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Objective:To explore the application value of apparent diffusion coefficient (ADC) of magnetic resonance diffusion weighted imaging (DWI) parameters in glioma classification and glioma microstructure evaluation.Methods:From June 2017 to November 2019, 38 patients with glioma confirmed by surgery and pathology in Haikou Hospital Affiliated to Xiangya Medical College of Central South University were retrospectively analyzed. According to the pathological results, they were divided into low-grade (WHO Ⅰ-Ⅱ, 15 cases) glioma group and high-grade (WHO Ⅲ-Ⅳ, 23 cases) glioma group. They received magnetic resonance imaging (MRI) plain scan and DWI scan respectively, and the ADC value and microstructure of different grades of glioma were compared. The correlation between ADC value of glioma and the percentage of vascular endothelial growth factor (VEGF)-positive cells, cell density and integrated optical density (IOD) value of aquaporin 1 (AQP1) expression was analyzed.Results:(1) MRI examination showed that the signals of low-grade glioma were more uniform, with no or slight peritumoral edema and space occupying effect, and the enhancement was more non enhanced or slightly enhanced. The signals of high-grade glioma were more heterogeneous due to necrosis and bleeding, and the peritumoral edema and space occupying effect were more obvious, showing uneven obvious enhancement or irregular ring enhancement; (2) The percentage of VEGF positive cells, cell density and the IOD value of AQP1 expression in high-grade glioma were significantly higher than that in low-grade glioma, and the ADC value was lower than that in low-grade glioma (all P<0.05); (3) The ADC value of glioma patients was negatively correlated with the percentage of VEGF-positive cells, cell density, and the IOD value of AQP1 expression ( r=-0.55, -0.65, -0.63, all P<0.05). Conclusions:The ADC value of glioma can indirectly reflect the expression of VEGF, cell density and AQP1 positive expression level, which is helpful for preoperative glioma classification and evaluation of glioma microstructure and biological characteristics.
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Objective@#To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. @*Materials and Methods@#Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. @*Results@#With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. @*Conclusion@#The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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Objective@#To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. @*Materials and Methods@#Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. @*Results@#With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. @*Conclusion@#The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.
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Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.
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Since the outbreak of coronavirus disease 2019 (COVID-19), chest computed tomography (CT) has been an important imaging modality in the diagnosis, treatment and follow-up of patients with COVID-19,but meanwhile the risk of cross-infection between the staff and patients in Department of Radiology is increasing. Shelter CT is specifically used for the examination of patients with suspected or confirmed COVID-19 to reduce the infection risk. Based on practical work experience, the management and prevention measures for COVID-19 in shelter CT are discussed from the aspects of the installation, function division and examination procedures of shelter CT, patient examination route, the staff management and infection prevention for radiology technologists, and the disinfection of CT equipments and object surface.
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Objective To explore the value of pretargeting technology in vitro MRI of L5 peptide guided streptavidin-conjugated and polyethylene glycol modification protected ultra-small superparamagnetic iron oxide(SA-PEG-USPIO) to hepatocellular carcinoma(HCC) via glypican-3(GPC3) receptor.Methods Direct immumofluorescence assay with carboxyfluorescein(FAM) labeled L5 and competitive inhibition was performed in HepG2 and HL-7702 cells.Imaging was obtained from fluorescent microscope.Immunoassay fluorescence images were carried out to determine the expression of GPC3 in HepG2 cell.PEG-USPIO conjugated with streptavidin was made by carbodiimide reaction,and the hydrodynamic diameters,Zeta potential and magnetic relaxivity of SA-PEG-USPIO and PEG-USPIO were measured.HL7702 cells were used for evaluate cells viability of SA-PEG-USPIO and PEG-USPIO.HepG2 and HL-7702 cells were used as experimental and control group respectively.Each of the two cell lines were further divided into three groups:L5-BT united SA-PEG-USPIO group,SA-PEG-USPIO group and control group.Prussian blue staining and MRI was preformed to observe the targeting efficacy of SA-PEG-USPIO respectively,and normalized T2 signal was recorded.The significant changes of normalized T2 signal intensity among groups was deterumine by using One-way analysis of variance.Results There were much more fluorescences on the membrane and cytoplasm of HepG2 cells than those on HL-7702 cells and cells of competition group.And indirect immunofluorescence images show the obvious expression of GPC3 in HepG2 cell.The SA-PEG-USPIO and PEG-USPIO nanoparticles had hydrodynamic diameters of (22.73 ± 3.31) and (35.97±5.19)nm,Zeta potential of them were (4.22±0.53) and (-7.91± 1.22)mV and magnetic relaxivity were 0.139 4× 103 and 0.103 9 × 103 mM-1s1.Although the highest concentration of SA-PEG-USPIO and PEG-USPIO was 2.4 mmol/L,cells viability was greater than 80％.The most iron particle was observed in L5-BT united SA-PEG-USPIO group of HepG2 cells.In vitro MR,the normalized T2 signal intensity of HepG2 cells in L5-BT united SA-PEG-USPIO group,SA-PEG-USPIO group and control group were 39±7,77 ± 12 and 93 ± 4.There was significant difference among those three groups (F=23.96,P＜0.01).The normalized T2 signal intensity of HL-7702 cells in each of three groups were 69± 11,78±8 and 95±5.There was no significant difference among those three groups (F=2.86,P＞0.05).Conclusion By the pretargeting method,L5 peptide guided SA-PEG-USPIO has effective targeting ability to HepG2 cells in vitro.
