Background: The outbreak of Coronavirus Disease 2019 (COVID-19) has become a global public health emergency. Methods: 204 elderly patients (!60 years old) diagnosed with COVID-19 in Renmin Hospital of Wuhan University from January 31st to February 20th, 2020 were included in this study. Clinical endpoint was inhospital death. Results: Of the 204 patients, hypertension, diabetes, cardiovascular disease, and chronic obstructive pulmonary disease (COPD) were the most common coexisting conditions. 76 patients died in the hospital. Multivariate analysis showed that dyspnea (hazards ratio (HR) 2.2, 95% confidence interval (CI) 1.414-3.517; p < 0.001), older age (HR 1.1, 95% CI 1.070-1.123; p < 0.001), neutrophilia (HR 4.4, 95% CI 1.310-15.061; p = 0.017) and elevated ultrasensitive cardiac troponin I (HR 3.9, 95% CI 1.471-10.433; p = 0.006) were independently associated with death. Conclusion:Although so far the overall mortality of COVID-19 is relatively low, the mortality of elderly patients is much higher. Early diagnosis and supportive care are of great importance for the elderly patients of COVID-19.
Purpose Clinical implementation of magnetic resonance imaging (MRI)‐only radiotherapy requires a method to derive synthetic CT image (S‐CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI‐based S‐CT generation and evaluated the dosimetric accuracy on prostate IMRT planning. Methods A paired CT and T2‐weighted MR images were acquired from each of 51 prostate cancer patients. Fifteen pairs were randomly chosen as tested set and the remaining 36 pairs as training set. The training subjects were augmented by applying artificial deformations and feed to a two‐dimensional U‐net which contains 23 convolutional layers and 25.29 million trainable parameters. The U‐net represents a nonlinear function with input an MR slice and output the corresponding S‐CT slice. The mean absolute error (MAE) of Hounsfield unit (HU) between the true CT and S‐CT images was used to evaluate the HU estimation accuracy. IMRT plans with dose 79.2 Gy prescribed to the PTV were applied using the true CT images. The true CT images then were replaced by the S‐CT images and the dose matrices were recalculated on the same plan and compared to the one obtained from the true CT using gamma index analysis and absolute point dose discrepancy. Results The U‐net was trained from scratch in 58.67 h using a GP100‐GPU. The computation time for generating a new S‐CT volume image was 3.84–7.65 s. Within body, the (mean ± SD) of MAE was (29.96 ± 4.87) HU. The 1%/1 mm and 2%/2 mm gamma pass rates were over 98.03% and 99.36% respectively. The DVH parameters discrepancy was less than 0.87% and the maximum point dose discrepancy within PTV was less than 1.01% respect to the prescription. Conclusion The U‐net can generate S‐CT images from conventional MR image within seconds with high dosimetric accuracy for prostate IMRT plan.
PurposeTo investigate the intrafractional stability of the motion relationship between the diaphragm and tumor, as well as the feasibility of using diaphragm motion to estimate lung tumor motion.MethodsEighty‐five paired (pre and posttreatment) daily 4D‐CBCT images were obtained from 20 lung cancer patients who underwent SBRT. Bony registration was performed between the pre‐ and post‐CBCT images to exclude patient body movement. The end‐exhalation phase image of the pre‐CBCT image was selected as the reference image. Tumor positions were obtained for each phase image using contour‐based translational alignments. Diaphragm positions were obtained by translational alignment of its apex position. A linear intrafraction model was constructed using regression analysis performed between the diaphragm and tumor positions manifested on the pretreatment 4D‐CBCT images. By applying this model to posttreatment 4D‐CBCT images, the tumor positions were estimated from posttreatment 4D‐CBCT diaphragm positions and compared with measured values. A receiver operating characteristic (ROC) test was performed to determine a suitable indicator for predicting the estimate accuracy of the linear model.ResultsUsing the linear model, per‐phase position, mean position, and excursion estimation errors were 1.12 ± 0.99 mm, 0.97 ± 0.88 mm, and 0.79 ± 0.67 mm, respectively. Intrafractional per‐phase tumor position estimation error, mean position error, and excursion error were within 3 mm 95%, 96%, and 99% of the time, respectively. The residual sum of squares (RSS) determined from pretreatment images achieved the largest prediction power for the tumor position estimation error (discrepancy < 3 mm) with an Area Under ROC Curve (AUC) of 0.92 (P < 0.05).ConclusionUtilizing the relationship between diaphragm and tumor positions on the pretreatment 4D‐CBCT image, intrafractional tumor positions were estimated from intrafractional diaphragm positions. The estimation accuracy can be predicted using the RSS obtained from the pretreatment 4D‐CBCT image.
Background Magnetic resonance imaging (MRI) and functional MRI techniques have been widely used in the diagnosis of human immunodeficiency virus (HIV) infection related diseases. Purpose To explore whether magnetic resonance diffusion-weighted imaging (DWI) can track water molecular diffusion changes in the brain of asymptomatic HIV-positive adolescents. Material and Methods Multi-b value DWI was performed in 23 adolescents, including 15 HIV-positive participants and eight HIV-negative healthy participants. Mean apparent diffusion coefficient (ADC), slow apparent diffusion coefficient (ADCs) values, fast apparent diffusion coefficient (ADCf) values, distribution diffusion coefficient (DDC) values, and heterogeneity index (α) values were calculated within regions of interest (ROIs) in the frontal lobes, basal ganglia, and temporal lobe. Non-parametric tests were then performed. Results In the bilateral frontal lobes, the mean α values in HIV-positive participants were significantly increased compared with those in healthy participants (right side P = 0.001; left side P = 0.000). In the left frontal lobe, the mean DDC value in HIV-positive participants was significantly increased compared with that in healthy participants ( P = 0.008). In the bilateral frontal lobes, the mean ADCf values in HIV-positive participants were significantly lower than those in healthy participants (right side P = 0.011; left side P = 0.008). In the left basal ganglia, the mean α values in HIV-positive participants were significantly lower than that in healthy participants ( P = 0.013). Conclusion Multi-b value DWI could reflect the early characteristics of water molecule diffusion in HIV infections.
Objective: To evaluate the application value of multib-value diffusion-weighted imaging (DWI) with monoexponential model and stretched-exponential model in the diagnosis of HIV-positive patients. Methods: 50, 150, 200, 400, 600, 800 s mm 22) DWI was performed in 23 adolescent orphans from AIDS families, including 15 HIV-positive subjects and 8 HIV-negative healthy subjects. Apparent diffusion coefficient (ADC) values were fitted by monoexponential model; distribution diffusion coefficient (DDC) values and heterogeneity index (a) values were fitted by stretched-exponential model in bilateral basal ganglia, then non-parametric tests were performed. Results: The signal intensity attenuation in multi-b-value DWI could be well described by both mono-exponential model and stretched-exponential model. In the left basal ganglia, mean a-values in HIV-positive subjects (a 5 0.848 6 0.068) were significantly lower than that in healthy subjects (a 5 0.923 6 0.050, p 5 0.013). There was no statistical difference of a-values between HIV-positive subjects and healthy control subjects in the right basal ganglia. Apart from these, there were also no statistical differences of DDC values or ADC values between two groups in bilateral basal ganglia (all p . , we can see stretched-exponential model DWI can provide more information than mono-exponential model DWI. Advances in knowledge: Multi-b-value DWI was performed in subjects with HIV. DWI measurements could be neuroimaging biomarkers of cerebral injury in the course of HIV infection.
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