x-ray computed tomography

Latest posts

Predicting microvascular invasion in small (≤ 5 cm) HCC using radiomics-based peritumoral analysis

Insights into Imaging September 2024

This study assessed the predictive capacity of CT-enhanced radiomics models when determining microvascular invasion (MVI) for isolated hepatocellular carcinoma (HCC). The radiomics model was shown to be a promising noninvasive biomarker for preoperatively predicting MVI in individuals with a solitary HCC ≤ 5 cm and has applications in shaping personalized treatment policies. Key points: Article: Predicting microvascular invasion in small (≤ 5 cm)

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

European Radiology January 2024

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Evaluating the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

Fides Regina Schwartz September 2023

In the ever-evolving landscape of radiology, the quest for enhanced image quality and reduced noise, particularly in obese patients, remains an enduring challenge. It is within this context that a novel algorithm for noise reduction in dual-source dual-energy (DE) CT imaging is a promising development. In a retrospective study involving seventy-nine patients with contrast-enhanced abdominal imaging, this novel algorithm was

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

European Radiology Experimental September 2023

This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used was trained and externally evaluated on open-source, multi-centre retrospective data that contained radiologist-annotated non-contrast CT head studies. The authors concluded that the DL model has applications in a triage role with the potential to improve diagnostic yield

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

European Radiology Experimental August 2023

The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT. LungQuant was evaluated by comparing its results with independent visual evaluations by a group of clinical experts. The results indicated that an automatic quantification tool may be beneficial and contribute to an improved clinical

Robustness of pulmonary nodule radiomic features on CT as a function of varying radiation dose levels

European Radiology August 2023

The aim of this study was to present an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. The authors found that a large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations and determined that a lower radiation dose introduces increasingly random noise and bias to radiomic

Enhanced CT-based radiomics predicts pathological complete response for advanced adenocarcinoma

Insights into Imaging April 2023

This study shows that the combination of CT imaging and clinical factors pre-neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG) could help stratify potential responsiveness to NAC, which can also result in helping to provide a basis for clinicians to develop more customized treatment plans for patients. Key points Radiomics method can predict that AEG patients can

Using parametric feature maps to enhance the stability of CT radiomics

Laura Jensen January 2023

It is known that the reproducibility of radiomic features is influenced by myriad factors, one of which is the size of the segmented volume. We hypothesized that parametric maps calculated with a fixed voxel size could address this issue. To test the hypothesis, we conducted a phantom study and could show that the stability across different volumes of interest sizes

The role of radiomics in the detection of lymph node metastases

European Radiology Experimental December 2022

The authors of this retrospective analysis looked at the role that radiomics played when applied to contrast-enhanced computed tomography (CT) in detecting lymph node (LN) metastases in lung cancer patients compared to 18F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference. The authors determined that radiomics showed good discrimination power, regardless of the modelling technique, in detecting LN metastases in lung

The potential of texture analysis for breast density classification

European Radiology Experimental August 2022

Breast cancer continues to be the most commonly diagnosed cancer among women with over 2 million new cases per year worldwide. One important independent risk factor for developing breast cancer is breast density (BD). Epidemiological studies show that women with dense tissue may have an increased risk of developing breast cancer by 2-6 times when compared to women with less

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

European Radiology Experimental September 2023

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

Latest posts

Predicting microvascular invasion in small (≤ 5 cm) HCC using radiomics-based peritumoral analysis

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

Evaluating the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

Robustness of pulmonary nodule radiomic features on CT as a function of varying radiation dose levels

Enhanced CT-based radiomics predicts pathological complete response for advanced adenocarcinoma

Using parametric feature maps to enhance the stability of CT radiomics

The role of radiomics in the detection of lymph node metastases

The potential of texture analysis for breast density classification

Predicting microvascular invasion in small (≤ 5 cm) HCC using radiomics-based peritumoral analysis

External validation, radiological evaluation, and development of DL lung segmentation in chest CT

Evaluating the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

Robustness of pulmonary nodule radiomic features on CT as a function of varying radiation dose levels

Enhanced CT-based radiomics predicts pathological complete response for advanced adenocarcinoma

Using parametric feature maps to enhance the stability of CT radiomics

The role of radiomics in the detection of lymph node metastases

The potential of texture analysis for breast density classification

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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