Objective To comprehensively investigate the subcomponent characteristics of inhibitory control for children with autism spectrum disorder(ASD)aged 4-5 years under experimental and natural environments.Methods Thirty children with ASD aged 4-5 years and 30 chronological-age and intellectual level-matched typically developing children were recruited.The Simon task,Go/nogo task,and Stroop task were used to examine the conflict response inhibition,prepotent response inhibition,and interference control subcomponents of inhibitory control,respectively.The inhibit subscale of the behavior rating inventory of executive function-preschool version was employed to assess children's inhibition in daily natural situations.Results Under the incongruent condition of the Simon task,there were no significant differences in mean reaction time and accuracy between ASD group and the control group(P>0.05).In the Go/nogo task,the ASD group demonstrated elevated false positive errors compared to the controls[3.10%(0,6.20%)vs.0(0,0.78%),P=0.005].However,there were no significant differences in mean reaction time and false alarm error between ASD group and the control group(P>0.05).In the Stroop task,there were no significant differences in the accuracy and mean reaction time between ASD group and the control group(P>0.05).Additionally,the ASD group scored significantly worse than the controls in the inhibit subscale of BRIEF-P[(60.47±9.63)vs.(54.23±7.45),P=0.007].Conclusions The inhibitory control of children with ASD aged 4-5 years are partially impaired in a structural experimental setting while severely deficient in a natural environment.

Objective To analyze the computed tomography(CT)imaging features of urachal carcinoma and evaluate its diagnostic value.Methods The clinical data of 20 patients with urachal carcinoma confirmed by surgery and pathology,who were admitted to The First Affiliated Hospital of Naval Medical University from Dec.2012 to Dec.2022,were collected.Seventeen of the 20 patients underwent enhanced CT urography and 3 underwent pelvic CT plain scan+enhanced scan.After scanning,multiplanar reconstruction was performed on the post-processing workstation.The general data,clinical symptoms,CT imaging findings,pathological data,and prognosis of the patients were analyzed and summarized.Results The patients included 16 males and 4 females,aged 27 to 75 years old,with a median age of 61.50(41.50,71.25)years old.The tumors were all located in the anterior wall of the bladder,along the urachus,with a maximum diameter of 1.72-5.55 cm and a median maximum diameter of 3.34(2.48,3.71)cm.Fourteen cases had cystic-solid lesions and 6 had solid lesions.In the cystic-solid lesions,9 cases showed the"upper cystic and lower solid"sign on the sagittal plane.Calcification was noted in 17 cases.After enhanced scanning,18 cases showed progressive enhancement,and 2 cases showed"fast in and fast out"enhancement.Tumor invasion extended beyond the urachus and/or bladder muscle layer in 19 cases.At the end of follow-up,3 cases had recurrence,2 had metastasis,5 had no recurrence after surgery,3 died,and 7 were lost to follow-up.Conclusion Urachal carcinoma has certain characteristic manifestations on CT imaging.Reconstructing the sagittal plane with enhanced CT scanning and multiplanner reformation can help preoperative diagnosis and prognostic evaluation of urachal carcinoma.

Pancreatic extracorporeal shock wave lithotripsy(P-ESWL),a non-invasive treatment,is widely accepted worldwide as the preferred option for pancreatic duct stone(PDS)treatment.P-ESWL provides significant relief of painful symptoms and improves patients' quality of life through efficient lithotripsy and catheter removal.Although there is risk of post-operative complications such as pancreatitis,the overall incidence is low and can be further minimized by effective management strategies.It is worth noting that computed tomography-based quantitative analysis and radiomics prediction model provide a scientific basis for personalized P-ESWL,heralding more precise and efficient treatment in the future.P-ESWL for treating PDS will be further improved by future multi-center and large-sample studies,as well as by the integration of artificial intelligence and machine learning algorithms,which may lead to significant therapeutic effects and improvements in patients' quality of life.

Objective To explore the correlation between stroke etiology and clinical and imaging features in patients with acute ischemic stroke(AIS)due to large vessel occlusion treated by intravascular thrombectomy.Methods A total of 213 patients with AIS and endovascular embolectomy in our hospital from Oct.2016 to Jun.2018 were enrolled retrospectively.According to the etiological classification criteria of Trial of Org 10172 in Acute Stroke Treatment(TOAST),there were 116 cases of cardioembolism and 97 cases of non-cardioembolism.Multivariate logistic regression analysis was used to screen the clinical and imaging characteristics for identifying cardioembolism and non-cardioembolism.Results Compared with non-cardioembolism AIS,cardioembolism AIS was associated with higher NIHSS scores(adjusted odds ratio[OR]=1.09,95%confidence interval[95%CI]1.01-1.18,P=0.02),atrial fibrillation(adjusted OR=76.46,95%CI 26.75-218.51,P＜0.01),absence of hypertension(adjusted OR=0.32,95%CI 0.12-0.84,P=0.02),antiplatelet drug use(adjusted OR=5.03,95%CI 1.22-20.63,P=0.03),shorter onset-to-puncture time(adjusted OR=0.998,95%CI 0.996-1.000,P=0.04),and presence of hyperdense artery sign(HAS)(adjusted OR=4.45,95%CI 1.47-13.49,P=0.01).Conclusion There are some differences in clinical and imaging characteristics between patients with cardioembolism and non-cardioembolism AIS.The occurrence of HAS suggests a higher probability of cardioembolism in AIS patients.

