Objective:To construct a model for predicting early recurrence of hepatocellular carcinoma (HCC) patients after radiofrequency ablation by different machine learning models based on multimodal MRI and clinical indicators, and to evaluate the predictive efficacy of the model.Methods:The data of patients with HCC who underwent radiofrequency ablation in Fourth Medical Center of Chinese PLA General Hospital and the First Medical Center of Chinese PLA General Hospital from January 2015 to December 2021 were retrospectively analyzed. A total of 169 patients with HCC were enrolled, including 152 males and 17 females, aged (57.2±9.2) years. The training set ( n=135) and the test set ( n=34) were randomly divided according to 8∶2. There were 49 cases recurrence in training set and 12 cases recurrence in test set. Based on the training set, the clinical influencing factors of early recurrence in patients with HCC after radiofrequency ablation were screened by univariated and multivariate logistic analysis, and the imaging features were sequentially screened by variance threshold method, select K-best and LASSO regression. Support vector machine (SVM), logistic regression and random forest (RFOREST) were used to construct the prediction models of early postoperative recurrence with simple imagomics alone or combined clinical features, respectively, and the receiver operating characteristic (ROC) curve was used to evaluate the prediction efficiency of the models. Results:Multivariate logistic regression analysis showed that preoperative alpha-fetoprotein >20 μg/L, platelet count >140×10 9 and tumor location were the influential factors for early recurrence of HCC patients after radiofrequency ablation (all P<0.05). Through variance threshold analysis, select K-best and LASSO regression, 16 optimal image omics features were selected. SVM, logistic regression and RFOREST were used to construct a simple imaging omics model for predicting early recurrence of HCC patients after radiofrequency ablation. The areas under ROC curve of the test set were 0.826, 0.830 and 0.826, respectively. And the areas under ROC curve of the constructed imagomics combined clinical model of test set were 0.830, 0.830 and 0.909, respectively. The area under ROC curve of RFOREST in the test set was better than that of SVM and logistic regression ( Z=2.19, 3.98, P=0.008, 0.008). Conclusion:The combined model constructed by SVM, logistic regression and RFOREST based on clinical indicators and image omics features is effective in predicting the early recurrence of patients with HCC after radiofrequency ablation, and the model constructed by RFOREST is the best.

Objective:To evaluate the accuracy of an artificial intelligence (AI) model in measuring key angles on pelvic radiographs of the hip and assess its effectiveness in diagnosing developmental dysplasia of the hip (DDH) and borderline developmental dysplasia of the hip (BDDH).Methods:A retrospective analysis was conducted using anteroposterior pelvic X-ray films from 1,029 patients with suspected DDH. The data were collected from the Department of Radiology, Fourth Medical Center of the Chinese PLA General Hospital. Among the patients, 273 were male, and 756 were female, with an average age of 57.01 ± 18.16 years (range, 12-88 years). The dataset was randomly divided into a training set (720 cases), a test set (206 cases), and a validation set (103 cases). Two radiologists identified and marked key anatomical points of the hip joint to establish the training dataset, which was then used to develop a deep learning-based AI model capable of locating these key anatomical positions. Using the identified anatomical points, the AI model automatically measured and calculated the Sharp angle, center-edge (CE) angle, and T?nnis angle in the test dataset. The measurement results from the AI model were compared with those of the radiologists to evaluate the model's accuracy. The validation set was used to optimize model parameters, and the test dataset was used to evaluate the diagnostic performance of DDH. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic efficacy of the AI model for DDH and BDDH.Results:The accuracy rates of the AI model in measuring the left Sharp angle, CE angle, and T?nnis angle for diagnosing DDH were 89.8%, 90.1%, and 86.8%, respectively. For the right side, the accuracy rates were 93.7%, 92.2%, and 80.5%, respectively. There were no statistically significant differences in the mean values of the Sharp, T?nnis, and CE angles between manual and AI measurements ( P>0.05). Pearson correlation tests and intraclass correlation coefficient (ICC) analyses revealed high consistency between AI and manual measurements of the Sharp angle, T?nnis angle, and CE angle, with r-values and ICC values exceeding 0.75. Additionally, the AI model performed measurements significantly faster (1.7±0.1 s) than radiologists (88.1±8.4 s and 90.3±7.4 s, P<0.001). The areas under the ROC curves (AUCs) for diagnosing DDH using the Sharp angle, CE angle, and T?nnis angle measured by the AI model were 0.883, 0.922, and 0.908 (left side) and 0.924, 0.871, and 0.922 (right side), respectively. For diagnosing BDDH, the AUCs of the left and right CE angles measured by the AI model were 0.787 and 0.676, respectively. Kappa test results indicated good agreement between the AI model and manual measurements as well as final clinical diagnoses. For the CE angle, the κ value of the AI model was 0.663, while κ values for the Sharp and T?nnis angles were all greater than 0.800. Conclusion:The convolutional neural network-based AI model effectively and automatically measures the Sharp, CE, and T?nnis angles and demonstrates high diagnostic efficacy for DDH and BDDH.

