AIMC Journal:
European radiology

Showing 541 to 550 of 639 articles

Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.

European radiology Feb 17, 2020
PURPOSE: To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).
Nomograms Reproducibility of Results Support Vector Machine Aged Age Factors Magnetic Resonance Imaging Adult Neoplasm Invasiveness Humans Neoplasm Grading Uterine Cervical Neoplasms Algorithms Middle Aged Peritoneum Retrospective Studies Hysterectomy Female Informatics Diffusion Magnetic Resonance Imaging
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Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

European radiology Feb 17, 2020
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of th...
Reproducibility of Results Humans Information Dissemination Deep Learning Artificial Intelligence Radiology Radiologists Machine Learning Validation Studies as Topic Algorithms Forecasting
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Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning.

European radiology Feb 16, 2020
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
Computed Tomography Angiography Ventricular Function, Right Deep Learning Acute Disease Pulmonary Embolism Aged Heart Ventricles Humans Adult Retrospective Studies Sensitivity and Specificity Female Male Reproducibility of Results Middle Aged
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Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network.

European radiology Feb 14, 2020
OBJECTIVES: To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images.
Humans Osteoporosis Bone Density Abdomen Adult Tomography, X-Ray Computed Area Under Curve Retrospective Studies Female Male Lumbar Vertebrae Reproducibility of Results Middle Aged Absorptiometry, Photon Aged Deep Learning ROC Curve
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Assessment of knee pain from MR imaging using a convolutional Siamese network.

European radiology Feb 13, 2020
OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguis...
Severity of Illness Index Deep Learning Neural Networks, Computer Magnetic Resonance Imaging Humans Knee Joint Female Area Under Curve Aged Arthralgia Radiography Male Osteoarthritis, Knee Middle Aged Knee Synovitis
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Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

European radiology Feb 13, 2020
INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Body Surface Area Intra-Abdominal Fat Subcutaneous Fat Whole Body Imaging Disease Progression Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Muscle, Skeletal Positron Emission Tomography Computed Tomography Neoplasm Staging Body Composition Progression-Free Survival Proportional Hazards Models Aged Adult Humans Survival Rate Male Prognosis Middle Aged Female Reproducibility of Results
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CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists.

European radiology Feb 13, 2020
OBJECTIVES: To evaluate the deep learning models for differentiating invasive pulmonary adenocarcinomas (IACs) among subsolid nodules (SSNs) considered for resection in a retrospective diagnostic cohort in comparison with a size-based logistic model ...
Retrospective Studies Tomography, X-Ray Computed Female Aged Male Humans Radiologists Radiography, Thoracic Adenocarcinoma of Lung Middle Aged Deep Learning ROC Curve Lung Neoplasms Logistic Models
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Assessment of liver fibrosis severity using computed tomography-based liver and spleen volumetric indices in patients with chronic liver disease.

European radiology Feb 13, 2020
OBJECTIVES: To evaluate whether the liver and spleen volumetric indices, measured on portal venous phase CT images, could be used to assess liver fibrosis severity in chronic liver disease.
Humans Retrospective Studies Liver Severity of Illness Index Tomography, X-Ray Computed Case-Control Studies Female Deep Learning Adult Chronic Disease ROC Curve Male Organ Size Liver Cirrhosis Middle Aged Liver Diseases Spleen
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A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma.

European radiology Feb 5, 2020
OBJECTIVES: To establish a quantitative MR model that uses clinically relevant features of tumor location and tumor volume to differentiate lower grade glioma (LRGG, grades II and III) and glioblastoma (GBM, grade IV).
Humans Machine Learning Female Magnetic Resonance Imaging Adult Diagnosis, Differential Brain Neoplasms Glioblastoma Male Neoplasm Staging Glioma Tumor Burden Support Vector Machine
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Ruling out rotator cuff tear in shoulder radiograph series using deep learning: redefining the role of conventional radiograph.

European radiology Feb 5, 2020
OBJECTIVE: To develop a deep learning algorithm that can rule out significant rotator cuff tear based on conventional shoulder radiographs in patients suspected of rotator cuff tear.
Rotator Cuff Injuries Rotator Cuff Male Middle Aged Deep Learning Radiography Radiographic Image Interpretation, Computer-Assisted Humans Retrospective Studies Female Reproducibility of Results Sensitivity and Specificity
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