Abdominal radiology (New York)
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Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. ⋯ The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.
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To study the detection of clinically significant prostate cancer (PCa) by readers with different experience, comparing performance with biparametric magnetic resonance imaging (bmMRI) and with the reference multiparametric (mpMRI). ⋯ The study revealed the impact of the readers' experience when using bpMRI. The bpMRI without contrast media was a valid alternative for expert readers, whereas less experienced ones needed DCE to significantly boost SNS and AUC. Results indicate 700-800 cases as threshold for reliable interpretation with bpMRI.
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To investigate the frequency and imaging features of radiographically evident abdominal immune-related adverse events (irAEs) in patients with metastatic non-small-cell lung cancer (NSCLC) treated with PD-1 inhibitors. ⋯ Abdominal irAEs were detected on CT in 13% of NSCLC patients treated with nivolumab, and colitis, in the pancolitis form, was the most common irAE. Given the expanding role of immunotherapy, radiologists should be aware of the frequency and imaging manifestations of abdominal irAEs and the impact on patient management.
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To investigate the diagnostic accuracy of MRI for placenta accreta spectrum (PAS) and clinical outcome prediction in women with placenta previa, using a novel MRI-based predictive model. ⋯ The diagnostic accuracy of MRI, especially considering the combination of the most predictive MRI findings, is higher when the target of the prediction is the clinical outcome rather than the PAS.
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To evaluate the value of MR liver extracellular volume (ECVliver) in assessment of liver fibrosis with chronic hepatitis B (CHB), and to compare its performance with two-dimensional (2D) shear-wave elastography (SWE). ⋯ MR ECVliver plays a promising role in the prediction of liver fibrosis for patients with CHB, comparable to 2D SWE.