Objective To analyze the prevalence of chronic diseases and its influencing factors of dust-exposed male workers in a coal mine. Methods A total of 9 782 dust-exposed male workers from a coal mine in Shanxi Province were selected as the study subjects using the purposive sampling method. Their occupational health examination results were collected to analyze the prevalence of chronic diseases and its influencing factors. Results The prevalence of dyslipidemia, hyperuricemia, hypertension and diabetes were 40.3%, 30.7%, 23.5% and 5.6%, respectively. The prevalence of chronic diseases was 64.8%. Among them, the prevalence of having one, two, three or more chronic diseases were 36.5%, 21.6% and 6.7%, respectively. The prevalence of comorbid chronic diseases was 28.3%, with the highest prevalence of concurrent dyslipidemia and hyperuricemia of 11.0%. The results of binary logistic regression analysis showed that the risk of chronic disease was higher in workers <40 years old, smoking, overweight, obesity and total working years >20 years (all P<0.05). The results of multinomial logistic regression analysis showed that workers <40 years old, overweight, obesity and total working years >20 years were risk factors for having one chronic disease (all P<0.05). The workers <40 years old, smoking, overweight, obesity and total working years >20 years were risk factors for having two chronic diseases (all P<0.05). The workers <40 years old, smoking, alcohol consumption, overweight, obesity, other types of work, and working years >20 years were risk factors for having three or more chronic diseases (all P<0.05). Conclusion The prevalence of chronic diseases is high and the comorbidity of chronic diseases is common among dust-exposed male workers. The main influencing factors were age, smoking, alcohol consumption, overweight, obesity, type of work, and working year. Workers with more contributing factors have higher risk of chronic comorbidities.

The accurate assessment of burn injuries is complex and difficult. The emergence of some new skin imaging techniques, such as hyperspectral imaging, unilateral magnetic resonance imaging, laser Doppler perfusion imaging, etc., has made great progress in research on burn injury assessment. With the continuous progress of imaging technology and medical imaging, medical imaging technologies such as digital radiography, computed tomography, magnetic resonance imaging, and ultrasonography, and so on, have played an important role in burn injury assessment. However, there are still relatively few research reports on burn injury imaging, which may be due to the fact that the imaging techniques and diagnostic experience used for burn injury assessment are not yet fully popular in some medical institutions, and the imaging manifestations related to burns are complex and lack of specificity. This article mainly reviews the application research progress of various imaging techniques in the assessment of burn injury in recent years, aiming to explore the application value of various imaging techniques in burn injury assessment.

Objective: To prospectively compare single-shot (SS) echo-planar imaging (EPI) and field-of-view optimized and constrained undistorted single-shot multiplexed sensitivity-encoding (FOCUS MUSE) for diffusion-weighted imaging (DWI) in evaluating thyroid-associated ophthalmopathy (TAO). Materials and Methods: SS EPI and FOCUS MUSE DWIs were obtained from 39 patients with TAO (18 male; mean ± standard deviation: 48.3 ± 13.3 years) and 26 healthy controls (9 male; mean ± standard deviation: 43.0 ± 18.5 years). Two radiologists scored the visual image quality using a 4-point Likert scale. The image quality score, signal-to-noise ratio (SNR), contrast-tonoise ratio (CNR), and apparent diffusion coefficient (ADC) of extraocular muscles (EOMs) were compared between the two DWIs. Differences in the ADC of EOMs were also evaluated. The performance of discriminating active from inactive TAO was assessed using receiver operating characteristic curves. The correlation between ADC and clinical activity score (CAS) was analyzed using Spearman correlation. Results: Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated significantly higher image quality scores (P < 0.001), a higher SNR and CNR on the lateral rectus muscle (LRM) and medial rectus muscle (MRM) (P < 0.05), and a non-significant difference in the ADC of the LRM and MRM. Active TAO showed higher ADC than inactive TAO and healthy controls with both SS EPI and FOCUS MUSE DWIs (P < 0.001). Inactive TAO and healthy controls did not show a significant ADC difference with both DWIs. Compared with SS EPI DWI, FOCUS MUSE DWI demonstrated better discrimination of active from inactive TAO (AUC:0.925 vs. 0.779; P = 0.007). The ADC was significantly correlated with CAS in SS EPI DWI (r = 0.391, P < 0.001) and FOCUS MUSE DWI (r = 0.645, P < 0.001). Conclusion FOCUS MUSE DWI provides better images for evaluating EOMs and better performance in diagnosing active TAO than SS EPI DWI. The application of FOCUS MUSE will facilitate the DWI evaluation of TAO.

