radiomics

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

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 →

The latest developments in radiomics and AI may help against prostate cancer

The authors of this systematic review explored the currently available literature on artificial intelligence (AI) and radiomics applied to molecular imaging of prostate cancer. Due to the great promise that nuclear medicine holds regarding improving the quality of life for prostate cancer patients, this study looks at the myriad areas in which AI and radiomics can positively be applied to

Read More →

The potential of texture analysis for breast density classification

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

Read More →

Radiomic features of breast parenchyma

The object of this study was to assess the similarities and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. The authors aimed to address the problem using an enlarged set of texture radiomic features, dense/non-dense areas comparison and a new manufacturer, concluding that texture features from FOR PROCESSING mammograms were the

Read More →

Can radiomics signatures predict tumor reponse of patients treated with chemotherapy and targeted therapy?

The authors of this retrospective study had the goal of evaluating the effectiveness of radiomics signatures in order to predict the tumor response of non-small cell lung cancer (NSCLC) patients who were treated with first-line chemotherapy, targeted therapy, or a combination of the two. The authors determined that radiomics signatures based on pre-treatment CT scans can accurately predict tumor response

Read More →

Clinical applications of AI and radiomics in neuro-oncology

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Radiomics in the prediction of disease-free survival in early-stage squamous cervical cancer

The authors of this study conducted multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region in order to predict disease-free survival (DFS) in a cohort of 191 patients with early-stage squamous cervical cancer (ESSCC). They were able to conclude that multiparametric MRI-derived radiomics based on multi-scale tumor region can in fact aid in the prediction of DFS for

Read More →

Measuring bias when using cross-validation in radiomics

Radiomics studies often perform a feature selection step to remove redundant and irrelevant features from the generic features extracted from radiological images. However, one must take care if feature selection is used together with cross-validation. In this case, the feature selection must be applied to each fold separately. If it is applied beforehand on all data as a preprocessing step,

Read More →

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

The authors of this study aimed to systematically review radiomic feature reproducibility and predictive model validation strategies in studies that deal with CT and MRI radiomics of bone and soft-tissue sarcomas. The review consisted of 278 papers, forty-nine of which were published between 2008 and 2020. The authors found that the issues of radiomic feature reproducibility and model validation varied

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 →

The latest developments in radiomics and AI may help against prostate cancer

The authors of this systematic review explored the currently available literature on artificial intelligence (AI) and radiomics applied to molecular imaging of prostate cancer. Due to the great promise that nuclear medicine holds regarding improving the quality of life for prostate cancer patients, this study looks at the myriad areas in which AI and radiomics can positively be applied to

Read More →

The potential of texture analysis for breast density classification

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

Read More →

Radiomic features of breast parenchyma

The object of this study was to assess the similarities and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data. The authors aimed to address the problem using an enlarged set of texture radiomic features, dense/non-dense areas comparison and a new manufacturer, concluding that texture features from FOR PROCESSING mammograms were the

Read More →

Can radiomics signatures predict tumor reponse of patients treated with chemotherapy and targeted therapy?

The authors of this retrospective study had the goal of evaluating the effectiveness of radiomics signatures in order to predict the tumor response of non-small cell lung cancer (NSCLC) patients who were treated with first-line chemotherapy, targeted therapy, or a combination of the two. The authors determined that radiomics signatures based on pre-treatment CT scans can accurately predict tumor response

Read More →

Clinical applications of AI and radiomics in neuro-oncology

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

Read More →

Radiomics in the prediction of disease-free survival in early-stage squamous cervical cancer

The authors of this study conducted multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region in order to predict disease-free survival (DFS) in a cohort of 191 patients with early-stage squamous cervical cancer (ESSCC). They were able to conclude that multiparametric MRI-derived radiomics based on multi-scale tumor region can in fact aid in the prediction of DFS for

Read More →

Measuring bias when using cross-validation in radiomics

Radiomics studies often perform a feature selection step to remove redundant and irrelevant features from the generic features extracted from radiological images. However, one must take care if feature selection is used together with cross-validation. In this case, the feature selection must be applied to each fold separately. If it is applied beforehand on all data as a preprocessing step,

Read More →

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

The authors of this study aimed to systematically review radiomic feature reproducibility and predictive model validation strategies in studies that deal with CT and MRI radiomics of bone and soft-tissue sarcomas. The review consisted of 278 papers, forty-nine of which were published between 2008 and 2020. The authors found that the issues of radiomic feature reproducibility and model validation varied

Read More →

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