OOIR: Observatory of International Research

Papers

(The H4-Index of European Radiology is 54. 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 2020-05-01 to 2024-05-01.)

Article	Citations
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)	519
Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis	482
Artificial intelligence in radiology: 100 commercially available products and their scientific evidence	190
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	186
CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients	152
Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives	141
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images	136
Chest CT for detecting COVID-19: a systematic review and meta-analysis of diagnostic accuracy	132
Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors	128
The sensitivity and specificity of chest CT in the diagnosis of COVID-19	117
Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform	115
Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm	107
How can we combat multicenter variability in MR radiomics? Validation of a correction procedure	96
To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)	93
An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude	90
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation	88
Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis	85
Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms	82
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
From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans	77
Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning–based radiomics	77
Imaging features and evolution on CT in 100 COVID-19 pneumonia patients in Wuhan, China	75
A decade of radiomics research: are images really data or just patterns in the noise?	74

COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia	73
Utility of sonoelastography for the evaluation of rotator cuff tendon and pertinent disorders: a systematic review and meta-analysis	73
Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm	72
CT iterative vs deep learning reconstruction: comparison of noise and sharpness	71
Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning	71
Identifying normal mammograms in a large screening population using artificial intelligence	71
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	69
Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses	68
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	68
Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers	68
Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment plan	67
Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis	66
Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of tumor response in hepatocellular carcinoma after DEB-TACE	65
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network	62
Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI	61
Automated quantification of COVID-19 severity and progression using chest CT images	61
Pancreas image mining: a systematic review of radiomics	59
Chest CT practice and protocols for COVID-19 from radiation dose management perspective	58
Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI	58
Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE	57
Long-term outcomes of radiofrequency ablation for unifocal low-risk papillary thyroid microcarcinoma: a large cohort study of 414 patients	57
Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks	56
The Lisbon Agreement on Femoroacetabular Impingement Imaging—part 1: overview	56
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	55
Interpretation of CT signs of 2019 novel coronavirus (COVID-19) pneumonia	55
Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study	55
Preoperative sarcopenia is associated with poor overall survival in pancreatic cancer patients following pancreaticoduodenectomy	55
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	54
Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study	54