Objective To observe the effect of pre-hospital and intra-hospital collaborative ther-apeutic model in the treatment of patients with acute cerebral infarction.Methods A total of 67 pa-tients with acute cerebral infarction were selected as the research objects,and they were divided into observation group(n=37)and control group(n=30)according to the therapeutic model.The con-trol group was conducted with the traditional therapeutic model,while the observation group was con-ducted with the pre-hospital and intra-hospital collaborative therapeutic model.The time efficiency of intravenous thrombolysis,early recovery of nerve function and oxidative stress indexes were compared between the two groups.Results There was no significant difference in the time from onset to visit between the two groups(P＞0.05);the time from seeing a doctor to thrombolysis and the time from seeing a doctor to signing the informed consent for intravenous thrombolysis in the observation group were significantly shorter than those in the control group(P＜0.05).On the hospital admission,there was no significant difference in the National Institutes of Health Stroke Scale(NIHSS)score be-tween the two groups(P＞0.05);at the time points of 7 days after admission and 90 days after thrombolysis,the NIHSS scores of patients in the observation group were significantly lower than that in the control group(P＜0.05).There were no significant differences in the levels of serum glutathione peroxidase(GSH-Px)and malonaldehyde(MDA)between the two groups(P＞0.05);the level of serum superoxide dismutase(SOD)in the observation group was significantly higher than that in the control group(P＜0.05).One patient died in the control group,with a mortality rate of 3.33％;no patient died in the observation group.Conclusion Pre-hospital and intra-hospital collaborative therapeutic model can effectively improve the time efficiency of intravenous thrombolysis for patients with acute cerebral infarction,alleviate the neurological damage,and reduce degree of oxidative stress reaction and death risk.

Objective To observe the effect of pre-hospital and intra-hospital collaborative ther-apeutic model in the treatment of patients with acute cerebral infarction.Methods A total of 67 pa-tients with acute cerebral infarction were selected as the research objects,and they were divided into observation group(n=37)and control group(n=30)according to the therapeutic model.The con-trol group was conducted with the traditional therapeutic model,while the observation group was con-ducted with the pre-hospital and intra-hospital collaborative therapeutic model.The time efficiency of intravenous thrombolysis,early recovery of nerve function and oxidative stress indexes were compared between the two groups.Results There was no significant difference in the time from onset to visit between the two groups(P＞0.05);the time from seeing a doctor to thrombolysis and the time from seeing a doctor to signing the informed consent for intravenous thrombolysis in the observation group were significantly shorter than those in the control group(P＜0.05).On the hospital admission,there was no significant difference in the National Institutes of Health Stroke Scale(NIHSS)score be-tween the two groups(P＞0.05);at the time points of 7 days after admission and 90 days after thrombolysis,the NIHSS scores of patients in the observation group were significantly lower than that in the control group(P＜0.05).There were no significant differences in the levels of serum glutathione peroxidase(GSH-Px)and malonaldehyde(MDA)between the two groups(P＞0.05);the level of serum superoxide dismutase(SOD)in the observation group was significantly higher than that in the control group(P＜0.05).One patient died in the control group,with a mortality rate of 3.33％;no patient died in the observation group.Conclusion Pre-hospital and intra-hospital collaborative therapeutic model can effectively improve the time efficiency of intravenous thrombolysis for patients with acute cerebral infarction,alleviate the neurological damage,and reduce degree of oxidative stress reaction and death risk.

Postzygotic mutations are acquired in normal tissues throughout an individual's lifetime and hold clues for identifying mutagenic factors.Here,we investigated postzygotic mutation spectra of healthy individuals using optimized ultra-deep exome sequencing of the time-series samples from the same volunteer as well as the samples from different individuals.In blood,sperm,and muscle cells,we resolved three common types of mutational signatures.Signatures A and B represent clock-like mutational processes,and the polymorphisms of epigenetic regulation genes influence the pro-portion of signature B in mutation profiles.Notably,signature C,characterized by C＞T transitions at GpCpN sites,tends to be a feature of diverse normal tissues.Mutations of this type are likely to occur early during embryonic development,supported by their relatively high allelic frequencies,presence in multiple tissues,and decrease in occurrence with age.Almost none of the public datasets for tumors feature this signature,except for 19.6％of samples of clear cell renal cell carcinoma with increased activation of the hypoxia-inducible factor 1(HIF-1)signaling pathway.Moreover,the accumulation of signature C in the mutation profile was accelerated in a human embryonic stem cell line with drug-induced activation of HIF-1α.Thus,embryonic hypoxia may explain this novel signature across multiple normal tissues.Our study suggests that hypoxic condition in an early stage of embryonic development is a crucial factor inducing C＞T transitions at GpCpN sites;and indi-viduals'genetic background may also influence their postzygotic mutation profiles.

