OOIR: Observatory of International Research

Papers

(The H4-Index of European Radiology is 43. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-11-01 to 2024-11-01.)

Article	Citations
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)	537
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence	228
Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm	121
An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude	109
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)	102
Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018	84
Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI	72
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning	72
Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE	71
Introducing the Node Reporting and Data System 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer	67
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study	66
Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers	65
Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge	62
Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients	61
Pancreas image mining: a systematic review of radiomics	60
Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics	59
Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review	59
Volumetric assessment of the periablational safety margin after thermal ablation of colorectal liver metastases	59
An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education	56
Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA	55
MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland	51
Which role for chest x-ray score in predicting the outcome in COVID-19 pneumonia?	51
Can artificial intelligence reduce the interval cancer rate in mammography screening?	50
Radiomics signature of brain metastasis: prediction of EGFR mutation status	50
Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions	50

Percutaneous microwave ablation of bone tumors: a systematic review	49
Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study	47
CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma	47
Pulmonary embolism in patients with COVID-19 and value of D-dimer assessment: a meta-analysis	47
MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance	47
Deep learning–based metal artefact reduction in PET/CT imaging	47
Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy	47
Prospective comparison of the diagnostic accuracy of 18F-FDG PET/MRI, MRI, CT, and bone scintigraphy for the detection of bone metastases in the initial staging of primary breast cancer patients	47
Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study	46
Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma	45
Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer	45
Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data	45
MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study	44
COVID-19 classification of X-ray images using deep neural networks	44
Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer	44
Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms	44
Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms	44
CT-like images based on T1 spoiled gradient-echo and ultra-short echo time MRI sequences for the assessment of vertebral fractures and degenerative bone changes of the spine	43
CT diagnostic reference levels based on clinical indications: results of a large-scale European survey	43
Structured reporting in radiology: a systematic review to explore its potential	43