Machine and deep learning methods for radiomics
Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …
extracted imaging information to clinical and biological endpoints. The development of …
Beyond imaging: the promise of radiomics
The domain of investigation of radiomics consists of large-scale radiological image analysis
and association with biological or clinical endpoints. The purpose of the present study is to …
and association with biological or clinical endpoints. The purpose of the present study is to …
Radiomics in prostate cancer: An up-to-date review
M Ferro, O de Cobelli, G Musi… - Therapeutic …, 2022 - journals.sagepub.com
Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male
population. The diagnosis, the identification of aggressive disease, and the post-treatment …
population. The diagnosis, the identification of aggressive disease, and the post-treatment …
Characterization of PET/CT images using texture analysis: the past, the present… any future?
After seminal papers over the period 2009–2011, the use of texture analysis of PET/CT
images for quantification of intratumour uptake heterogeneity has received increasing …
images for quantification of intratumour uptake heterogeneity has received increasing …
Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
Background The large volume and suboptimal image quality of portable chest X-rays
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …
(CXRs) as a result of the COVID-19 pandemic could post significant challenges for …
Radiomics-based prognosis analysis for non-small cell lung cancer
Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative
features from radiological images. Radiomic features have been shown to provide …
features from radiological images. Radiomic features have been shown to provide …
Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI
H Khanfari, S Mehranfar, M Cheki… - BMC Medical …, 2023 - Springer
Background The purpose of this study is to investigate the use of radiomics and deep
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …
Prostate cancer detection using deep convolutional neural networks
Prostate cancer is one of the most common forms of cancer and the third leading cause of
cancer death in North America. As an integrated part of computer-aided detection (CAD) …
cancer death in North America. As an integrated part of computer-aided detection (CAD) …
Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values
D Bonekamp, S Kohl, M Wiesenfarth, P Schelb… - Radiology, 2018 - pubs.rsna.org
Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean
apparent diffusion coefficient (ADC), and radiologist assessment for characterization of …
apparent diffusion coefficient (ADC), and radiologist assessment for characterization of …
The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer
B Ilhan, P Guneri, P Wilder-Smith - Oral oncology, 2021 - Elsevier
Oral cancer (OC) is the sixth most commonly reported malignant disease globally, with high
rates of disease-related morbidity and mortality due to advanced loco-regional stage at …
rates of disease-related morbidity and mortality due to advanced loco-regional stage at …