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European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, 01/2017, Volume 44, Issue 1, pp. 151 - 165
After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has... 
Recommendations | Heterogeneity | Image texture | Critical review | PET/CT | Animals | Positron Emission Tomography Computed Tomography - trends | Imaging, Three-Dimensional - trends | Humans | Image Enhancement - methods | Evidence-Based Medicine | Forecasting | Tomography | Medical diagnosis | Cancer | Life Sciences | Bioengineering | Nuclear medicine
Journal Article
Journal Article
The Journal of Nuclear Medicine, ISSN 0161-5505, 05/2018, Volume 59, p. 640
Purpose: Radiomics is foreseen as an essential prognostic tool for cancer risk assessment. The radiomics signature described in Aerts et al. (Nat. Commun.... 
Phenotypes | Translation | Medical imaging | Lung cancer | Positron emission | Health risks | Patients | Wavelet | Cohort analysis | Computed tomography | Mathematical analysis | Medical prognosis | Risk assessment | Dependence | Tomography | Feature extraction | Head and neck cancer | Head & neck cancer | Pretreatment | Cancer | Tumors
Journal Article
Computerized Medical Imaging and Graphics, ISSN 0895-6111, 2015, Volume 46, pp. 300 - 301
Journal Article
European journal of nuclear medicine and molecular imaging, ISSN 1619-7070, 07/2019, pp. 1 - 8
Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent... 
Learning | Medicine | Nuclear medicine | Image segmentation | Learning algorithms | Medical imaging | Segmentation | Image processing | Noise reduction | Artificial intelligence | Image reconstruction
Journal Article
IEEE Transactions on Medical Imaging, ISSN 0278-0062, 06/2009, Volume 28, Issue 6, pp. 881 - 893
Journal Article
European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, 4/2011, Volume 38, Issue 4, pp. 663 - 672
Journal Article
European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, 7/2016, Volume 43, Issue 8, pp. 1453 - 1460
Journal Article
IEEE Transactions on Image Processing, ISSN 1057-7149, 06/2019, Volume 28, Issue 6, pp. 3075 - 3088
Journal Article
The Journal of Nuclear Medicine, ISSN 0161-5505, 05/2018, Volume 59, p. 287
Objectives: Radiomics is the high-throughput analysis of medical images for treatment individualization. It conventionally involves the quantification of... 
Medical imaging | Image processing | Medical treatment | Lung cancer | Standardization | Medical services | Benchmarks | Workflow | Radiation therapy | Computer programs | Interpolation | Digital imaging | International cooperation | Precision medicine | Computed tomography | Reproducibility | Biomarkers | Feature extraction | Software | Two dimensional analysis | Configurations | Tumors | Cancer
Journal Article
Scientific Reports, ISSN 2045-2322, 12/2019, Volume 9, Issue 1, pp. 1 - 11
Our aim was to evaluate the impact of the accuracy of image segmentation techniques on establishing an overlap between pre-treatment and post-treatment... 
Image processing | Computed tomography | Segmentation | Head and neck cancer | Radiation therapy | Bayesian analysis | Patients | Positron emission tomography | Tumors | Esophagus
Journal Article
Clinical and Translational Imaging, ISSN 2281-5872, 10/2018, Volume 6, Issue 5, pp. 379 - 391
To perform an updated review regarding the influence of methodological conditions on radiomics analyses of 18F-FDG PET imaging.We performed a systematic review... 
Heterogeneity | Medicine & Public Health | Diagnostic Radiology | Methodology | Nuclear Medicine | Medical and Radiation Physics | Interventional Radiology | Imaging / Radiology | Radiotherapy | Texture | PET | Radiomics
Journal Article
JOURNAL OF NUCLEAR MEDICINE, ISSN 0161-5505, 09/2019, Volume 60, Issue Suppl 2, pp. 38S - 44S
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and... 
FDG-PET | QUANTIFICATION | TEXTURE FEATURES | MODEL | machine learning | CANCER | PREDICTION | HETEROGENEITY | radiomics | deep learning | METHODOLOGY | SEGMENTATION | NEURAL-NETWORK | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Nuclear medicine | Image segmentation | Automation | Image processing | Segmentation | Data acquisition | Machine learning
Journal Article
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, ISSN 1619-7070, 01/2017, Volume 44, Issue 1, pp. 151 - 165
After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has... 
FDG-PET | THERAPY RESPONSE | Image texture | QUANTIFYING TUMOR HETEROGENEITY | CERVICAL-CANCER | Recommendations | Heterogeneity | F-18-FDG PET | GRADIENT-BASED METHOD | IMAGING FEATURES | PET/CT | QUANTITATIVE ASSESSMENT | PROGNOSTIC-SIGNIFICANCE | Critical review | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | INTRATUMORAL METABOLIC HETEROGENEITY
Journal Article
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