Objective:To synthesize non-contrast-enhanced CT images from enhanced CT images using deep learning method based on convolutional neural network, and to evaluate the similarity between synthesized non-contrast-enhanced CT images by deep learning(DL-SNCT) and plain CT images considered as gold standard subjectively and objectively, as well as to explore their potential clinical value.Methods:Thirty-four patients who underwent conventional plain scan and enhanced CT scan at the same time were enrolled. Using deep learning model, DL-SNCT images were generated from the enhanced CT images for each patient. With plain CT images as gold standard, the image quality of DL-SNCT images was evaluated subjectively. The evaluation indices included anatomical structure clarity, artifacts, noise level, image structure integrity and image deformation using a 4-point system). Paired t-test was used to compare the difference in CT values of different anatomical parts with different hemodynamics (aorta, kidney, liver parenchyma, gluteus maximus) and different liver diseases with distinct enhancement patterns (liver cancer, liver hemangioma, liver metastasis and liver cyst) between DL-SNCT images and plain CT images. Results:In subjective evaluation, the average scores of DL-SNCT images in artifact, noise, image structure integrity and image distortion were all 4 points, which were consistent with those of plain CT images ( P>0.05). However, the average score of anatomical clarity was slightly lower than that of plain CT images (3.59±0.70 vs. 4) with significant difference ( Z = -2.89, P<0.05). For different anatomical parts, the CT values of aorta and kidney in DL-SNCT images were significantly higher than those in plain CT images ( t=-12.89, -9.58, P<0.05). There was no statistical difference in the CT values of liver parenchyma and gluteus maximus between DL-SNCT images and plain CT images ( P>0.05). For liver lesions with different enhancement patterns, the CT values of liver cancer, liver hemangioma and liver metastasis in DL-SNCT images were significantly higher than those in plain CT images( t=-10.84, -3.42, -3.98, P<0.05). There was no statistical difference in the CT values of liver cysts between DL-SNCT iamges and plain CT images ( P>0.05). Conclusions:The DL-SNCT image quality as well as the CT values of some anatomical structures with simple enhancement patterns is comparable to those of plain CT images considered as gold-standard. For those anatomical structures with variable enhancement and those liver lesions with complex enhancement patterns, there is still vast space for DL-SNCT images to be improved before it can be readily used in clinical practice.

Pure autonomic failure (PAF) is an α-synucleinopathy featured by slowly progressive autonomic failure. A patient who presented with orthostatic hypotension associated dizziness and syncope, postprandial hypotension, supine hypertension and anhidrosis, was hospitalized. The patient did not show incontinence, urinary retention, constipation, ataxia, and extrapyramidal symptoms. In combination of the description of the patient′s symptoms with PAF related references, the pathogenesis, clinical manifestations, diagnosis and differential diagnosis of PAF and its relationship with other α-synucleinopathies were demonstrated in this report.

