x-ray computed tomography

Latest posts

Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

Insights into Imaging May 2022

In this retrospective study, the authors investigated if radiomics features extracted from nephrographic-phase (NP) CT images combined with clinicoradiological characteristics may have the potential to preoperatively differentiate between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs). They were able to demonstrate that radiomics analysis may be used as a potentially noninvasive method for distinguishing low- from high-grade

Radiomic nomogram predicts pathology invasiveness

European Radiology March 2022

The purpose of this retrospective diagnostic study was to develop and validate a preoperative CT-based nomogram combined with radiomic and clinical-radiological signatures to distinguish preinvasive lesions from pulmonary invasive lesions. The authors were able to demonstrate that a nomogram constructed by identified clinical-radiological signatures and combined radiomic signatures has the potential to precisely predict pathology invasiveness. Key points The radiomic

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

European Radiology November 2021

The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning

European Radiology October 2021

The purpose of this study was to evaluate the performance of deep learning using ResNet50 in the differentiation of benign and malignant vertebral fracture on computed tomography (CT). The study used a dataset of 433 patients, which was retrospectively selected from the authors’ spinal CT image database. The authors concluded that ResNet50 achieved good accuracy, which can be further improved

Practice makes perfect: Using AR to simulate CT guided procedures

Dimitri Amiras August 2021

As a junior radiology trainee, when I was not in the reporting room or on the wards doing portable ultrasounds, I found learning CT procedures a daunting task for the first time on a patient. This Halsted method of training, whilst effective, is expensive, takes a long time to achieve competency and can be a stressful experience. Simulation training, for

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

So Yeon Kim July 2021

Of late, deep learning-based algorithms have been successfully applied to various medical imaging modalities, ranging from chest radiographs to head CT scans. Compared to other body parts, there is a paucity of data regarding the application of deep learning-based algorithms in the liver. This can be attributed to the following reasons: First, unlike other body parts usually relying on single-phase

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia

European Radiology June 2021

The authors of this study aimed to develop and validate a radiomics nomogram for the prompt prediction of severe COVID-19 pneumonia. This was done through the retrospective collection of 316 COVID-19 patients (246 non-severe and 70 severe cases), which were allocated to training, validation, and testing cohorts. The authors found that the CT-based radiomics signature showed favourable predictive efficacy for

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

European Radiology May 2021

In this study, the authors explored the application of deep learning in patients with primary osteoporosis. Furthermore, they aimed to develop a fully automatic method based on a deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. The authors were able to determine that a deep learning-based method could achieve full

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

European Radiology April 2021

The authors of this study aimed to evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). They were able to determine that low iodine concentration DECT, when combined with deep learning in pediatric abdominal CT, can maintain

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Raquel Perez-Lopez February 2021

Medical imaging encodes information of underlying tissues and can provide a comprehensive view of the entire body repeatedly throughout the course of disease. Therefore, medical imaging has been the foundation of disease detection and follow-up in clinical practice for decades. However, to date, observer-dependent evaluation of medical imaging has been a constraint for developing imaging biomarkers towards precision medicine. The

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Raquel Perez-Lopez February 2021

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

Latest posts

Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

Radiomic nomogram predicts pathology invasiveness

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning

Practice makes perfect: Using AR to simulate CT guided procedures

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

Can machine learning predict the WHO/ISUP nuclear grade of clear cell renal cell carcinomas?

Radiomic nomogram predicts pathology invasiveness

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning

Practice makes perfect: Using AR to simulate CT guided procedures

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

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x-ray computed tomography

Welcome to the blog on Artificial Intelligence ofthe European Society of Radiology

Latest posts

Become A Member Today!

Check out our different membership options.

Membership

Free membership

ESR Friends

Footnotes:

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology