Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

Objective: To analyze the characteristics and trends of drowning mortality among Chinese residents from 2010 to 2021, so as to provide a basis for developing targeted prevention strategies and reducing the burden of drowning deaths. Methods: Data on drowning mortality among Chinese residents from 2010 to 2021 were collected from the China Cause of Death Surveillance Dataset (2010-2021). The crude mortality was calculated and then standardized using the data from the Sixth National Population Census in 2010. The characteristics of drowning mortality were analyzed by genders, regions, and ages. The average annual percent change (AAPC) was employed to assess the trend in drowning mortality. Results: From 2010 to 2021, the overall crude drowning mortality among Chinese residents was 3.53/100 000, with a standardized mortality of 3.34/100 000, both showed decreasing trends (AAPC=-2.438% and -3.739%, both P<0.05). The crude and standardized mortality were higher in males than in females (4.55/100 000 vs. 2.47/100 000, 4.43/100 000 vs. 2.16/100 000, both P<0.05). Both the crude drowning mortality (AAPC=-2.974% and -1.337%) and standardized drowning mortality (AAPC=-3.806% and -3.599%) among males and females showed decreasing trends, respectively (all P<0.05). The crude and standardized drowning mortality were higher in rural residents than in urban residents (4.13/100 000 vs. 2.35/100 000, 3.86/100 000 vs. 2.27/100 000, both P<0.05). Both the crude and standardized drowning mortality in rural residents showed decreasing trends (AAPC=-3.343% and -4.515%, both P<0.05), whereas no statistically significant trends were observed in urban residents (both P>0.05). Both the crude and standardized drowning mortality were higher in western residents than in eastern residents and central residents (4.30/100 000 vs. 2.89/100 000 and 3.66/100 000, 4.14/100 000 vs. 2.64/100 000 and 3.49/100 000, all P<0.05). The standardized mortality showed decreasing trends in eastern, central and western residents (AAPC=-3.237%, -2.344%, and -5.467%, all P<0.05). The groups aged ≥65 years and 1-<5 years experienced relatively high crude drowning mortality of 8.81/105 and 7.38/105, respectively. Decreasing trends were observed in groups aged <1 year, 1-<5 years, and 5-<15 years (AAPC=-14.126%, -11.452%, and -7.443%, all P<0.05). In contrast, no statistically significant trends were observed in the other age groups (all P>0.05). Conclusions The overall drowning mortality rate among Chinese residents showed a declining trend from 2010 to 2021. However, the risks of drowning mortality remained relatively high among males, rural residents, residents in the western region, children, and the elderly.

Objective To propose a lightweight deep learning network-based segmentation method for automatic segmenta-tion of the skeletal muscle at the third cervical spine(C3)level.Methods Firstly,121 patients with head and neck tumors admitted to the Department of Oncology of Zigong First People's Hospital from January 2019 to December 2022 were selected and randomly divided into a training set,a validation set and a test set in the ratio of 7∶1∶2.Secondly,a lightweight Mamba architecture was introduced into the UNet network and an attention gate(AG)mechanism was added to the skip connection path to construct a MB-UNet network model.Finally,the trained network models were evaluated for segmentation performance on the test set,the MB-UNet network model was compared with manual segmentation over the results of determination of skeletal muscle area(SMA),and with classical network models in terms of parameter scale and computation effort including UNet,Deeplab V3+,U2Net,VMUNet and UltraLight-VMUNet models.The time required by the MB-UNet network model for predicting SMA and that by the physician with the assistance of the model was summarized.Results When used for segmenting the skeletal muscle at C3 level the constructued MB-UNet network model gained advantages over the classical models,with a Dice similarity coefficient of 88.23%,an intersection over union(IoU)ratio of 78.94%,a sensitivity of 91.27%and a 95%Hausdorff distance of 7.13 mm;the SMA determined by manual segementation was basically close to that by the MB-UNet network model;the MB-UNet network model behaved generally better than the classical network models,with the computation effort being 1.88 GFLOPS and the parameter scale being 0.77M;it took the MB-UNet network model 1.93 s for the prediction on the test set,and only 2 min for the physician to obtain satisfactory results with the assistance of the MB-UNet network model,which was significantly shorter than that by munual segmentation(20 min).Conclusion The proposed method contributes to segmenting the skeletal muscle at C3 level precisely and rapidly and calculating SMA accurately,which helps clinicians to quickly diagnose sarcopenia in patients with head and neck tumors and improves the diagnostic efficiency.[Chinese Medical Equipment Journal,2025,46(8):11-17]

