radiomics

Latest posts

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)

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

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

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

Richard Do September 2019

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.

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

European Radiology September 2019

This article sets out to determine whether machine learning can be used to train and calibrate the signature for diagnosing hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. The authors proved that artificial intelligence could enhance clinicians’ decision and reduce the rate of cirrhotic patients requiring liver biopsy. Key points In cirrhotic patients with visually indeterminate liver nodules, expert

Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features

Alberto Tagliafico September 2019

Despite the encouraging results, more studies are needed in order to further evaluate these preliminary findings and to find to what extent radiomics and AI approaches can be integrated in clinical practice in a useful and reliable strategy [1]. I think that several issues reduce the application of radiomics approaches in clinical practice: the lack of knowledge of its basic

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

Daniel Pinto dos Santos July 2019

Over the last few years, the number of studies published using quantitative imaging biomarkers to classify or predict pathologies has steadily increased. As of today, a quick PubMed search for radiomics, imaging biomarkers or radiogenomics reveals well over 4,000 articles. However, somewhat surprisingly, given this amount of published research, outside of academic literature there is no widespread clinical application of

MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

European Radiology May 2019

This study aimed to assess whether MRI radiomics can categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer patients. The authors evaluated the diagnostic performance of the signatures derived from MRI radiomics in 286 patients with proven adnexal tumor. The study results suggest a correlation between radiomics features extracted from MRI and

What the radiologist should know about artificial intelligence – an ESR white paper

Daniel Pinto dos Santos April 2019

Did you ever get lost between all the buzz words floating around like artificial intelligence, imaging informatics, radiomics, imaging biobanks, neural networks, clinical decision support, etc.? And what are the ethical and medico-legal implications of all these new developments? Then you may want to read the ESR white paper that was recently published by the ESR’s eHealth and Informatics subcommittee

How machine learning-based high-dimensional CT texture analysis is influenced by segmentation margin

Burak Kocak April 2019

Radiomic workflows include various challenging steps. One of the most demanding steps in radiomics is the segmentation process. Particularly for the renal cell carcinomas, most of the studies used manual tumour contour delineation. In this work, our group wanted to perform an experiment by changing the segmentation margin a little bit, that is, just 2 mm, to see what happens

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

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

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

European Radiology October 2019

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

Richard Do September 2019

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

European Radiology September 2019

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

Latest posts

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

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

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

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

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

What the radiologist should know about artificial intelligence – an ESR white paper

How machine learning-based high-dimensional CT texture analysis is influenced by segmentation margin

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

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

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

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

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules

Breast cancer Ki-67 expression prediction by digital breast tomosynthesis radiomics features

Why radiomics research does not translate to clinical practice: evaluation of literature using RQS and TRIPOD

MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

What the radiologist should know about artificial intelligence – an ESR white paper

How machine learning-based high-dimensional CT texture analysis is influenced by segmentation margin

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radiomics

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