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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.

Most used hashtags:

deep learningmachine learningmriradiomicsx-ray computed tomographyai newsCOVID-19mammographyinterviewneural networksbreast cancerdiagnostic imagingtomographyworkflowMDCTdiagnosis

Latest posts

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

European Radiology December 2020

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

Read More →

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

European Radiology October 2020

The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the prediction of axillary lymph node (ALN) metastasis in breast cancer. The authors found that the CESM-based radiomics nomogram showed good application prospects in the preoperative prediction of ALN metastasis in breast cancer. Key points The CESM-based

Read More →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

European Radiology October 2020

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

Combining molecular and imaging metrics in cancer: radiogenomics

Katja Pinker June 2020

In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics, proteomics, metabolomics, etc.) to disease treatments and prevention strategies for our patients. Molecular testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. In addition, invasive tumor sampling only provides a snap-shot of often heterogeneous tumors and is not

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

European Radiology June 2020

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

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

European Radiology March 2020

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Burak Kocak February 2020

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

European Radiology December 2019

The purpose of this retrospective study was to investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). The study included 218 bladder cancer patients who underwent DWI prior to biopsy between July 2014 and December 2018. The authors discovered that combining DWI radiomics features with transurethral resection (TUR)

Read More →

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

European Radiology Experimental November 2019

The aim of this study was to develop a supervised machine learning (ML) algorithm that would use diffusion-weighted imaging-derived radiomic features to predict median overall survival in patients with pancreatic ductal adenocarcinoma. Based on the evaluation of 132 patients, it was determined that the use of ML allowed the prediction of overall survival with high diagnostic accuracy. Key points Pancreatic

Read More →

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

European Radiology October 2019

This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of induction chemotherapy response and survival in nasopharyngeal carcinoma patients. The results proved that multiparametric MRI-based radiomics could be helpful for personalized risk stratification in patients receiving induction chemotherapy. Key points MRI Radiomics can predict IC response and

Read More →

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

European Radiology December 2020

This study aimed to evaluate whether radiomics from magnetic resonance imaging (MRI) would allow for the prediction of the overall survival in patients with isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas. The authors of this study also investigated the added prognostic value of radiomics over clinical features. The authors found that radiomics has the potential for noninvasive risk stratification and can

Read More →

Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study

European Radiology October 2020

The aim of this retrospective study was to establish and validate a radiomics nomogram that was based on contrast-enhanced spectral mammography (CESM) for the prediction of axillary lymph node (ALN) metastasis in breast cancer. The authors found that the CESM-based radiomics nomogram showed good application prospects in the preoperative prediction of ALN metastasis in breast cancer. Key points The CESM-based

Read More →

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma

European Radiology October 2020

The authors of this retrospective study aimed to develop and validate a CT-based radiomics model for preoperative prediction of spread through air space (STAS) in lung adenocarcinoma. They found that a CT-based radiomics model can preoperatively predict, with good diagnosis performance, STAS in lung adenocarcinoma. Key points CT-based radiomics and machine learning model can predict spread through air space (STAS)

Read More →

Combining molecular and imaging metrics in cancer: radiogenomics

Katja Pinker June 2020

In oncology, we are in the era of personalized medicine that enables increasingly precise, often molecular-based approaches (genomics, transcriptomics, proteomics, metabolomics, etc.) to disease treatments and prevention strategies for our patients. Molecular testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients. In addition, invasive tumor sampling only provides a snap-shot of often heterogeneous tumors and is not

Read More →

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

European Radiology June 2020

The purpose of this retrospective study was to develop and evaluate the performance of U-Net to determine whether U-Net-based deep learning could accurately perform fully automated localization and segmentation of cervical tumors in MR images, as well as the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. Key points U-Net-based deep learning can perform accurate fully automated localization and

Read More →

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

European Radiology March 2020

In this study, the authors aimed to build a dual-energy CT (DECT)-based deep learning radiomics nomogram that could be used for lymph node metastasis prediction in gastric cancer. Ultimately, the DECT-based deep learning radiomics nomogram operated well in predicting lymph node metastasis in gastric cancer. Key points This study investigated the value of deep learning dual-energy CT–based radiomics in predicting

Read More →

Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

Burak Kocak February 2020

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

Read More →

Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

European Radiology December 2019

The purpose of this retrospective study was to investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). The study included 218 bladder cancer patients who underwent DWI prior to biopsy between July 2014 and December 2018. The authors discovered that combining DWI radiomics features with transurethral resection (TUR)

Read More →

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

European Radiology Experimental November 2019

The aim of this study was to develop a supervised machine learning (ML) algorithm that would use diffusion-weighted imaging-derived radiomic features to predict median overall survival in patients with pancreatic ductal adenocarcinoma. Based on the evaluation of 132 patients, it was determined that the use of ML allowed the prediction of overall survival with high diagnostic accuracy. Key points Pancreatic

Read More →

MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma

European Radiology October 2019

This study integrated the clinical data and radiomics signature generated by a support vector machine to establish a radiomics nomogram for prediction of induction chemotherapy response and survival in nasopharyngeal carcinoma patients. The results proved that multiparametric MRI-based radiomics could be helpful for personalized risk stratification in patients receiving induction chemotherapy. Key points MRI Radiomics can predict IC response and

Read More →

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  • Option to participate in the European Diploma. 3
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  • Content e-mails for all ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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Footnotes:

01

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.