCE IMAGING

Diagnosis of bone metastases: a meta-analysis comparing 18FDG PET, CT, MRI and bone scintigraphy

(2011) Hui-Lin Yang et al.   EUROPEAN RADIOLOGY

Diagnostic Imaging of Solitary Tumors of the Spine: What to Do and Say

(2008) Mathieu H. Rodallec et al.   RADIOGRAPHICS