Objective:To compare the diagnostic value of semi-quantitative parameters of fluorine 18-labelled prostate-specific membrane antigen( 18F-PSMA)positron emission tomography /computed tomography(PET/CT)and the Prostate-specific Membrane Antigen Reporting and Data System(PSMA-RADS)score for identifying benign and malignant oligo-PSMA-avid bone lesions(1-5 lesions)in elderly patients with prostate cancer. Methods:A retrospective analysis was conducted on 157 prostate cancer patients who underwent 18F-PSMA PET/CT examinations at Beijing Hospital from October 2022 to August 2024.According to the inclusion and exclusion criteria, a total of 63 patients were selected.All patients underwent 18F-PSMA PET/CT examination for the purpose of initial staging or detecting lesions with biochemical recurrence.PSMA-avid bone lesions were evaluated using the PSMA-RADS version 2.0 scoring system and the semi-quantitative parameters were measured on PSMA PET/CT images.According to the comprehensive diagnostic criteria, PSMA-avid bone lesions were divided into metastatic group and non-metastatic group.The differences in PSMA-RADS scores, semi-quantitative parameters, bone density abnormalities, and lesion distribution were compared between the two groups.Multivariate logistic regression analysis was performed to determine the factors related to the bone metastasis in prostate cancer.By plotting the receiver operating characteristic(ROC)curves and calculating the area under the curve(AUC), factors with better diagnostic performance were evaluated and screened, and the optimal diagnostic threshold for each factor in diagnosing bone metastasis was determined. Results:There were a total of 129 PSMA-avid bone lesions for 63 patients(aged 60-84 years, median age 69 years), including 35 lesions(27.1%)in the metastatic group and 94 lesions(72.9%)in the non-metastatic group.The differences between metastatic group and non-metastatic group in PSMA-RADS scores[5(4, 5) vs.3(3, 3)], maximum standardized uptake value(SUV max)[12.6(7.0, 18.4) vs.4.7(3.5, 5.9)], lesion SUV max/mediastinal blood pool SUV max ratio(lesion-to-blood pool ratio, LBR)[5.4(3.0, 8.3) vs.1.7(1.4, 2.2)], lesion SUV max/liver SUV max ratio(lesion-to-liver ratio, LLR)[2.6(1.6, 4.1) vs.0.8(0.7, 1.1)], PSMA receptor expressing tumor volume(PSMA-TV)[0.6(0.3, 1.0) vs.1.0(0.7, 1.5)], total lesion of PSMA(TL-PSMA)[4.4(2.4, 7.0) vs.2.4(1.7, 3.9)], proportion of changes in osteogenic bone density[77.1%(27/35) vs.2.1%(2/94)], proportion of lesions located in the ribs[14.3%(5/35) vs.46.8%(44/94)], and proportion of lesions located in the pelvis[54.3%(19/35) vs.20.2%(19/94)]were all statistically significant(all P<0.05). Multivariate logistic regression analysis indicated that none of the variables with statistically significant differences between groups above were independent risk factors for osseous metastasis in prostate cancer(all P>0.05). Among them, The PSMA-RADS score, LLR, LBR, and SUV max all had good diagnostic efficacy for osseous metastasis, with 0.995(95% CI: 0.987-1.000), 0.923(95% CI: 0.869-0.977), 0.898(95% CI: 0.828-0.967), and 0.890(95% CI: 0.820-0.961), respectively.The cut-off values for diagnosing osseous metastasis were 4 score for PSMA-RADS score, 0.934 for LLR, 1.990 for LBR, and 5.47 for SUV max, respectively.According to Delong's test, there were statistically significant differences in AUC between PSMA-RADS score and 18F-PSMA PET/CT semi-quantitative parameters(LLR, LBR, and SUV max)( Z-values were 2.677, 2.776, and 2.929, respectively, and P-values were 0.007, 0.006, and 0.003, respectively). Conclusions:The PSMA-RADS score(Version 2.0)and 18F-PSMA PET/CT semi-quantitative parameters(LLR, LBR, and SUV max)both have good diagnostic value in differentiating benign and malignant PSMA-avid bone lesions in elderly patients with prostate cancer, among which the PSMA-RADS score has the best diagnostic efficacy.

