| Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients |
88 |
| Current Applications and Future Impact of Machine Learning in Radiology |
85 |
| Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters |
83 |
| Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction |
83 |
| Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry |
57 |
| Gadolinium Retention: A Research Roadmap from the 2018 NIH/ACR/RSNA Workshop on Gadolinium Chelates |
56 |
| A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction |
55 |
| Photon-counting CT: Technical Principles and Clinical Prospects |
55 |
| Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs |
54 |
| Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study |
52 |
| Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs |
52 |
| Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide |
50 |
| Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging |
49 |
| MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy |
46 |
| A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using F-18-FDG PET of the Brain |
46 |
| Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System |
43 |
| A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop |
42 |
| Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review |
40 |
| Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment |
40 |
| Quantitative Elastography Methods in Liver Disease: Current Evidence and Future Directions |
40 |
| Guidelines for Acquisition, Interpretation, and Reporting of Whole-Body MRI in Myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS) |
39 |
| Validation of a Method to Compensate Multicenter Effects Affecting CT Radiomics |
38 |
| Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection |
38 |
| Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values |
37 |
| Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning |
37 |
| What Are We Missing? False-Negative Cancers at Multiparametric MR Imaging of the Prostate |
36 |
| PI-RADS Steering Committee: The PI-RADS Multiparametric MRI and MRI-directed Biopsy Pathway |
35 |
| The RSNA Pediatric Bone Age Machine Learning Challenge |
34 |
| Intra-articular Coricosteroid Injections in the Nip and Knee: Perhaps Not as Safe as We Thought? |
33 |
| Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis |
33 |
| Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas |
32 |
| Breast MRI: State of the Art |
32 |
| Consensus Recommendations for Evaluation, Interpretation, and Utilization of Computed Tomography and Magnetic Resonance Enterography in Patients With Small Bowel Crohn's Disease |
32 |
| Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Preciction |
32 |
| Reduction in Thyroid Nodule Biopsies and Improved Accuracy with American College of Radiology Thyroid Imaging Reporting and Data System |
31 |
| Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling |
31 |
| Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI |
31 |
| Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes |
31 |
| Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study |
31 |
| Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images |
30 |
| Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks |
30 |
| Cerebral Microbleeds: Imaging and Clinical Significance |
30 |
| Multiparametric MRI for Bladder Cancer: Validation of VI-RADS for the Detection of Detrusor Muscle Invasion |
30 |
| Deep Learning for Diagnosis of Chronic Myocardial Infarction on Nonenhanced Cardiac Cine MRI |
29 |
| One-year Retention of Gadolinium in the Brain: Comparison of Gadodiamide and Gadoterate Meglumine in a Rodent Model |
29 |
| Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial |
29 |
| Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment |
29 |
| A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI |
29 |
| Differentiation of Hepatocellular Carcinoma from Other Hepatic Malignancies in Patients at Risk: Diagnostic Performance of the Liver Imaging Reporting and Data Sytem Version 2014 |
29 |
| US Fine-Needle Aspiration Biopsy for Thyroid Malignancy: Diagnostic Performance of Seven Society Guidelines Applied to 2000 Thyroid Nodules |
29 |