Objective To identify risk factors for pathological upgrading after radical prostatectomy in patients with biopsy-confirmed International Society of Urological Pathology(ISUP)grade 1 prostate cancer and to develop a predictive nomogram.Methods A total of 256 patients with ISUP grade 1 prostate cancer diagnosed by biopsy and undergoing radical prostatectomy in The First Affiliated Hospital of Naval Medical University between Jan.2017 and May 2024 were retrospectively enrolled.Clinical,imaging,and biopsy data were collected.Independent predictors were identified using univariate and multivariate binary logistic regression,and a nomogram model was constructed.Model performance was evaluated using receiver operating characteristic curve,clinical impact curve,and decision curve analysis.The stability of the model was evaluated by Hosmer-Lemeshow test.Results Multivariate binary logistic regression analysis revealed that the number of positive puncture cores(odds ratio[OR]=1.80),prostate imaging and reporting data system(PI-RADS)score(OR=1.88),and prostate specific antigen density(PSAD)stage(OR=1.43)were independent predictors of pathological upgrading(all P＜0.01).The area under curve(AUC)value of the nomogram model based on the above 3 predictors was 0.82(95％confidence interval 0.77-0.87).Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.01-0.99.Clinical impact curve analysis showed that at a threshold probability of 0.40,the model could avoid 45 unnecessary interventions(12％reduction in false-positive rate)with a net clinical benefit of 0.46.The Hosmer-Lemeshow test indicated good model fit(P=0.45).Conclusion The constructed nomogram model can accurately predict the risk of pathological upgrading after radical prostatectomy in patients with ISUP grade 1 prostate cancer,providing a quantitative tool to support individualized decision-making for active surveillance.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Objective:A prognosis prediction model for pancreatic cancer was constructed based on multi-scale radiomics combined with deep learning, and the prediction effect of the model was evaluated.Methods:A retrospective analysis was conducted on the clinical data of 215 patients who underwent radical resection of pancreatic cancer at the First Affiliated Hospital of Naval Medical University from January 2017 to December 2017. Among them, 134 were male and 81 were female, with an age of (61.9±9.2) years. Patients were randomly divided into the training set ( n=151) and the test set ( n=64) in a ratio of 7: 3. Habitat features, peritumoral radiomics features, 3D radiomics features, and 2.5D deep learning features were extracted from preoperative CT images respectively. After feature screening, a survival prediction model was constructed using the CoxBoost machine learning algorithm that integrated the Boosting algorithm and the Cox proportional hazards model. The performance of the model was evaluated using the area under the time-dependent receiver operating characteristic curve and the consistency index. The clinical benefits of the model were evaluated using decision curve analysis. The survival curves were plotted using the Kaplan-Meier method, and the log-rank test was used for the comparison of survivals between groups. Results:The LASSO, random forest and extreme gradient boosting models were each used to screen out the top 10 most important features and take the union, ultimately obtaining 20 radiomics features for modeling. In the training set and test set, the consistency index of the CoxBoost model in predicting overall survival was 0.717 (95% CI: 0.669-0.765) and 0.688 (95% CI: 0.610-0.766), respectively, and the area under the curve for predicting overall survival at 1, 2, and 3 years after surgery was 0.830 (95% CI: 0.752-0.898), 0.753 (95% CI: 0.665-0.833), 0.828 (95% CI: 0.735-0.908) and 0.690 (95% CI: 0.549-0.824), 0.780 (95% CI: 0.649-0.887 and 0.793 (95% CI: 0.660-0.897), respectively. The area under the curve for predicting long-term survival after surgery (≥40 months) was above 0.8. Based on the optimal cutoff value of -0.19 for the predicted value of the CoxBoost model calculated by the R package " survminer", the patients were divided into high-risk (predicted value >-0.19) and low-risk (predicted value <-0.19) groups. In both the training set and the test set, the survival of patients in the low-risk group was better than that in the high-risk group (training set: χ2=39.01, P<0.001; test set: χ2=12.34, P<0.001). The median survival period of patients in the high-risk group was lower than that in the low-risk group (training set: 15.80 vs 34.07 months; test set: 16.87 vs 43.07; months). Decision curve analysis shows that patients obtain survival benefit when the threshold probability of the training set is greater than 0.25 and that of the test set is greater than 0.45. Conclusion:The CoxBoost model has a good predictive ability for the overall survival of pancreatic cancer patients after surgery and can effectively screen out patient subgroups that may significantly benefit from surgical treatment.

