Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

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Radiomics for classification of bone mineral loss: A machine learning study

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Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm

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Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

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Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

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Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia

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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

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CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study

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Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future

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Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

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Radiomics analysis using stability selection supervised component analysis for right-censored survival data

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Automated detection of COVID-19 cases using deep neural networks with X-ray images

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Evaluation of the relationship between inpatient COVID-19 mortality and chest CT severity score

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Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial

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MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas

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COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

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Predictive value of initial CT scan for various adverse outcomes in patients with COVID-19 pneumonia

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A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

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Integrative analysis for COVID-19 patient outcome prediction

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Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review

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Clinical characteristics, outcomes, and risk factors for mortality in hospitalized patients with COVID-19 and cancer history: a propensity score-matched study

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Lightweight deep learning models for detecting COVID-19 from chest X-ray images

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A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients

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Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma

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CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm

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Noninvasive O6 Methylguanine-DNA Methyltransferase Status Prediction in Glioblastoma Multiforme Cancer Using Magnetic Resonance Imaging Radiomics Features: Univariate and Multivariate Radiogenomics Analysis

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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children

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Automatic segmentation of the uterus on MRI using a convolutional neural network

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Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury

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Machine learning based brain tumour segmentation on limited data using local texture and abnormality

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Survey on deep learning for radiotherapy

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Radiomics: the bridge between medical imaging and personalized medicine

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Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images

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Applications and limitations of radiomics

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Radiomics: Images Are More than Pictures, They Are Data

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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

(2015) G. S. Collins et al.   BMJ-British Medical Journal

Machine Learning methods for Quantitative Radiomic Biomarkers

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

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Radiomics: the process and the challenges

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