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective:To explore the correlation of bone marrow polychonal plasma cell proportion (pPC% ) and clinical features in newly diagnosed multiple myeloma (NDMM) patients.Methods:A retrospective analysis of 317 patients with NDMM admitted to Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from January 2018 to January 2023 was performed. The results of the pPC% in all patients were clear. The relationship between the pPC% and clinical characteristics was analyzed.Results:A total of 317 patients were included, comprising 180 males and 137 females. The median age at diagnosis was 61 (26-91) years, and 55.8% were 60 years or older. The pPC% in the bone marrow of patients with NDMM was different in the DS, International Staging System (ISS), and revised ISS (R-ISS) stages ( P=0.002, 0.010, and 0.049, respectively), whereas no statistical difference in pPC% was observed among patients with different FISH risk stratigrams ( P=0.971). The correlation coefficient between pPC% and hemoglobin (HGB) at the first diagnosis in patients was 0.211 ( P<0.01). The correlation coefficients with serum calcium, serum creatinine, M protein level, and β 2-microglobulin were -0.141, -0.120, -0.181, and -0.207, respectively, and the results of the significance test were P=0.012, 0.033, 0.004, and 0.002, respectively, indicating a negative correlation. Compared with the patients with a pPC% of ≥2.5%, the group of patients with a pPC% of <2.5% had significantly higher levels of light chain, serum calcium, serum creatinine, M protein, and β 2-microglobulin at the initial diagnosis ( P<0.05) ; lower HGB level ( P<0.001) ; and a higher proportion of patients in ISS stage Ⅲ ( P=0.034) . Conclusion:In this study, the pPC% in patients with NDMM was associated with clinical features of good prognosis, including higher HGB, lower serum calcium, serum creatinine, M protein quantity, β 2-microglobulin, light chain involvement, lower proportion of advanced disease (DS stage and ISS stage Ⅲ), and clinical features showing lower tumor burden.

Perihilar cholangiocarcinoma(PHC)is a common malignancy of biliary tract,for which surgery is the most effective treatment.However,its prognosis is not satisfactory even after surgical resection.In recent years,there have been some new advances in the surgical treatment of PHC.In this paper,we reviewed the existing literatures,demonstrated the current situation of preoperative biliary drainage,liver hyperplasia,hepatic resection,liver transplantation and minimally invasive surgery in the treatment of PHC,and prospected the future research direction.

Post-endoscopic retrograde cholangiopancreatography pancreatitis(PEP)is one of the most common complications after endoscopic retrograde cholangiopancreatography(ERCP).Numerous PEP prediction models have been established based on different statistical methods at home and abroad.The PEP prediction model,as a tool for evaluating and screening high-risk populations,can provide a basis for medical staff to find high-risk PEP patients early and take effective preventive measures.In recent years,new PEP prediction models have appeared one after another,but there is still a lack of recognized reliable prediction models in clinic.This article reviews the research status of PEP risk prediction models,aim to provide a direction for establishing a more reliable,accurate,and practical PEP risk prediction model in the later period.

OBJECTIVE: To investigate whether meranzin hydrate (MH) can alleviate depression-like behavior and hypomotility similar to Chaihu Shugan Powder (CSP), and further explore the potential common mechanisms. METHODS: Totally 120 Spraque-Dawley rats were randomly divided into 5-8 groups including sham, vehicle, fluoxetine (20 mg/kg), mosapride (10 mg/kg), CSP (30 g/kg), MH (9.18 mg/kg), [D-Lys3]-GHRP-6 (Dlys, 0.5 mg/kg), and MH+Dlys groups by a random number table, 8 rats in each group. And 32 mice were randomly divided into wild-type, MH (18 mg/kg), growth hormone secretagogue receptor-knockout (GHSR-KO), and GHSR+MH groups, 8 mice in each group. The forced swimming test (FST), open field test (OFT), tail suspension test (TST), gastric emptying (GE) test, and intestinal transit (IT) test were used to assess antidepressant and prokinetic (AP) effects after drug single administration for 30 min with absorbable identification in rats and mice, respectively. The protein expression levels of brain-derived neurotrophic factor (BDNF) and phosphorylated mammalian target of rapamycin (p-mTOR) in the hippocampus of rats were evaluated by Western blot. The differences in functional brain changes were determined via 7.0 T functional magnetic resonance imaging-blood oxygen level-dependent (fMRI-BOLD). RESULTS: MH treatment improved depression-like behavior (FST, OFT) and hypomotility (GE, IT) in the acute forced swimming (FS) rats (all P<0.05), and the effects are similar to the parent formula CSP. The ghrelin antagonist [D-Lys3]-GHRP-6 inhibited the effect of MH on FST and GE (P<0.05). Similarly, MH treatment also alleviated depression-like behavior (FST, TST) in the wild-type mice, however, no effects were found in the GHSR KO mice. Additionally, administration of MH significantly stimulated BDNF and p-mTOR protein expressions in the hippocampus (both P<0.01), which were also prevented by [D-Lys3]-GHRP-6 (P<0.01). Besides, 3 main BOLD foci following acute FS rats implicated activity in hippocampus-thalamus-basal ganglia (HTB) circuits. The [D-Lys3]-GHRP-6 synchronously inhibited BOLD HTB foci. As expected, prokinetic mosapride only had effects on the thalamus and basal ganglia, but not on the hippocampus. Within the HTB, the hippocampus is implicated in depression and FD. CONCLUSIONS MH accounts for part of AP effects of parent formula CSP in acute FS rats, mainly via ghrelin-related shared regulation coupled to BOLD signals in brain areas. This novel functionally connection of HTB following acute stress, treatment, and regulation highlights anti-depression unified theory.
Rats ; Mice ; Animals ; Brain-Derived Neurotrophic Factor/metabolism* ; Ghrelin/metabolism* ; Antidepressive Agents/therapeutic use* ; Hippocampus ; Stress, Psychological ; Mammals/metabolism*