European journal of radiology
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With artificial intelligence (AI) precipitously perched at the apex of the hype curve, the promise of transforming the disparate fields of healthcare, finance, journalism, and security and law enforcement, among others, is enormous. For healthcare - particularly radiology - AI is anticipated to facilitate improved diagnostics, workflow, and therapeutic planning and monitoring. And, while it is also causing some trepidation among radiologists regarding its uncertain impact on the demand and training of our current and future workforce, most of us welcome the potential to harness AI for transformative improvements in our ability to diagnose disease more accurately and earlier in the populations we serve.
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To investigate the predictive capability of machine learning-based multiparametric magnetic resonance (MR) imaging radiomics for evaluating the aggressiveness of papillary thyroid carcinoma (PTC) preoperatively. ⋯ Our study showed that machine learning-based multiparametric MR imaging radiomics could accurately distinguish aggressive from non-aggressive PTC preoperatively. This approach may be helpful for informing treatment strategies and prognosis of patients with aggressive PTC.
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To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing. ⋯ With regard to AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, whereas medical students and surgeons tend to be more skeptical towards this technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are rather afraid of turf losses.
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Comparative Study
Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer.
To compare the diagnostic performance of PI-RADS v2 and v2.1 for detecting transition zone prostate cancer (TZPC) on multiparametric prostate MRI (mpMRI). ⋯ These results suggest that compared with PI-RADS v2, PI-RADS v2.1 could be preferable for evaluating TZ lesions.
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The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter has been proven to be a prognostic and predictive biomarker for lower grade glioma (LGG). This study aims to build a radiomics model to preoperatively predict the MGMT promoter methylation status in LGG. ⋯ Conventional MRI radiomics models are reliable for predicting the MGMT promoter methylation status in LGG patients. The fusion of radiomics features from different series may increase the prediction performance.