Objective: To establish radiomics models based on different CT scaning phases to distinguish mediastinal metastatic lymph nodes in NSCLC and to explore the diagnostic efficacy of these models. Methods: The CT images of 86 preoperative patients with NSCLC who were performed both plain and enhanced CT scans were analyzed retrospectively. The 231 mediastinal lymph nodes were enrolled in this study which were divided into two independent cohorts: 163 lymph nodes enrolled from January 2015 to June 2017 constituted the training cohort, and 68 lymph nodes enrolled from July 2017 to June 2018 constituted the validation cohort. The regions of interest (ROIs) were delineated on plain scan phase, arterial phase and venous phase CT images respectively, and 841 features were extracted from each ROI. LASSO-logistic regression analysis was used to select features and develop models. The area under the ROC curve (AUC value), sensitivity, specificity, accuracy, positive predictive value and negative predictive value of different models for distinguishing metastatic lymph nodes were compared. Results: A total of 6 models were established, and the AUC values were all greater than 0.800. The plain CT model yielded the highest AUC, specificity, accuracy and positive predictive value with 0.926, 0.860, 0.871, 0.906 in the training cohort and 0.925, 0.769, 0.882, 0.870 in the validation cohort. When plain and venous phase CT images were combined with arterial phase CT images, the sensitivity and negative predictive value of the models increased from 0.879, 0.821 and 0.919, 0.789 to 0.949, 0.878 and 0.979, 0.900 respectively. Conclusions The CT radiomics model could be used to assist the clinical diagnosis of lymph nodes. The AUC value of the model based on plain scanning was the highest, while the sensitivity and negative predictive value of the model could be improved by combining the arterial phase CT images.

Objective:To provide a feasible method for the evaluation of cardiac function based on cardiac gated 4DCT, the radiomics technology combined with enhanced ECG gated 4DCT images were used to quantitatively analyze the changes of left ventricular CT radiomics characteristics in cardiac cycle.Methods:The enhanced ECG 4DCT images of 14 patients were reconstructed at intervals of 5% of cardiac cycle. The left ventricular muscle (LVM) and the contrast agent well filled area of left ventricular were delineated with a 13 mm diameter sphere (Cardiac Region of Interest, cardiac ROI) in a single phase. 3Dslicer software was used to extract 92 features of all the sketches, analyze the distribution of CT values on the cardiac ROI and LVM, and preliminarily screen the stable features based on the cardiac ROI (one-way ANOVA). The stable features were used to further screen LVM (one-way ANOVA) to get the difference features. Wilcoxon rank sum test was used to analyze the change of characteristics with heartbeat in the heartbeat cycle.Results:In the heartbeat cycle the mean CT values of cardiac cavity ROI in cardiac cavity changed less than that in LVM, with the change rates of 9.23% and 17.88%, respectively. There were 36 stable features with no significant difference in cardiac cavity ROI ( P>0.05). 20 of them were statistically significant ( F=1.641-6.206, P<0.05), and the average change rate was 98.63%, such as median (-103.96%) and mean (123.67%) of the first order matrix, gray level non uniformity (99.81%) of GLDM matrix and other changes reached more than 99%. The differences between the maximum and minimum values in different cardiac cycles were statistically significant ( Z=-3.921--3.173, P<0.05). Conclusions:With the combination of radiomics and enhanced ECG 4DCT image, the microscopic changes of CT image features in the cardiac cycle can be amplifed. A new method for the assessment of left ventricular function changes was provided. The features such as median, mean may have more application potential.

Objective: To explore the ability of computed-tomography (CT) radiomic features to predict the Epidermal growth factor receptor (EGFR) mutation status and the therapeutic response of advanced lung adenocarcinoma to EGFR- Tyrosine kinase inhibitors (TKIs) treatment. Methods: A retrospective analysis was performed on 253 patients diagnosed as advanced lung adenocarcinoma, who underwent EGFR mutation detection, and those with EGFR sensitive mutation were treated with TKIs. Using the Lasso regression model and the 10 fold cross-validation method, the radiomic features of predicted EGFR mutation status and the screening of TKIs for sensitive populations were obtained. 715 radiomic features were extracted from unenhanced, arterial phase and venous phase, respectively. Results: The area under curve (AUC) values of the multi-phases including unenhanced, arterial phase and venous phase of the EGFR mutation status validation group were 0.763, 0.807 and 0.808, respectively. The number of radiomic features extracted from the multi-phases were 5, 18 and 23, respectively, which could distinguish the EGFR mutation status. The AUC values of the multi-phases of the EGFR-TKIs sensitive validation group were 0.730, 0.833 and 0.895, respectively. The number of radiomic features extracted from the multi-phases were 3, 7 and 22, respectively, which can be used to screen the superior population for TKIs treatment. The efficiency of radiomic features extracted from venous phase in predicting EGFR mutant status and EGFR-TKIs sensitivity was significantly superior than those of unenhanced and arterial phase. Conclusions The radiomic features of CT scanning can be used as the radiomics biomarker to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population in TKIs therapy, which provides the basis for targeted therapy.