Objective:To compare the diagnostic value of semi-quantitative parameters of fluorine 18-labelled prostate-specific membrane antigen( 18F-PSMA)positron emission tomography /computed tomography(PET/CT)and the Prostate-specific Membrane Antigen Reporting and Data System(PSMA-RADS)score for identifying benign and malignant oligo-PSMA-avid bone lesions(1-5 lesions)in elderly patients with prostate cancer. Methods:A retrospective analysis was conducted on 157 prostate cancer patients who underwent 18F-PSMA PET/CT examinations at Beijing Hospital from October 2022 to August 2024.According to the inclusion and exclusion criteria, a total of 63 patients were selected.All patients underwent 18F-PSMA PET/CT examination for the purpose of initial staging or detecting lesions with biochemical recurrence.PSMA-avid bone lesions were evaluated using the PSMA-RADS version 2.0 scoring system and the semi-quantitative parameters were measured on PSMA PET/CT images.According to the comprehensive diagnostic criteria, PSMA-avid bone lesions were divided into metastatic group and non-metastatic group.The differences in PSMA-RADS scores, semi-quantitative parameters, bone density abnormalities, and lesion distribution were compared between the two groups.Multivariate logistic regression analysis was performed to determine the factors related to the bone metastasis in prostate cancer.By plotting the receiver operating characteristic(ROC)curves and calculating the area under the curve(AUC), factors with better diagnostic performance were evaluated and screened, and the optimal diagnostic threshold for each factor in diagnosing bone metastasis was determined. Results:There were a total of 129 PSMA-avid bone lesions for 63 patients(aged 60-84 years, median age 69 years), including 35 lesions(27.1%)in the metastatic group and 94 lesions(72.9%)in the non-metastatic group.The differences between metastatic group and non-metastatic group in PSMA-RADS scores[5(4, 5) vs.3(3, 3)], maximum standardized uptake value(SUV max)[12.6(7.0, 18.4) vs.4.7(3.5, 5.9)], lesion SUV max/mediastinal blood pool SUV max ratio(lesion-to-blood pool ratio, LBR)[5.4(3.0, 8.3) vs.1.7(1.4, 2.2)], lesion SUV max/liver SUV max ratio(lesion-to-liver ratio, LLR)[2.6(1.6, 4.1) vs.0.8(0.7, 1.1)], PSMA receptor expressing tumor volume(PSMA-TV)[0.6(0.3, 1.0) vs.1.0(0.7, 1.5)], total lesion of PSMA(TL-PSMA)[4.4(2.4, 7.0) vs.2.4(1.7, 3.9)], proportion of changes in osteogenic bone density[77.1%(27/35) vs.2.1%(2/94)], proportion of lesions located in the ribs[14.3%(5/35) vs.46.8%(44/94)], and proportion of lesions located in the pelvis[54.3%(19/35) vs.20.2%(19/94)]were all statistically significant(all P<0.05). Multivariate logistic regression analysis indicated that none of the variables with statistically significant differences between groups above were independent risk factors for osseous metastasis in prostate cancer(all P>0.05). Among them, The PSMA-RADS score, LLR, LBR, and SUV max all had good diagnostic efficacy for osseous metastasis, with 0.995(95% CI: 0.987-1.000), 0.923(95% CI: 0.869-0.977), 0.898(95% CI: 0.828-0.967), and 0.890(95% CI: 0.820-0.961), respectively.The cut-off values for diagnosing osseous metastasis were 4 score for PSMA-RADS score, 0.934 for LLR, 1.990 for LBR, and 5.47 for SUV max, respectively.According to Delong's test, there were statistically significant differences in AUC between PSMA-RADS score and 18F-PSMA PET/CT semi-quantitative parameters(LLR, LBR, and SUV max)( Z-values were 2.677, 2.776, and 2.929, respectively, and P-values were 0.007, 0.006, and 0.003, respectively). Conclusions:The PSMA-RADS score(Version 2.0)and 18F-PSMA PET/CT semi-quantitative parameters(LLR, LBR, and SUV max)both have good diagnostic value in differentiating benign and malignant PSMA-avid bone lesions in elderly patients with prostate cancer, among which the PSMA-RADS score has the best diagnostic efficacy.

