Journal of magnetic resonance imaging : JMRI
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J Magn Reson Imaging · May 2020
ReviewArtificial intelligence in the interpretation of breast cancer on MRI.
Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer-aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image-specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. ⋯ Magn. Reson. Imaging 2020;51:1310-1324.
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J Magn Reson Imaging · May 2020
Clinical TrialProstate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques.
Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI. ⋯ 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.
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J Magn Reson Imaging · May 2020
EditorialEditorial for "Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI-Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI-IMPROD Trials)".
5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:1568-1569.
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J Magn Reson Imaging · May 2020
EditorialEditorial for "Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques".
5 TECHNICAL EFFICACY STAGE: 3 J. Magn. Reson. Imaging 2020;51:1554-1555.
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J Magn Reson Imaging · May 2020
Multicenter StudyDeep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.
The dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known. ⋯ 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1487-1496.