Objective To comprehensively investigate the subcomponent characteristics of inhibitory control for children with autism spectrum disorder(ASD)aged 4-5 years under experimental and natural environments.Methods Thirty children with ASD aged 4-5 years and 30 chronological-age and intellectual level-matched typically developing children were recruited.The Simon task,Go/nogo task,and Stroop task were used to examine the conflict response inhibition,prepotent response inhibition,and interference control subcomponents of inhibitory control,respectively.The inhibit subscale of the behavior rating inventory of executive function-preschool version was employed to assess children's inhibition in daily natural situations.Results Under the incongruent condition of the Simon task,there were no significant differences in mean reaction time and accuracy between ASD group and the control group(P>0.05).In the Go/nogo task,the ASD group demonstrated elevated false positive errors compared to the controls[3.10%(0,6.20%)vs.0(0,0.78%),P=0.005].However,there were no significant differences in mean reaction time and false alarm error between ASD group and the control group(P>0.05).In the Stroop task,there were no significant differences in the accuracy and mean reaction time between ASD group and the control group(P>0.05).Additionally,the ASD group scored significantly worse than the controls in the inhibit subscale of BRIEF-P[(60.47±9.63)vs.(54.23±7.45),P=0.007].Conclusions The inhibitory control of children with ASD aged 4-5 years are partially impaired in a structural experimental setting while severely deficient in a natural environment.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Objective:A prognosis prediction model for pancreatic cancer was constructed based on multi-scale radiomics combined with deep learning, and the prediction effect of the model was evaluated.Methods:A retrospective analysis was conducted on the clinical data of 215 patients who underwent radical resection of pancreatic cancer at the First Affiliated Hospital of Naval Medical University from January 2017 to December 2017. Among them, 134 were male and 81 were female, with an age of (61.9±9.2) years. Patients were randomly divided into the training set ( n=151) and the test set ( n=64) in a ratio of 7: 3. Habitat features, peritumoral radiomics features, 3D radiomics features, and 2.5D deep learning features were extracted from preoperative CT images respectively. After feature screening, a survival prediction model was constructed using the CoxBoost machine learning algorithm that integrated the Boosting algorithm and the Cox proportional hazards model. The performance of the model was evaluated using the area under the time-dependent receiver operating characteristic curve and the consistency index. The clinical benefits of the model were evaluated using decision curve analysis. The survival curves were plotted using the Kaplan-Meier method, and the log-rank test was used for the comparison of survivals between groups. Results:The LASSO, random forest and extreme gradient boosting models were each used to screen out the top 10 most important features and take the union, ultimately obtaining 20 radiomics features for modeling. In the training set and test set, the consistency index of the CoxBoost model in predicting overall survival was 0.717 (95% CI: 0.669-0.765) and 0.688 (95% CI: 0.610-0.766), respectively, and the area under the curve for predicting overall survival at 1, 2, and 3 years after surgery was 0.830 (95% CI: 0.752-0.898), 0.753 (95% CI: 0.665-0.833), 0.828 (95% CI: 0.735-0.908) and 0.690 (95% CI: 0.549-0.824), 0.780 (95% CI: 0.649-0.887 and 0.793 (95% CI: 0.660-0.897), respectively. The area under the curve for predicting long-term survival after surgery (≥40 months) was above 0.8. Based on the optimal cutoff value of -0.19 for the predicted value of the CoxBoost model calculated by the R package " survminer", the patients were divided into high-risk (predicted value >-0.19) and low-risk (predicted value <-0.19) groups. In both the training set and the test set, the survival of patients in the low-risk group was better than that in the high-risk group (training set: χ2=39.01, P<0.001; test set: χ2=12.34, P<0.001). The median survival period of patients in the high-risk group was lower than that in the low-risk group (training set: 15.80 vs 34.07 months; test set: 16.87 vs 43.07; months). Decision curve analysis shows that patients obtain survival benefit when the threshold probability of the training set is greater than 0.25 and that of the test set is greater than 0.45. Conclusion:The CoxBoost model has a good predictive ability for the overall survival of pancreatic cancer patients after surgery and can effectively screen out patient subgroups that may significantly benefit from surgical treatment.