Objective To explore the feasibility and corresponding implementation methods of constructing a sample resource bank based on individual-level data of completed clinical trials and using it to construct external controls for drug/device clinical trials.Methods Taking the pre-marketing clinical trial of transcatheter active valve replacement(TAVR)for the treatment of aortic valve stenosis as an example,the individual-level databases of multiple trials were standardized to form a sample bank.The original data of any trial in the sample bank were selected as the experimental group,and the remaining samples were selected as the control group.The potential confounding was handled by using the propensity score matching and stratification methods to clarify the process of constructing external controls based on the sample bank of individual-level data of clinical trials.Results This study included individual-level data of single-group trials of 4 TAVR devices,with a total of 569 subjects(59.2%male).The number of subjects in Trials 1 to 4 was 120,120,163,and 166,respectively.Propensity score matching enabled the matching of 113,117,125,and 147 subjects with comparable or similar characteristics from individual-level data from other trials,respectively,demonstrating a high matching success rate.The PS score distribution plot after stratification showed that the proportions of subjects in the experimental and control groups in strata 1 to 5 in scheme 1 were 4/103,11/103,22/92,32/87,and 51/64,respectively.For all constructed external controlled trials,a certain number of control samples with similar baseline characteristics to the experimental groups were distributed within each propensity score stratum.The results of the simulation test also reflected the potential differences between different devices in the 12-month all-cause mortality rate.Conclusions The sample bank constructed with individual-level data from clinical trials,as a high-quality data source,can serve as a source of external control for single-arm trials in the same field,and as a useful supplement to the external control scenario of real-world evidence to support drug and device development.At the same time,targeted research on research methods and bias control measures in related fields is also needed.

The pregnant woman was 30 years old,G2P0.This singleton pregnancy at 22 weeks of gestation was screened for second-trimester ultrasound malformations,suggesting fetal aortic valve atresia,aortic stenosis with reverse blood flow,mitral valve atresia,and markedly enlarged left ventricle,which was considered for the diagnosis of hypoplastic left heart syndrome(HLHS).The pregnancy was terminated at our hospital and subsequently underwent genetic testing with results of heterozygous variants in the NOTCH1 gene,which can cause aortic valve disease type 1.The findings of the fetal autopsy were aortic valve atresia,mitral valve widening and thickening,and left ventricular enlargement with myocardial infarction.This report focuses on the ultrasound characteristics of HLHS with left ventricular enlargement and its hemodynamic changes in order to improve clinicians'understanding of the progressive changes in the disease phenotype of HLHS.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To explore the characteristic changes in white matter microstructure in Parkinson's disease(PD)patients via automated fiber quantification(AFQ)technology,providing a basis for the identification and diagnosis of PD,and to analyze the feasibility of combining the AFQ method with support vector machine(SVM)in the diagnosis of PD.Methods Forty patients with primary PD(PD group)and 20 healthy controls(HC)(HC group)were prospectively selected.The AFQ technology was applied for white matter fiber tract analysis.Statistical analyses were performed using FSL(v6.0)software and SPSS 27.0 software.Independent-sample t-tests were conducted for comparisons between groups in AFQ analysis.The AFQ method was used to analyze the relationship between diffusion tensor imaging(DTI)parameters and Montreal Cognitive Assessment(MoCA)scores.Results(1)The results of AFQ analysis revealed that compared with the HC group,the PD group exhibited significantly lower fractional anisotropy(FA)values in the right cingulum bundle,left cingulum bundle hippocampus,and left uncinate fasciculus,with no differences in the FA values of the remaining 17 fiber tracts.Moreover,PD group demonstrated higher mean diffusivity(MD)values in the left cingulum bundle,left cingulum bundle hippocampus,left inferior frontal occipital fasciculus,left inferior longitudinal fasciculus,left superior longitudinal fasciculus,and left uncinate fasciculus.These differences were statistically significant(P<0.05),while no significant differences were found in the MD values of the remaining 14 fiber tracts.Furthermore,the MD values of the left inferior frontal occipital fasciculus,and left inferior longitudinal fasciculus were negatively correlated with the MoCA scores.(2)The classification results of SVM showed that the best results were achieved when combining the differential nodes of FA and MD as classification features,with an area under the curve(AUC)of 0.922,an accuracy of 84.81%,a sensitivity of 87.50%,and a specificity of 82.05%.Conclusion The DTI parameters in PD patients can serve as potential biomarkers for diagnosis.The AFQ methods provides an effective approach for detecting alterations white matter tract integrity,offering important insights for the identification and diagnosis of PD.The best results are achieved when combining the differential nodes of FA and MD as classification features.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

The pregnant woman was 30 years old,G2P0.This singleton pregnancy at 22 weeks of gestation was screened for second-trimester ultrasound malformations,suggesting fetal aortic valve atresia,aortic stenosis with reverse blood flow,mitral valve atresia,and markedly enlarged left ventricle,which was considered for the diagnosis of hypoplastic left heart syndrome(HLHS).The pregnancy was terminated at our hospital and subsequently underwent genetic testing with results of heterozygous variants in the NOTCH1 gene,which can cause aortic valve disease type 1.The findings of the fetal autopsy were aortic valve atresia,mitral valve widening and thickening,and left ventricular enlargement with myocardial infarction.This report focuses on the ultrasound characteristics of HLHS with left ventricular enlargement and its hemodynamic changes in order to improve clinicians'understanding of the progressive changes in the disease phenotype of HLHS.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.