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Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

Zheng-Han Yang February 2022

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

European Radiology March 2021

In this study, the authors extended the ComBat approach to provide a harmonization procedure that is applicable to any radiomic feature. They achieve this by combining image standardization with ComBat realignment, thus demonstrating that this could efficiently remove the scanner/protocol effect while preserving the individual variations in phantom, brain, and prostate MR scans. Key points Radiomic feature values obtained using

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

David Bonekamp January 2021

We use a previously validated artificial neural network to evaluate its performance in a much larger, subsequent, consecutive cohort. In the community, there exists a belief that with infinite training data, an AI system can theoretically be trained that has the ability to handle all possible data and thus be generalised to all environments. Applied to the prostate, this would

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

European Radiology January 2021

The purpose of this study, performed between January 2014 and May 2019 across five different centers, was to construct an MRI radiomics model and help radiologists to improve the preoperative assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC). The authors were able to find that the MRI-based radiomics model could be used to assess the status of

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

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

European Radiology November 2020

This retrospective study aimed to investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning in order to differentiate benign lesions from malignant lesions using model-free parameter maps. The authors determined that radiomics analysis coupled with machine learning does improve the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Stefanie Hectors September 2020

Advanced hepatocellular carcinoma (HCC) carries a dismal prognosis. For a decade, sorafenib, a multi-kinase inhibitor, was the only approved systemic therapy for HCC. However, its response rate in advanced HCC is only about 2%. The last few years have seen rapid approval of additional systemic therapies for HCC, including immunotherapy strategies. Immune checkpoint inhibitor nivolumab has a promising reported response

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

Yuyun Xu September 2020

As a research hotspot in recent years, radiomics provides a new perspective for image diagnosis or evaluation by mining a large number of non-traditional visual information in medical images and adds new indicators (image markers). Radiomics may become a non-invasive, low-cost and easy to popularize imaging tool. White matter hyperintensity (WMH) refers to the high signal of white matter area

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

European Radiology August 2020

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly in patients with glioma, as well as to identify potential covariates that may have an influence on the diagnostic performance of machine learning. The authors were able to show that machine learning demonstrated excellent diagnostic performance in

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

He Zhang July 2020

Endometrial cancer (EC) has the highest rate of malignancy in women in the entire world, including China, which has the largest population. Accurately staging EC prior to an invasive procedure still poses a challenge for clinicians. In the present study, we used more than five hundred EC patients’ MR images to train the computer to establish a deep learning diagnostic

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

European Radiology January 2021

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

European Radiology December 2020

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

European Radiology November 2020

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Stefanie Hectors, Bachir Taouli September 2020

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

Yuyun Xu, Xiangyang Gong September 2020

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

European Radiology August 2020

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

He Zhang July 2020

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

Latest posts

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

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

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

How can we combat multicenter variability in MR radiomics? Validation of a correction procedure

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

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

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging

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

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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