Objective To investigate the correlation between the total CSVD burden and fundus vasculopathy,and evaluate significance of fundus vasculopathy for cognitive impairment in CSVD patients.Methods A total of 290 inpatients who taking physical examination in our department from May 2021 to August 2022 were consecutively recruited,and according to their score of CSVD,they were divided into group 0(129 cases),group 1(51 case),group 2(42 cases),group 3(44 cases),and group 4(24 cases).All the subjects underwent brain magnetic resonance imaging,fundus fluorescein angiography,and cognitive function assessment.The total CSVD burden and fundus vasculopathy were evaluated.The general clinical data,results of laboratory tests,fundus vascularization,and cognitive function were compared among the groups with different CSVD burden scores.Spearman correlation analysis and linear correlation analysis were used to explore the correlation between total CSVD burden score and fundus vascular disease.Results Significant differences were observed in terms of age,years of education,cerebral infarction/TIA,total cho-lesterol,LDL-C,creatinine,MoC A and MMSE scores,positive results of connectivity test,digit-symbol conversion test,Stroop colour-word interference test and verbal fluency test,values of CRAE,CRVE,AVR,Scheie grade,DWMH and PVWMH,enlarged perivascular space in the basal ganglia(BG-EPVS),lacunar infarct and cerebral microbleeds(CMB)in different CSVD total bur-den groups(P＜0.05,P＜0.01).Spearman correlation analysis showed that total CSVD burden was negatively correlated with CRAE and AVR(r=-0.655,P=0.000;r=-0.679,P=0.000),and positively with CRVE and Scheie grade(r=0.560,P=0.000;r=0.685,P=0.000).Multivari-ate linear analysis showed that the total CSVD burden after adjusting for relevant risk factors was significantly correlated with CRAE,CRVE,AVR and Scheie grades(P＜0.01).Conclusion Fun-dus vasculopathy is strongly associated with increased total CSVD burden,and it can be regarded as a valid predictor of CSVD-related cognitive impairment.

Objective:To explore the diagnostic value of 18F-prostate specific membrane antigen (PSMA) PET/CT PRIMAY score combined with multiparameter MRI (mpMRI) PI-RADS score for clinically significant prostate cancer (CsPCa). Methods:The data of 63 patients with prostate cancer who underwent radical prostatectomy at Beijing Hospital from January 2019 to December 2023 were retrospectively analyzed. The median age was 70 (64, 75) years old with prostate-specific antigen (PSA) level of 8.46 (5.40, 14.80) ng/ml. All patients underwent 18F-PSMA PET/CT and mpMRI examination before surgery, and pathological large sections of prostate specimens were made after surgery. The prostate lesions were diagnosed and located by two radiologists and one pathologist respectively. Lesions with Gleason scores (GS)≥3+ 4 from the surgical pathology were diagnosed with CsPCa, and lesions with negative or GS=6 were diagnosed with non-CsPCa. The PSMA PET/CT images were evaluated using the PRIMARY study criteria (5-level PRlMARY score): no pattern (score of 1), diffuse transition zone or central zone(not focal) (score of 2), focal transition zone(score of 3), focal peripheral zone(score of 4), or an SUV max of at least 12 (score of 5). The degree of uptake of imaging agent in prostate lesions was semi-quantitatively evaluated using lesion-to-background ratios (LBR) of SUV max. MpMRI was evaluated according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. The patients were divided into CsPCa group and non-CsPCa group based on patients and lesions. Mann-Whitney U test and chi-square test were used to compare the differences between groups. Multivariate logistic regression analysis was performed to determine the independent predictive factors of CsPCa. Receiver operator characteristic (ROC) curve was used to determine the optimal diagnostic threshold for each independent predictor. Predictive models were constructed for PRIMARY score, PI-RADS score, and their combined application, and the diagnostic performance of each model for CsPCa was compared. Results:Of all 63 patients, there were 54 cases in CsPCa group (85.7%) and 9 cases in non-CsPCa group (14.3%).There was significant difference between CsPCa group and non-CsPCa group in the serum PSA level [9.64 (6.1, 15.3) ng/ml vs. 5.6 (4.6, 