Related references
Note: Only part of the references are listed.Three-Dimensional Texture Feature Analysis of Pulmonary Nodules in CT Images: Lung Cancer Predictive Models Based on Support Vector Machine Classifier
Ni Gao et al.
JOURNAL OF DIGITAL IMAGING (2020)
Experimental Multicenter and Multivendor Evaluation of the Performance of PET Radiomic Features Using 3-Dimensionally Printed Phantom Inserts
Elisabeth Pfaehler et al.
JOURNAL OF NUCLEAR MEDICINE (2020)
CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review
Mathilde Espinasse et al.
DIAGNOSTICS (2020)
Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features
Jianping Zhang et al.
NUCLEAR MEDICINE COMMUNICATIONS (2020)
A Role for FDG PET Radiomics in Personalized Medicine?
Gary J. R. Cook et al.
SEMINARS IN NUCLEAR MEDICINE (2020)
Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation
Barbara Palumbo et al.
DIAGNOSTICS (2020)
Radioguided lung lesion localization: introducing a fluoroscopy system in a SPECT/CT scan
Rexhep Durmo et al.
NUCLEAR MEDICINE COMMUNICATIONS (2019)
Management of the solitary pulmonary nodule
Faria Nasim et al.
CURRENT OPINION IN PULMONARY MEDICINE (2019)
Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study
Wei Wu et al.
EUROPEAN RADIOLOGY (2019)
Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis
Alex Zwanenburg
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)
Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules
Yoganand Balagurunathan et al.
SCIENTIFIC REPORTS (2019)
Image quality evaluation in a modern PET system: impact of new reconstructions methods and a radiomics approach
Gabriel Reynes-Llompart et al.
SCIENTIFIC REPORTS (2019)
Using neighborhood gray tone difference matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules
Song Chen et al.
CANCER IMAGING (2019)
Malignant-benign classification of pulmonary nodules based on random forest aided by clustering analysis
Wenhao Wu et al.
PHYSICS IN MEDICINE AND BIOLOGY (2019)
Radiomics in Oncological PET/CT: a Methodological Overview
Seunggyun Ha et al.
NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)
Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture
Jose Raniery Ferreira Jr et al.
JOURNAL OF DIGITAL IMAGING (2018)
LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity
Christophe Nioche et al.
CANCER RESEARCH (2018)
Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters
Roberto Berenguer et al.
RADIOLOGY (2018)
The Value of F-18-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
Ling Wang et al.
BIOMED RESEARCH INTERNATIONAL (2018)
Repeatability of SUV in Oncologic 18F-FDG PET
Martin A. Lodge
JOURNAL OF NUCLEAR MEDICINE (2017)
Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017
Heber MacMahon et al.
RADIOLOGY (2017)
Diagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions
Song Chen et al.
SCIENTIFIC REPORTS (2017)
PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology
M. Sollini et al.
SCIENTIFIC REPORTS (2017)
Management of the Solitary Pulmonary Nodule
Edward Y. Chan et al.
ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2017)
Radiomics of pulmonary nodules and lung cancer
Ryan Wilson et al.
TRANSLATIONAL LUNG CANCER RESEARCH (2017)
A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images
Ashis Kumar Dhara et al.
JOURNAL OF DIGITAL IMAGING (2016)
Predicting Malignant Nodules from Screening CT Scans
Samuel Hawkins et al.
JOURNAL OF THORACIC ONCOLOGY (2016)
Recent Trends in the Identification of Incidental Pulmonary Nodules
Michael K. Gould et al.
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2015)
The role of 18F-FDG PET or 18F-FDG-PET/CT in the evaluation of solitary pulmonary nodules
Wenbo Li et al.
EUROPEAN JOURNAL OF RADIOLOGY (2015)
FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
Ronald Boellaard et al.
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2015)
FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules
Kenta Miwa et al.
EUROPEAN JOURNAL OF RADIOLOGY (2014)
Reproducibility of semi-quantitative parameters in FDG-PET using two different PET scanners: Influence of attenuation correction method and examination interval
Tomohito Kamibayashi et al.
MOLECULAR IMAGING AND BIOLOGY (2008)
The solitary pulmonary nodule
D Ost et al.
NEW ENGLAND JOURNAL OF MEDICINE (2003)