AIMC Journal:
European radiology

Showing 561 to 570 of 646 articles

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

European radiology Jan 17, 2020
OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.
Female Aged Aged, 80 and over Adult Stomach Neoplasms Humans Prognosis Retrospective Studies Tomography, X-Ray Computed Adenocarcinoma Lymphatic Metastasis Neoplasm Staging Male Middle Aged Deep Learning Lymph Nodes Nomograms
View on PubMed DOI

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.

European radiology Jan 3, 2020
OBJECTIVES: We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analy...
Humans Artificial Intelligence Ultrasonography ROC Curve Liver Neoplasms Carcinoma, Hepatocellular Contrast Media Chemoembolization, Therapeutic Young Adult Adult Male Middle Aged Adolescent Retrospective Studies Female Aged Aged, 80 and over
View on PubMed DOI

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

European radiology Jan 3, 2020
OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial n...
Multiple Sclerosis Humans Neural Networks, Computer Brain Reproducibility of Results Female Adult Male Middle Aged Magnetic Resonance Imaging
View on PubMed DOI

Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.

European radiology Jan 3, 2020
OBJECTIVES: To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations,...
Humans Algorithms Retrospective Studies Female Lung Neoplasms Reproducibility of Results Multiple Pulmonary Nodules Aged Radiographic Image Interpretation, Computer-Assisted Male Pneumonectomy Middle Aged Radiography, Thoracic Deep Learning
View on PubMed DOI

Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model.

European radiology Dec 11, 2019
OBJECTIVE: To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively.
Middle Aged Ultrasonography Magnetic Resonance Imaging Rectal Neoplasms Preoperative Period Female Aged Adult Male Humans Neural Networks, Computer Extranodal Extension Machine Learning Prospective Studies
View on PubMed DOI

Radiomics allows for detection of benign and malignant histopathology in patients with metastatic testicular germ cell tumors prior to post-chemotherapy retroperitoneal lymph node dissection.

European radiology Dec 11, 2019
OBJECTIVES: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs) after post-chemotherapy LN dissection (pcRPLND) in patients with metastatic non-seminomatous testic...
Orchiectomy Computational Biology Reproducibility of Results Lymph Node Excision Lymph Nodes Young Adult Neoplasm Staging Lymphatic Metastasis Adult Retroperitoneal Space Testicular Neoplasms Male Neoplasms, Germ Cell and Embryonal Humans Middle Aged Machine Learning Retrospective Studies Tomography, X-Ray Computed
View on PubMed DOI

Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.

European radiology Dec 10, 2019
OBJECTIVES: To evaluate the diagnostic performance of a deep learning algorithm for automated detection of small F-FDG-avid pulmonary nodules in PET scans, and to assess whether novel block sequential regularized expectation maximization (BSREM) reco...
Humans Artificial Intelligence Algorithms Retrospective Studies Female Aged Solitary Pulmonary Nodule Middle Aged Aged, 80 and over Positron Emission Tomography Computed Tomography Deep Learning Adult Radiopharmaceuticals Image Processing, Computer-Assisted Sensitivity and Specificity Fluorodeoxyglucose F18 Lung Neoplasms Male Multiple Pulmonary Nodules
View on PubMed DOI

Deep learning: definition and perspectives for thoracic imaging.

European radiology Dec 6, 2019
Relevance and penetration of machine learning in clinical practice is a recent phenomenon with multiple applications being currently under development. Deep learning-and especially convolutional neural networks (CNNs)-is a subset of machine learning,...
Neural Networks, Computer Machine Learning Algorithms Deep Learning Radiology Radiography, Thoracic Humans
View on PubMed DOI

A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.

European radiology Dec 6, 2019
OBJECTIVE: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme wi...
Adenocarcinoma in Situ Deep Learning Adolescent ROC Curve Image Processing, Computer-Assisted Lung Neoplasms Solitary Pulmonary Nodule Neoplasm Invasiveness Radiologists Feasibility Studies Adenocarcinoma of Lung Young Adult Adult Male Middle Aged Disease Progression Tomography, X-Ray Computed Humans Female Neural Networks, Computer Aged Artificial Intelligence Aged, 80 and over Retrospective Studies
View on PubMed DOI

Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study.

European radiology Nov 26, 2019
OBJECTIVES: To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.
Phantoms, Imaging Tomography, X-Ray Computed Xanthine Urinary Calculi Humans Tomography Scanners, X-Ray Computed Uric Acid Neural Networks, Computer Calcium Oxalate Calcium Phosphates Machine Learning Struvite Cysteine Algorithms Kidney Calculi
View on PubMed DOI
Popular Topics
Popular Journals