Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Objective To investigate the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Objective:To analyze the vascular distribution of palmar digital arteries in syndactyly through color Doppler ultrasound angiography and make the classification according to the level of artery bifurcation points, discusse its clinical significance.Methods:A retrospective analysis was performed for the data of children with congenital and digital malformations admitted to Wuxi Ninth People’s Hospital from January 2017 to December 2022. Preoperative vascular ultrasonography of the hand was performed to locate the bifurcation point of the common palmar digital artery in the digital webbed area. The vascular conditions were classified according to the distance between the bifurcation point and the 1/3 point of the proximal phalangeal as the reference, such as normal type (<0 mm), mild reduction type (0-5 mm), moderate decrease type (6-10 mm), and severe reduction type (>10 mm), absent type (only 1 artery between the fingers). The total variation of the bifurcation point of the common palmar digital artery in the children, as well as the proportion of different vascular condition subtypes were recorded. According to the classification of blood vessels, the corresponding operation plan was made. The positioning accuracy of color Doppler ultrasound angiography was evaluated based on the gold standard of common palmar digital artery bifurcation points detected during operation. Complications such as postoperative infection, flap necrosis, finger vasospasm and the morphology of finger web at the last follow-up were observed. Descriptive statistical analysis was used.Results:A total of 123 children with 181 finger webs were enrolled, including 72 males and 51 females, aged 10-28 months. There were 55 cases of simple type, 36 cases of complex type (bone) and 32 cases of Poland syndrome. Color Doppler ultrasound angiography showed that the total variation rate of the common palmar digital arterial bifurcation point was 24.3% (44/181), of which 11.6% (21/181) were mildly reduced type, 8.3% (15/181) were moderately reduced type, 3.3% (6/181) were severe reduced type, and 1.1% (2/181) were absent type. The vascular variation rates of simple syndactyly, bony syndactyly, and Poland syndrome were 0 (0/55), 19.5% (7/36), and 41.1% (37/90), respectively. Compared with the actual situation during surgery, the positioning accuracy of color Doppler ultrasound angiography was 100%. All pediatric surgeries were successfully completed, and the flaps and skin grafts survived smoothly. There were no postoperative complications such as infection, vascular crisis, or finger necrosis. All patients were followed up for 6 to 27 months, with an average of 13.5 months. At the last follow-up, the depth, width, and slope of the fingerwebs were all close to normal.Conclusion:Congenital syndactyly has a high variation rate at the bifurcation point of the common palmar digital artery, and bony syndactyly and Poland syndrome refer to the need to improve preoperative vascular evaluation. Color Doppler ultrasound angiography has the advantages of no radiation, high sensitivity and low price, and the vascular typing based on color Doppler can improve the reliable basis for the formulation of clinical surgical plans.

Periarticular fracture of the shoulder is a common type of fractures in the elderly. Postoperative adverse events such as internal fixation failure, humeral head ischemic necrosis and upper limb dysfunction occur frequently, which seriously endangers the exercise and health of the elderly. Compared with the fracture with normal bone mass, the osteoporotic periarticular fracture of the shoulder is complicated with slow healing and poor rehabilitation, so the clinical management becomes more difficult. At present, there is no targeted guideline or consensus for this type of fracture in China. In such context, experts from Youth Osteoporosis Group of Chinese Orthopedic Association, Orthopedic Expert Committee of Geriatrics Branch of Chinese Association of Gerontology and Geriatrics, Osteoporosis Group of Youth Committee of Chinese Association of Orthopedic Surgeons and Osteoporosis Committee of Shanghai Association of Chinese Integrative Medicine developed the Chinese expert consensus on the diagnosis and treatment of osteoporotic periarticular fracture of the shoulder in the elderly ( version 2023). Nine recommendations were put forward from the aspects of diagnosis, treatment strategies and rehabilitation of osteoporotic periarticular fracture of the shoulder, hoping to promote the standardized, systematic and personalized diagnosis and treatment concept and improve functional outcomes and quality of life in elderly patients with osteoporotic periarticular fracture of the shoulder.

