European radiology
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The strategically acquired gradient echo (STAGE) protocol, developed for 3T scanners, allows one to derive quantitative maps such as T1, T2*, proton density, and quantitative susceptibility mapping in about 5 min. Our aim was to adapt the STAGE sequences for 1.5T scanners which are still commonly used in clinical practice. Furthermore, the accuracy and repeatability of the STAGE-derived T1 estimate were tested. ⋯ • The STAGE imaging protocol was optimized for use on 1.5T field strength scanners. • A practical STAGE protocol makes it possible to derive quantitative maps (i.e., T1, T2*, PD, and QSM) in about 7 min at 1.5T. • The T1 estimate derived from the STAGE protocol showed good accuracy and repeatability.
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This study aimed to discuss whether a diameter of 3 cm is a threshold for diagnosing lung adenocarcinomas presenting with radiological pure ground-glass mass (PGGM, pure ground-glass opacity > 3 cm) as adenocarcinomas in situ or minimally invasive adenocarcinomas (AIS-MIAs). Another aim was to identify CT features and patient prognosis that differentiate AIS-MIAs from invasive adenocarcinomas (IACs) in patients with PGGMs. ⋯ • Patients with pure ground-glass opacity > 3 cm in diameter are rare but can be diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. • The mean CT attenuation is the sole significant CT parameter that differentiates invasive adenocarcinoma from adenocarcinoma in situ or minimally invasive adenocarcinoma in patients with pure ground-glass opacity > 3 cm. • Lung adenocarcinoma with pure ground-glass opacity > 3 cm has an excellent prognosis, even in cases of invasive adenocarcinoma.
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Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT. ⋯ • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.
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To develop a convolutional neural network (CNN) model for the automatic detection and classification of rib fractures in actual clinical practice based on cross-modal data (clinical information and CT images). ⋯ • The developed convolutional neural network (CNN) performed better in fresh, healing, and old fractures and yielded a good classification performance in three categories, if both (clinical information and CT images) were used compared to CT images alone. • The CNN model had a higher sensitivity and matched precision in three categories than experienced radiologists with a shorter diagnosis time in actual clinical practice.
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The 2019 Coronavirus (COVID-19) results in a wide range of clinical severity and there remains a need for prognostic tools which identify patients at risk of rapid deterioration and who require critical care. Chest radiography (CXR) is routinely obtained at admission of COVID-19 patients. However, little is known regarding correlates between CXR severity and time to intubation. We hypothesize that the degree of opacification on CXR at time of admission independently predicts need and time to intubation. ⋯ • Chest radiography at the time of admission independently predicts time to intubation within 48 h and during the hospital stay in COVID-19 patients. • More opacities on chest radiography are associated with several fold increases in early mechanical ventilation among COVID-19 patients. • Chest radiography is useful in identifying COVID-19 patients whom may rapidly deteriorate and help inform clinical management as well as hospital bed and ventilation allocation.