reproducibility of results

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

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully

Read More →

Unlocking the Potential of Radiomics: Ongoing Challenges Are Revolving Around Methodology and Reproducibility

Over a decade in the making, the novel concept of radiomics has been silently brewing, promising to reshape the landscape of personalised and precision medicine. So, why has radiomics not made its clinical debut yet, despite its innovative and logical approach? The answer lies in the intricate world of advanced computation that forms the basis of radiomics, which in essence

Read More →

Deep learning image reconstruction improves DECT image quality

The purpose of this phantom study was the compare the image quality of a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) as well as assess the impact that these algorithms have on radiomics robustness. The authors determined that the new DLIR algorithm does in fact improve the quality of DECT images

Read More →

Using parametric feature maps to enhance the stability of CT radiomics

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

Read More →

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

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

The goal of this study was to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. A majority of features were not reproducible (as defined by a concordance correlation coefficient of greater than 0.9) when comparing their values across consecutive CECTs obtained within a two week period.

Read More →

Beyond diagnosis: is there a role for radiomics in prostate cancer management?

At present, therapeutic and prognostic recommendations for prostate cancer (PCa) predominantly hinge on risk-stratification tools that are built upon clinical parameters. Recent evidence indicates that incorporating imaging can enhance the precision of prognostic models based on clinical factors. However, challenges like subjective interpretation, variability in image analysis, and the absence of reliable quantitative measures need to be overcome to fully

Read More →

Unlocking the Potential of Radiomics: Ongoing Challenges Are Revolving Around Methodology and Reproducibility

Over a decade in the making, the novel concept of radiomics has been silently brewing, promising to reshape the landscape of personalised and precision medicine. So, why has radiomics not made its clinical debut yet, despite its innovative and logical approach? The answer lies in the intricate world of advanced computation that forms the basis of radiomics, which in essence

Read More →

Deep learning image reconstruction improves DECT image quality

The purpose of this phantom study was the compare the image quality of a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) as well as assess the impact that these algorithms have on radiomics robustness. The authors determined that the new DLIR algorithm does in fact improve the quality of DECT images

Read More →

Using parametric feature maps to enhance the stability of CT radiomics

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

Read More →

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

The authors of this retrospective study performed test-retest reproducibility analyses for a deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs with short-term intervals, in order to analyze influential factors on test-retest variations. The test, which included patients with pulmonary nodules resected in 2017, showed that DLAD was robust to the test-retest variation. Key points The deep learning–based

Read More →

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

The goal of this study was to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans. A majority of features were not reproducible (as defined by a concordance correlation coefficient of greater than 0.9) when comparing their values across consecutive CECTs obtained within a two week period.

Read More →

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