European radiology
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Radiotherapy (RT) is an effective method for treating head and neck cancer (HNC). However, RT may cause side effects during and after treatment. Radiation-induced brainstem injury (BSI) is often neglected due to its low incidence and short survival time and because it is indistinguishable from intracranial tumor progression. ⋯ There are many clinical studies on BSI caused by IMRT, PBT, and HIT. In this paper, we review the mechanism, dosimetry, and other aspects of BSI caused by IMRT, PBT, and HIT. Key Points• Enhanced MRI imaging can better detect radiation-induced BSI early.• This article summarized the dose constraints of brainstem toxicity in clinical studies using different techniques including IMRT, PBT, and HIT and recommended better dose constraints pattern to clinicians.• The latest pathological mechanism of radiation-induced BSI and the corresponding advanced treatment methods will be discussed.
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Multicenter Study
Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.
To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. ⋯ • Input matrix size significantly affected the performance of a deep learning-based CAD for detection of nodules or masses on chest radiographs. • The matrix size 896 showed the best performance in two different CNN detection models. • The optimal matrix size of chest radiographs could enhance CAD performance without additional training data.
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To evaluate organisational reporting infrastructure and patient-related reporting data in the diagnosis of vertebral fragility fractures (VFFs) as demonstrated on computed tomography (CT). ⋯ • Early detection and diagnosis of vertebral fragility fractures (VFFs) significantly reduce patient morbidity and mortality. This study describes the results of a retrospective UK-wide audit evaluating current radiology reporting practice in the opportunistic diagnosis of VFFs as demonstrated on computed tomography (CT) studies including the spine. • Key audit standards included comment made on bone integrity in primary report (target 100%), comment made on severity of fractures (90%), report used recommended terminology 'fracture' (100%), and report made appropriate recommendations for referral/further assessment (100%). The audit results demonstrated a lack of compliance with all audit standards; lack of compliance was most marked in the use of recommended terminology (achieved 60.3%), in relation to comment on fracture severity (achieved 26.2%) and for recommendation for referral/further assessment (achieved 2.6%). • Solutions are challenging and multifactorial but the opportunity exists for all radiologists to examine their practice and directly improve patient care.
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Multicenter Study
A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.
Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. ⋯ • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.
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Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. ⋯ Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.