Objective:To investigate the CT features of lepidic predominant adenocarcinoma (LPA) and other pathological subtypes in early-stage invasive pulmonary adenocarcinoma appearing as ground glass nodule (GGN); and to provide imaging-derived information for the clinical management of GGN.Methods:The clinical and CT data of patients with early-stage invasive pulmonary adenocarcinoma in the First Medical Center of PLA General Hospital from January to December 2019 were retrospectively reviewed. All patients presented with pure GGNs or mixed GGNs with a consolidation-to-tumor ratio (CTR)<0.5, with the pathological results confirmed by surgery. GGNs were divided into LPA and non-LPA (n-LPA) groups according to pathological subtypes. Univariate analysis was used to compare the clinical data and CT characteristics between the two groups. The multivariate analysis was performed for the indicators with statistically significant differences and a multivariate model was generated using the reverse elimination method. The area under the ROC curve (AUC) was used to evaluate the discriminatory power of this model for differentiation of LPA from n-LPA.Results:A total of 630 GGNs from 589 patients were analyzed, with 367 GGNs in LPA group and 263 GGNs in n-LPA group. In univariate analysis, the diameter [(14±5) mm], CT value [(-566±98) HU], and CTR [13.9% (0, 27.3%)] in the LPA group were significantly smaller than those in the n-LPA group [(15±5) mm, (-499±111) HU, 27.8%(7.7%, 40%)], respectively, P<0.05]. The frequency of mGGN, deep lobulation sign, burrs, vascular changes, bronchial changes, and clear tumor-lung interface were significantly higher in the n-LPA group than those in the LPA group ( P<0.05). Multivariate analysis results showed that mean CT values, CTR, deep lobulation sign, burr, vascular changes, and bronchial changes were independent predictors for predicting n-LPA ( P<0.05), which were included in the logistic model. Using the optimal cutoff value of 3.958, the logistic regression model for differentiate LPA from n-LPA had a sensitivity of 76.4%, a specificity of 78.7%, and an area under the curve of 0.840. Conclusion:The CT features are helpful for differentiating lepidic predominant subtype from other subtypes in early-stage invasive pulmonary adenocarcinoma presenting as a GGN.

Objective To analyze the relationship between preoperative serum sodium concentration and preoperative status of liver transplantation recipients and it's effect on early prognosis. Methods Retrospectively collected the clinical data of 281 patients underwent liver transplantation in First Affiliated Hospital of Zhengzhou University from January 2016 to September 2017. According to the preoperative serum sodium concentration, they were divided into hyponatremia group (< 130 mmol/L) 18 patients, normonatremia group (130-145 mmol/L)232 patients and hypernatremia group(> 145 mmol/L) 31 patients. The SPSS 21.0 statistical software was used to analyze the difference of preoperative MELD score, Child-Pugh score, postoperative survival rate and the incidence of graft dysfunction among three groups. Multivariate comparisons of measurement data were performed using analysis of variance. Pairwise comparisons between groups were performed using the LSD-t test. Chi-square tests were used to compare the count data sets. Results The preoperative MELD score was(19.27 ±7.35) scores, Child-Pugh score was(10.39±2.28) scores, serum creatinine concentration was(95.89 ± 49.40) μmol/L in hyponatremia group, the preoperative MELD score was(12.17土8.79) scores(P=0.001), Child-Pugh score was(8.50±2.68) scores (P =0.004) and serum creatinine was(66.07 ±24.13) μmol/L(P <0.05) in normonatremia group, the difference between two groups were statistically significant. There were no significant difference in the length of postoperative ICU stay and postoperative hospital stay among the three groups, there were no significant difference between the 30th and 90th postoperative survival rates and the incidence of graft dysfunction. Conclusions Hyponatremia is an indicator of poor preoperative status in liver transplantation recipients. Preoperative serum sodium concentration has no significant effect on early prognosis of liver transplantation.

Objective:To investigate the development status of mental health service of Shanghai mental health agencies,and to provide reference for further strengthening the standardized management of service agencies.Methods:In Shanghai,107 mental health service agencies (including psychiatric institutions,non-specialist medical institutions,non-specialist enterprise and public institutions,social institutions) were selected from Shanghai psychological service industry association.Each person in charge of these agencies was interviewed with an adapted 5 l-item questionnaire.A total of 89 valid questionnaires were collected.In this study,25 items of the questionnaire (belonging to the parts of agency information,mental health service situation and management of mental health service) were selected and analyzed.Results:All the surveyed agencies had been registered.The average age of these agencies for setting up mental health service was 8.9 years,and the average age of psychiatric institutions was the longest (18.6 years).Besides,psychiatric institutions attracted most of the clients (83.9％) in 2015.Among the professionals,only 32.5％ were full time,74.0％ were female,41.5％ were between 31 and 40 years old and 63.8％ were undergraduates.Psychiatrists,psychotherapists,counselors and psychometric person accounted for 78.0％ of the professionals in these agencies.Only 52.3％ of the agencies had full-time management personnel for mental health service.The most used method of assessing the quality of service and staff assessment was to obtain feedback from the client/family members (81.0％) and the assessment of the services (78.8％).Conclusion:The development of mental health service in Shanghai mental health agencies has been more normalized,but there is still a lack of full-time professionals.In addition,there is a lack of unified supervision and management mechanism for professionals.