| Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting |
75 |
| Post-contrast acute kidney injury - Part 1: Definition, clinical features, incidence, role of contrast medium and risk factors |
62 |
| Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma |
49 |
| The evolution of image reconstruction for CTfrom filtered back projection to artificial intelligence |
48 |
| Post-contrast acute kidney injury. Part 2: risk stratification, role of hydration and other prophylactic measures, patients taking metformin and chronic dialysis patients |
46 |
| Clinical indications for musculoskeletal ultrasound updated in 2017 by European Society of Musculoskeletal Radiology (ESSR) consensus |
35 |
| Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT |
34 |
| Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs |
32 |
| Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI |
31 |
| Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer |
29 |
| Retrospective validation of a new diagnostic criterion for hepatocellular carcinoma on gadoxetic acid-enhanced MRI: can hypointensity on the hepatobiliary phase be used as an alternative to washout with the aid of ancillary features? |
27 |
| Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade |
27 |
| Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer |
27 |
| Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center |
26 |
| Prostate artery embolisation for benign prostatic hyperplasia: a systematic review and meta-analysis |
26 |
| CT texture analysis of pancreatic cancer |
26 |
| Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer? |
26 |
| Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice |
26 |
| Endometrial Cancer MRI staging: Updated Guidelines of the European Society of Urogenital Radiology |
25 |
| Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators |
24 |
| Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI |
24 |
| Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study |
23 |
| Medical students' attitude towards artificial intelligence: a multicentre survey |
23 |
| Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging |
23 |
| Subjective and objective comparisons of image quality between ultra-high-resolution CT and conventional area detector CT in phantoms and cadaveric human lungs |
22 |
| Deep learning-based image restoration algorithm for coronary CT angiography |
22 |
| Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling |
21 |
| Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study |
21 |
| Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months |
20 |
| The performance of 3D ABUS versus HHUS in the visualisation and BI-RADS characterisation of breast lesions in a large cohort of 886 women |
20 |
| Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI |
19 |
| Combined hepatocellular cholangiocarcinoma: LI-RADS v2017 categorisation for differential diagnosis and prognostication on gadoxetic acid-enhanced MR imaging |
19 |
| Left ventricular global myocardial strain assessment comparing the reproducibility of four commercially available CMR-feature tracking algorithms |
19 |
| The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules |
19 |
| Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule |
18 |
| Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT |
18 |
| A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma |
18 |
| Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas |
18 |
| Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study |
18 |
| Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC) |
18 |
| Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma |
18 |
| Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach |
17 |
| Magnetic resonance neurography: current perspectives and literature review |
17 |
| Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective |
17 |
| Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists |
17 |
| Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer |
17 |
| Can histogram analysis of MR images predict aggressiveness in pancreatic neuroendocrine tumors? |
17 |
| Gadolinium retention after administration of contrast agents based on linear chelators and the recommendations of the European Medicines Agency |
17 |
| A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images |
17 |
| A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice |
16 |
