OOIR: Observatory of International Research

Papers

(The H4-Index of European Radiology is 56. 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 2020-03-01 to 2024-03-01.)

Article	Citations
Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review	823
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)	495
Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis	469
CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)	437
COVID-19 patients and the radiology department – advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI)	259
The role of imaging in 2019 novel coronavirus pneumonia (COVID-19)	185
ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists’ trainin	175
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence	173
CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients	148
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives	133
Chest CT for detecting COVID-19: a systematic review and meta-analysis of diagnostic accuracy	131
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images	130
Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors	120
The sensitivity and specificity of chest CT in the diagnosis of COVID-19	114
CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city	114
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform	110
Coronavirus disease 2019: initial chest CT findings	109
Coronavirus disease 2019 (COVID-19) imaging reporting and data system (COVID-RADS) and common lexicon: a proposal based on the imaging data of 37 studies	104
Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm	97
A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study	93
How can we combat multicenter variability in MR radiomics? Validation of a correction procedure	91
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)	86
An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude	85
Recommendation of low-dose CT in the detection and management of COVID-2019	84
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation	84

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis	81
Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms	81
Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients	81
Chest X-ray for predicting mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department	80
Acute pulmonary embolism in non-hospitalized COVID-19 patients referred to CTPA by emergency department	79
CT features of novel coronavirus pneumonia (COVID-19) in children	77
From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans	77
Imaging features and evolution on CT in 100 COVID-19 pneumonia patients in Wuhan, China	75
Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia	75
Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics	72
A decade of radiomics research: are images really data or just patterns in the noise?	71
COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia	71
Utility of sonoelastography for the evaluation of rotator cuff tendon and pertinent disorders: a systematic review and meta-analysis	70
Identifying normal mammograms in a large screening population using artificial intelligence	69
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning	69
Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm	68
CT iterative vs deep learning reconstruction: comparison of noise and sharpness	67
Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvan	66
Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses	66
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	66
Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction	65
Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment plan	65
Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis	64
Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers	62
Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy	61
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network	61
Automated quantification of COVID-19 severity and progression using chest CT images	61
Use of Vesical Imaging-Reporting and Data System (VI-RADS) for detecting the muscle invasion of bladder cancer: a diagnostic meta-analysis	60
Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE	58
Chest CT practice and protocols for COVID-19 from radiation dose management perspective	57
Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI	57
Long-term outcomes of radiofrequency ablation for unifocal low-risk papillary thyroid microcarcinoma: a large cohort study of 414 patients	56