7.6) ng/ml]( P<0.05). There was no statistically significant difference in age [71 (64, 75) years vs. 65 (63, 69) years], and number of lesions [2 (1, 2) vs. 2 (1, 3)] (all P>0.05). Of all 109 lesions, there were 81 lesions in CsPCa group(including 49 lesions with Gleason score = 3+ 4, 16 lesions with Gleason score=4+ 3, 14 lesions with Gleason score = 8, and 2 lesions with Gleason score>8) and 28 lesions in non-CsPCa group(including 14 lesions with Gleason score = 3+ 3 and 14 with benign prostate lesions). There was significant difference between CsPCa group and non-CsPCa group in PRIMARY score [4 (3, 5) vs. 2 (1, 4)], LBR [2.69 (2.08, 4.48) vs. 1.89 (1.45, 2.48)], PI-RADS score [4 (3, 5) vs. 2 (2, 3)] (all P<0.05). There was no statistically significant difference in the lesion distribution including the number of lesions located in the transition zone [15(18.5%) vs. 8(28.6%)] and in the peripheral zone[66(81.5%) vs. 20(71.4%)]( P>0.05). Multivariate logistic regression analysis indicated that PRIMARY score ( OR=2.134, 95% CI 1.429-3.187) and PI-RADS score ( OR=2.689, 95% CI 1.618-4.469) were independent predictors of CsPCa (both P<0.01). ROC curves analysis revealed that the cut-off value for diagnosing CsPCa was both 3 for PRIMARY score and PI-RADS score. The accuracy for PRIMARY score, PI-RADS score, and their combined complication in diagnosing CsPCa was 72%, 67%, and 83%, respectively. The sensitivity was 72%, 63%, and 91%, and the specificity was 75%, 79%, and 57%, respectively. The positive predictive value was 89%, 89%, and 86%, and the negative predictive value was 48%, 42%, and 70%, respectively. The area under the curve of the PRIMARY score, PI-RADS score, and their combined complication of the ROC curve for CsPCa were 0.733 (95% CI 0.624-0.842), 0.708 (95% CI 0.599-0.817), and 0.743 (95% CI 0.623-0.862), respectively. The diagnostic efficacy of their combined complication was higher than PRIMARY score or PI-RADS score alone (both P<0.01). Conclusions:Both the 18F-PSMA PET/CT PRIMAY score and the mpMRI PI-RADS score have good diagnostic value for CsPCa. The combined application of the two imaging parameters can improve the accuracy, sensitivity, and negative predictive value, which have a higher diagnostic efficiency of CsPCa.

Objective:To investigate the accuracy of resting energy expenditure(REE)prediction formulas and to develop a new REE prediction formula suitable for hospitalized older adults.Methods:Older adults hospitalized in the Department of Geriatrics from October 1, 2022, to November 31, 2022, were included in the study.The predicted values of REE(pREE)were estimated using 13 commonly employed formulas that incorporate parameters related to resting energy expenditure, such as gender, age, body mass index(BMI), and body weight.Indirect calorimetry measurements(mREE)served as the gold standard for comparison.Group differences between pREE and mREE, the coefficient of concordance( ICC), and accuracy(defined as±10% of the mREE values)were utilized to evaluate the performance of the formulas.The ten-fold cross-validation method was employed to identify valid variables and to construct a new prediction formula.The performance of this new formula was compared to mREE, the Harris-Benedict formula, the European Society of Clinical Nutrition and Metabolism(ESPEN)formula, and the Chinese Society of Clinical Nutrition and Metabolism(CSPEN)formula. Results:A total of 223 hospitalized participants aged 60 to 98 years(mean age 79.5±8.2 years)were included in the study.Among these participants, 49.3%(110 cases)were male, and the prevalence of frailty was approximately 84.3%(188 cases).The median difference between pREE and mREE ranged from 9.1 to 232.1 kcal/d.The predictions from the Harris-Benedict, ESPEN, and CSPEN equations differed significantly from mREE(all P<0.05), with respective accuracies of 30.9%, 31.4%, and 24.7%.A new equation was developed: pREE=794.847+ 8.661×body weight -7.976 × age+ 14.757 ×grip strength+ 5.037 × heart rate, with an ICC of 0.6(95% CI: 0.5-0.7), and the accuracy reached 56.3%. Conclusions:Existing equations demonstrate low accuracy in predicting REE among hospitalized older adults.The newly developed equation shows improved performance compared to previous models and can serve as a reference method for predicting REE in this demographic.