Objective To explore the ability of computed?tomography ( CT) radiomic features to predict the Epidermal growth factor receptor ( EGFR ) mutation status and the therapeutic response of advanced lung adenocarcinoma to EGFR? Tyrosine kinase inhibitors ( TKIs ) treatment. Methods A retrospective analysis was performed on 253 patients diagnosed as advanced lung adenocarcinoma, who underwent EGFR mutation detection, and those with EGFR sensitive mutation were treated with TKIs. Using the Lasso regression model and the 10 fold cross?validation method, the radiomic features of predicted EGFR mutation status and the screening of TKIs for sensitive populations were obtained.715 radiomic features were extracted from unenhanced, arterial phase and venous phase, respectively. Results The area under curve (AUC) values of the multi?phases including unenhanced, arterial phase and venous phase of the EGFR mutation status validation group were 0.763, 0.807 and 0.808, respectively. The number of radiomic features extracted from the multi?phases were 5, 18 and 23, respectively, which could distinguish the EGFR mutation status. The AUC values of the multi?phases of the EGFR?TKIs sensitive validation group were 0.730, 0.833 and 0.895, respectively. The number of radiomic features extracted from the multi?phases were 3, 7 and 22, respectively, which can be used to screen the superior population for TKIs treatment. The efficiency of radiomic features extracted from venous phase in predicting EGFR mutant status and EGFR?TKIs sensitivity was significantly superior than those of unenhanced and arterial phase. Conclusions The radiomic features of CT scanning can be used as the radiomics biomarker to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population in TKIs therapy, which provides the basis for targeted therapy.

Objective To explore the ability of computed?tomography ( CT) radiomic features to predict the Epidermal growth factor receptor ( EGFR ) mutation status and the therapeutic response of advanced lung adenocarcinoma to EGFR? Tyrosine kinase inhibitors ( TKIs ) treatment. Methods A retrospective analysis was performed on 253 patients diagnosed as advanced lung adenocarcinoma, who underwent EGFR mutation detection, and those with EGFR sensitive mutation were treated with TKIs. Using the Lasso regression model and the 10 fold cross?validation method, the radiomic features of predicted EGFR mutation status and the screening of TKIs for sensitive populations were obtained.715 radiomic features were extracted from unenhanced, arterial phase and venous phase, respectively. Results The area under curve (AUC) values of the multi?phases including unenhanced, arterial phase and venous phase of the EGFR mutation status validation group were 0.763, 0.807 and 0.808, respectively. The number of radiomic features extracted from the multi?phases were 5, 18 and 23, respectively, which could distinguish the EGFR mutation status. The AUC values of the multi?phases of the EGFR?TKIs sensitive validation group were 0.730, 0.833 and 0.895, respectively. The number of radiomic features extracted from the multi?phases were 3, 7 and 22, respectively, which can be used to screen the superior population for TKIs treatment. The efficiency of radiomic features extracted from venous phase in predicting EGFR mutant status and EGFR?TKIs sensitivity was significantly superior than those of unenhanced and arterial phase. Conclusions The radiomic features of CT scanning can be used as the radiomics biomarker to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population in TKIs therapy, which provides the basis for targeted therapy.

Radiomics has played an irreplaceable role along with the development of precision medicine. In the field of radiomics researches,the stability of imaging features is of vital significance,which is directly linked to the modeling analysis. In this review,we summarized the recent research progress on the reproducibility problems in four crucial steps of the standard workflow of radiomics including imaging acquisition and reconstruction, region of interest(ROI)segmentation, imaging feature extraction and modeling establishment. In addition,the commonly used software related to radiomics was briefly introduced.

Objective To investigate the changes of the parameters related to planning and re-planning CT imaging features in lung cancer patients presenting with radiation pneumonitis ( RP) by using radiomics technique,and identify the parameters intimately related to the incidence of RP. Methods A total of 31 lung cancer patients who were diagnosed with grade ≥ 2 RP after receiving radiation therapy were selected in this study. For each patient, planning CT images before radiation therapy and re-planning CT images after 40 Gy radiation therapy were obtained. The affected and contralateral lungs were considered as the region of interest (ROI).After the automatic segmentation of normal lung tissues,the parameters related to radiomics features were extracted from ROI by using radiomics software. The differences of these parameters between planning and re-planning CT images were statistically compared. Results ( 1 ) For unilateral lung within each time interval,86 parameters related to radiomics features were extracted; ( 2) Twenty-two parameters significantly differed between the affected and contralateral lungs prior to radiotherapy;(3) Twelve parameters significantly differed between the affected and contralateral lungs on re-planning CT images;(4) Twenty-eight parameters significantly differed in the affected lung before and after radiation therapy;(5) Twenty-eight parameters significantly differed in the contralateral lung before and after radiation therapy. Conclusions The CT imaging radiomics features significantly differ between planning and re-planning CT scan in partial lung cancer patients presenting with RP.Monitoring the dynamic changes of these parameters plays a potential role in predicting the incidence of RP.