Objective:To investigate the predictive value of computed tomography(CT) based radiomics model for the prognosis of patients with pancreatic ductal adenocarcinoma(PDAC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 206 PDAC patients who were admitted to Zhongshan Hospital of Fudan University from August 2018 to December 2020 were collected. There were 115 males and 91 females, aged (64±9)years. All 206 pati-ents underwent enhanced CT examination. Based on radom number table, the 206 patients were randomly divided into a training set of 165 cases and a validation set of 41 cases with a ratio of 4∶1. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) follow-up; (2) analysis of prognostic factors of PDAC patients in the training set; (3) construction and evaluation of prediction model for prognosis of PDAC patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Wilcoxon W test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. The Kaplan-Meier method was used to calculate the survival rate and Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX regression model. The PyCharm software was used for the least absolute shrinkage and selection operator method (LASSO)-COX regression analysis. The receiver operating characteristic curve was plotted to evaluate the performance of radiomics model. Results:(1)Follow-up. Of the 206 patients,205 cases were followed up for 17.1(range, 12.0?40.1)months. The postoperative 1-, 2-, 3-year survival rates were 80.10%, 29.61% and 4.85%. (2) Analysis of prognostic factors for PDAC patients in the training dataset. Results of multivariate analysis showed that pathological N stage was an independent influencing factor for prognosis of PDAC patients in the training set ( hazard ratio=1.476, 95% confidence interval as 1.054?2.067, P<0.05). (3) Construction and evaluation of prediction model for prognosis of PDAC patients. A total of 1 595 radiomics features were finally extracted from the 206 patients. By intra-group feature selection and dimensionality reduction using LASSO-COX regression model, 10 radiomics features were obtained. Combined with 10 radiomics features and 11 clinical features, using the LASSO-COX regression analysis, 15 features were finally extracted to construct the CT based radiomics model for predicting prognosis of PDAC. The areas under receiver operating characteristic curve of the prediction model in predicting 2-year and 3-year overall survival rates of PDAC patients in the training set were 0.834 (95% confidence interval as 0.777?0.891) and 0.883 (95% confidence interval as 0.834?0.932), respectively. The area under curve of the prediction model for patients in the validation set was 0.606 (95% confidence interval as 0.456?0.756) and 0.625 (95% confidence interval as 0.477?0.773). Conclusion:The prediction model constructed on CT based radiomics features and clinical features for predicting the prognosis of PDAC patients shows a promising prediction efficiency.

Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective To observe the value of 2D SECara-Net and 3D U2-Net models constructed based on 2D maximal intensity projection(MIP)and 3D time-of-flight MR angiography(3D TOF-MRA)images,respectively,also of their combination for MRA detecting unruptured saccular intracranial aneurysms(USIA).Methods Totally 973 patients with single USIA and 300 subjects who underwent healthy physical examination were retrospectively collected and divided into training set(n=923,containing 723 cases of USIA and 200 healthy subjects)and test set(n=350,containing 250 cases of USIA and 100 healthy subjects)at the ratio of 7:3.Pre-processed 3D TOF-MRA and the obtained 2D-MIP images in training set were imported into 3D U2-Net and 2D SECara-Net models for training and adjusting parameters,respectively.The efficiency of 2 models and their combination for detecting USIA were evaluated.Results The sensitivity,specificity and accuracy of 2D SECara-Net model for detecting USIA in test set was 78.80％(197/250),95.00％(95/100)and 83.43％(292/350),of 3D U2-Net model was 82.80％(207/250),86.00％(86/100)and 83.71％(293/350),respectively.The specificity of 2D SECara-Net model was higher than that of 3D U2-Net model(P=0.030),while no significant difference of sensitivity nor accuracy was found between 2 models(both P＞0.05).The specificity of the combination of the 2 models was 99.00％(99/100),higher than that of 3D U2-Net model(P＜0.05),and the sensitivity and accuracy of the combination was 91.20％(228/250)and 93.43％(327/350),respectivelty,both higher than those of 2 single models(all P＜0.05).Conclusion 2D SECara-Net and 3D U2-Net models had similar,sensitivity and accuracy for MRA detecting USIA.Combination of them could improve the detecting efficacy.

Objective:To investigate the predictive value of computed tomography(CT) based radiomics model for the prognosis of patients with pancreatic ductal adenocarcinoma(PDAC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 206 PDAC patients who were admitted to Zhongshan Hospital of Fudan University from August 2018 to December 2020 were collected. There were 115 males and 91 females, aged (64±9)years. All 206 pati-ents underwent enhanced CT examination. Based on radom number table, the 206 patients were randomly divided into a training set of 165 cases and a validation set of 41 cases with a ratio of 4∶1. The training set was used to construct the prediction model, and the test set was used to validate the performance of the prediction model. Observation indicators: (1) follow-up; (2) analysis of prognostic factors of PDAC patients in the training set; (3) construction and evaluation of prediction model for prognosis of PDAC patients. Comparison of measurement data with normal distribution between groups was conducted using the t test. Comparison of measurement data with skewed distribution between groups was conducted using the Wilcoxon W test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. The Kaplan-Meier method was used to calculate the survival rate and Log-rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX regression model. The PyCharm software was used for the least absolute shrinkage and selection operator method (LASSO)-COX regression analysis. The receiver operating characteristic curve was plotted to evaluate the performance of radiomics model. Results:(1)Follow-up. Of the 206 patients,205 cases were followed up for 17.1(range, 12.0?40.1)months. The postoperative 1-, 2-, 3-year survival rates were 80.10%, 29.61% and 4.85%. (2) Analysis of prognostic factors for PDAC patients in the training dataset. Results of multivariate analysis showed that pathological N stage was an independent influencing factor for prognosis of PDAC patients in the training set ( hazard ratio=1.476, 95% confidence interval as 1.054?2.067, P<0.05). (3) Construction and evaluation of prediction model for prognosis of PDAC patients. A total of 1 595 radiomics features were finally extracted from the 206 patients. By intra-group feature selection and dimensionality reduction using LASSO-COX regression model, 10 radiomics features were obtained. Combined with 10 radiomics features and 11 clinical features, using the LASSO-COX regression analysis, 15 features were finally extracted to construct the CT based radiomics model for predicting prognosis of PDAC. The areas under receiver operating characteristic curve of the prediction model in predicting 2-year and 3-year overall survival rates of PDAC patients in the training set were 0.834 (95% confidence interval as 0.777?0.891) and 0.883 (95% confidence interval as 0.834?0.932), respectively. The area under curve of the prediction model for patients in the validation set was 0.606 (95% confidence interval as 0.456?0.756) and 0.625 (95% confidence interval as 0.477?0.773). Conclusion:The prediction model constructed on CT based radiomics features and clinical features for predicting the prognosis of PDAC patients shows a promising prediction efficiency.

This article reports a case of adult neuronal intranuclear inclusion body disease with clinical manifestations of tremor,cognitive decline,and binocular visual impairment,which has not been clearly diagnosed and treated before.By reporting on this case,we aim to enhance physicians'understanding of adult onset of neuronuclear inclusion body disease,and to improve the diagnostic rate of neuronuclear inclusion body disease through imaging,skin biopsy,and NOTCH2NLC gene.

Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.
Algorithms ; Clothing ; Electrocardiography ; Equipment Design ; Female ; Humans ; Internet ; Machine Learning ; Maternal Health ; Monitoring, Ambulatory ; instrumentation ; Telemedicine ; instrumentation