Objective To investigate the relationship between the levels of serum neuron-specific enolase(NSE)and tumor necrosis factor alpha(TNF-α)and the attack stage and prognosis of children with epilepsy secondary to viral encephalitis(VE).Methods A total of 96 children with VE admitted to the People's Hospital Affiliated to Inner Mongolia Medical University from January 2015 to January 2020 were selected as the research subjects,and they were divided into the control group(children with VE,n=30)and the observation group(children with epilepsy secondary to VE,n=66).In addition,30 healthy children who underwent physical examination in the hospital during the same period were selected as the health group.The levels of serum NSE and TNF-α in the health group,control group and observation group were compared.Children in the observation group were further divided into the 24h relapse group(n=48)and 24h non-relapse group(n=18)according to the attack of disease within 24h after admission.The levels of serum NSE and TNF-α were compared between the two groups.Pearson correlation was used to analyze the relationship between the levels of NSE,TNF-α and the attack stage of epilepsy secondary to VE.Children in the observation group were divided into the good prognosis group(n=45)and the poor prognosis group(n=21)according to the Glasgow Outcome Scale score at discharge.The serum levels of NSE,TNF-α and other possible prognostic factors were compared between the two groups.Multivariate logistic regression analysis was used to explore the prognostic factors of children with epilepsy secondary to VE,and the predictive value of serum NSE and TNF-αlevels on the prognosis of children with epilepsy secondary to VE was analyzed by drawing the receiver operating characteristic curve.Results The level of serum NSE in the control group was significantly higher than that in the health group(P＜0.05),and there was no significant difference in the level of serum TNF-α between the control group and the health group(P＞0.05).The serum levels of NSE and TNF-α in the observation group were significantly higher than those in the control group(P＜0.05).The serum levels of NSE and TNF-α in the observation group were significantly higher than those in the control group(P＜0.05).The levels of serum NSE and TNF-α in the 24 h relapse group were significantly higher than those in the 24 h non-relapse group(P＜0.05).The proportion of severe abnormal EEG,severe abnormal brain images and complicated respiratory failure,and serum levels of c-reactive protein,NSE and TNF-α in the good prognosis group were lower than those in the poor prognosis group(P＜0.05);there were no significant differences in sex,age,body mass,brain injury site,fever,hypokalemia,hyponatremia,previous convulsions,stress hyperglycemia,complicated organ dysfunction,viral infection,first episode of epilepsy,Glasgow Coma Scale score at admission,duration of convulsion,length of hospital stay,white blood cell count,aspartate transaminase,creatine kinase and cardiac troponin levels between the two groups(P＞0.05).The results of multivariate logistic regression analysis showed that complicated respiratory failure,serum NSE and TNF-α levels were correlated with the prognosis of children with epilepsy secondary to VE(P＜0.05).The area under the curve(AUC)of serum NSE and TNF-α levels in predicting the prognosis of children with epilepsy secondary to VE was 0.724(95％confidence interval:0.672-0.776)and 0.689(95％confidence interval:0.637-0.734),respectively,with a sensitivity of 82.22％and 75.56％and a specificity of 76.19％and 71.43％;the AUC of the combination of the two in predicting the prognosis of children with epilepsy secondary to VE was 0.826(95％confidence interval:0.774-0.873),with a sensitivity of 73.33％and a specificity of 80.95％.Conclusion The serum levels of NSE and TNF-α are abnormally high in children with epilepsy secondary to VE.Both of them are factors affecting the prognosis of children with epilepsy secondary to VE,showing a good predictive value for the prognosis of epilepsy secondary to VE.