Objective: Recent studies have highlighted the active and potential role of perivascular adipose tissue (PVAT) in atherosclerosis and aneurysm progression, respectively. This study explored the link between PVAT attenuation and abdominal aortic aneurysm (AAA) progression using computed tomography angiography (CTA). Materials and Methods: This multicenter retrospective study analyzed patients with AAA who underwent CTA at baseline and follow-up between March 2015 and July 2022. The following parameters were obtained: maximum diameter and total volume of the AAA, presence or absence of intraluminal thrombus (ILT), maximum diameter and volume of the ILT, and PVAT attenuation of the aortic aneurysm at baseline CTA. PVAT attenuation was divided into high (> -73.4 Hounsfield units [HU]) and low (≤ -73.4 HU). Patients who had or did not have AAA progression during the follow-up, defined as an increase in the aneurysm volume > 10 mL from baseline, were identified. Kaplan–Meier and multivariable Cox regression analyses were used to investigate the association between PVAT attenuation and AAA progression. Results: Our study included 167 participants (148 males; median age: 70.0 years; interquartile range: 63.0–76.0 years), of which 145 (86.8%) were diagnosed with AAA accompanied by ILT. Over a median period of 11.3 months (range: 6.0–85.0 months), AAA progression was observed in 67 patients (40.1%). Multivariable Cox regression analysis indicated that high baseline PVAT attenuation (adjusted hazard ratio [aHR] = 2.23; 95% confidence interval [CI], 1.16–4.32; P = 0.017) was independently associated with AAA progression. This association was demonstrated within the patients of AAA with ILT subcohort, where a high baseline PVAT attenuation (aHR = 2.23; 95% CI, 1.08–4.60; P = 0.030) was consistently independently associated with AAA progression. Conclusion Elevated PVAT attenuation is independently associated with AAA progression, including patients of AAA with ILT, suggesting the potential of PVAT attenuation as a predictive imaging marker for AAA expansion.

Objective:To analyze the vascular distribution of palmar digital arteries in syndactyly through color Doppler ultrasound angiography and make the classification according to the level of artery bifurcation points, discusse its clinical significance.Methods:A retrospective analysis was performed for the data of children with congenital and digital malformations admitted to Wuxi Ninth People’s Hospital from January 2017 to December 2022. Preoperative vascular ultrasonography of the hand was performed to locate the bifurcation point of the common palmar digital artery in the digital webbed area. The vascular conditions were classified according to the distance between the bifurcation point and the 1/3 point of the proximal phalangeal as the reference, such as normal type (<0 mm), mild reduction type (0-5 mm), moderate decrease type (6-10 mm), and severe reduction type (>10 mm), absent type (only 1 artery between the fingers). The total variation of the bifurcation point of the common palmar digital artery in the children, as well as the proportion of different vascular condition subtypes were recorded. According to the classification of blood vessels, the corresponding operation plan was made. The positioning accuracy of color Doppler ultrasound angiography was evaluated based on the gold standard of common palmar digital artery bifurcation points detected during operation. Complications such as postoperative infection, flap necrosis, finger vasospasm and the morphology of finger web at the last follow-up were observed. Descriptive statistical analysis was used.Results:A total of 123 children with 181 finger webs were enrolled, including 72 males and 51 females, aged 10-28 months. There were 55 cases of simple type, 36 cases of complex type (bone) and 32 cases of Poland syndrome. Color Doppler ultrasound angiography showed that the total variation rate of the common palmar digital arterial bifurcation point was 24.3% (44/181), of which 11.6% (21/181) were mildly reduced type, 8.3% (15/181) were moderately reduced type, 3.3% (6/181) were severe reduced type, and 1.1% (2/181) were absent type. The vascular variation rates of simple syndactyly, bony syndactyly, and Poland syndrome were 0 (0/55), 19.5% (7/36), and 41.1% (37/90), respectively. Compared with the actual situation during surgery, the positioning accuracy of color Doppler ultrasound angiography was 100%. All pediatric surgeries were successfully completed, and the flaps and skin grafts survived smoothly. There were no postoperative complications such as infection, vascular crisis, or finger necrosis. All patients were followed up for 6 to 27 months, with an average of 13.5 months. At the last follow-up, the depth, width, and slope of the fingerwebs were all close to normal.Conclusion:Congenital syndactyly has a high variation rate at the bifurcation point of the common palmar digital artery, and bony syndactyly and Poland syndrome refer to the need to improve preoperative vascular evaluation. Color Doppler ultrasound angiography has the advantages of no radiation, high sensitivity and low price, and the vascular typing based on color Doppler can improve the reliable basis for the formulation of clinical surgical plans.