Objective:To investigate the predictive value of a radiomics model based on biparametric magnetic resonance imaging(bpMRI)for biochemical recurrence(BCR)after radical prostatectomy(RP)in elderly prostate cancer patients(≥60 years old).Methods:A retrospective analysis was conducted on data from 175 patients treated at Beijing Hospital from August 2017 to December 2021.Based on pathological results, image segmentation was performed on preoperative bpMRI T2, diffusion weighted imaging(DWI), and apparent diffusion coefficient(ADC)sequences.Pyradiomics was utilized to extract radiomic features, and Cox regression, Spearman correlation coefficient, and LASSO regression were employed for feature dimensionality reduction, leading to the construction of radiomic labels.Clinical models and image-clinical combined models were developed using multifactorial Cox regression analysis, and the performance of these models in predicting BCR was evaluated using the concordance index(C-index).Results:The 175 patients were randomly divided into a training set(122 cases)and a test set(53 cases)at a ratio of 7∶3, with 24 cases(19.7%, 24/122)and 11 cases(20.8%, 11/53)experiencing BCR, respectively.A total of 5 775 radiomic features were extracted from the three sequences, and after dimensionality reduction, 5 features were selected to construct the radiomic labels.The radiomics model exhibited C-index values of 0.764(95% CI: 0.655-0.872)and 0.769(95% CI: 0.632-0.906)in the training and test sets, respectively.Multifactorial Cox regression analysis revealed serum prostate-specific antigen(PSA)( HR=1.032, 95% CI: 1.010-1.054), postoperative pathology International Society of Urological Pathology(ISUP)grade grouping( HR=1.682, 95% CI: 1.039-2.722), and positive surgical margins( HR=2.513, 95% CI: 1.094-5.774)as independent predictors of BCR.The clinical model exhibited C-index values of 0.751(95% CI: 0.655-0.846)and 0.753(95% CI: 0.630-0.877)in the training and test sets, respectively.Following combined modeling of clinical factors and radiomic labels, the image-clinical combined model demonstrated the highest C-index values, namely 0.782(95% CI: 0.679-0.874)and 0.801(95% CI: 0.677-0.915)in the training and test sets, respectively. Conclusions:The radiomics model based on bpMRI can predict the occurrence of BCR after RP in elderly prostate cancer patients.Combined modeling of clinical factors and radiomic labels can enhance predictive efficiency.

Objective:To investigate the accuracy of resting energy expenditure(REE)prediction formulas and to develop a new REE prediction formula suitable for hospitalized older adults.Methods:Older adults hospitalized in the Department of Geriatrics from October 1, 2022, to November 31, 2022, were included in the study.The predicted values of REE(pREE)were estimated using 13 commonly employed formulas that incorporate parameters related to resting energy expenditure, such as gender, age, body mass index(BMI), and body weight.Indirect calorimetry measurements(mREE)served as the gold standard for comparison.Group differences between pREE and mREE, the coefficient of concordance( ICC), and accuracy(defined as±10% of the mREE values)were utilized to evaluate the performance of the formulas.The ten-fold cross-validation method was employed to identify valid variables and to construct a new prediction formula.The performance of this new formula was compared to mREE, the Harris-Benedict formula, the European Society of Clinical Nutrition and Metabolism(ESPEN)formula, and the Chinese Society of Clinical Nutrition and Metabolism(CSPEN)formula. Results:A total of 223 hospitalized participants aged 60 to 98 years(mean age 79.5±8.2 years)were included in the study.Among these participants, 49.3%(110 cases)were male, and the prevalence of frailty was approximately 84.3%(188 cases).The median difference between pREE and mREE ranged from 9.1 to 232.1 kcal/d.The predictions from the Harris-Benedict, ESPEN, and CSPEN equations differed significantly from mREE(all P<0.05), with respective accuracies of 30.9%, 31.4%, and 24.7%.A new equation was developed: pREE=794.847+ 8.661×body weight -7.976 × age+ 14.757 ×grip strength+ 5.037 × heart rate, with an ICC of 0.6(95% CI: 0.5-0.7), and the accuracy reached 56.3%. Conclusions:Existing equations demonstrate low accuracy in predicting REE among hospitalized older adults.The newly developed equation shows improved performance compared to previous models and can serve as a reference method for predicting REE in this demographic.

Objective:To evaluate the image quality of prostate T 2WI reconstructed based on deep learning (deep learning T 2WI) and the diagnostic performance for prostate cancer (PCa) in the transition zone. Methods:Totally 79 patients who underwent prostate MRI for elevated prostate specific antigen from December 2020 to September 2022 were prospectively consecutively collected from Beijing Hospital. Scan sequences included axial standard T 2WI, deep learning T 2WI, and diffusion-weighted imaging. The scan time was recorded. The image quality was scored subjectively including image quality, diagnostic confidence, noise level, artifacts, clarity and lesion detectability. For objective evaluation of image quality, signal-to-noise ratio (SNR) and contrast signal-to-noise ratio (CNR) were calculated. Two-parameter MRI prostate imaging reporting and data system version 2.1 (PI-RADS v2.1) scoring was performed for transition zone lesions using deep learning T 2WI and standard T 2WI, respectively. The subjective and objective image quality evaluation metrics for deep learning T 2WI and standard T 2WI were compared using the Wilcoxon signed-rank test. For transition zone lesions, the diagnostic performance of PI-RADS scores with deep learning T 2WI and standard T 2WI for PCa was evaluated by the receiver operating characteristic curve based on the lesion (all lesions in the transition zone) and the patient (the most malignant lesions in the transition zone), respectively, using the pathologic results as the gold standard. The area under the curve (AUC) was compared using the DeLong test. Results:Deep learning T 2WI significantly reduced the examination time by 64.6.%, from 4 min 37 s to 1 min 38 s. The scores of subjective image quality of deep learning T 2WI and standard T 2WI all were 5 (4, 5). The differences in image quality and lesion detectability were statistically significant ( Z=-2.32, -2.36, P=0.020, 0.018), and the differences of all other image quality evaluation metrics were not statistically significant ( P>0.05). The SNR of deep learning T 2WI and standard T 2WI were 17.11 (14.09, 21.92) and 9.15 (7.16, 11.17), with a statistically significant difference ( Z=-7.72, P<0.001). The CNR of deep learning T 2WI and standard T 2WI were 20.78 (13.42, 31.42) and 11.05 (7.82, 16.25), with a statistically significant difference ( Z=-7.54, P<0.001). Based on the lesion (40 PCa and 48 benign lesions), the AUC of the two-parameter PI-RADS score with deep learning T 2WI and standard T 2WI for diagnosing PCa in the transition zone were 0.915 (95%CI 0.856-0.975) and 0.916 (95%CI 0.857-0.976), without statistically significant difference ( Z=0.03, P=0.973). Based on the patient (33 PCa and 46 benign patients), the AUC of the two-parameter PI-RADS score with deep learning T 2WI and standard T 2WI were 0.921 (95%CI 0.857-0.984) and 0.939 (95%CI 0.886-0.992), without statistically significant difference ( Z=0.59, P=0.558). Conclusions:Compared with standard T 2WI, deep learning T 2WI of the prostate reduces scanning time while maintaining image quality and has comparable diagnostic performance for PCa in the transition zone.

Objective:To apply the best evidence of pre-examination and triage management of patients with non-traumatic acute abdomen to clinical practice and evaluate its effects.Methods:Using convenience sampling method, 15 nurses and 237 patients with non-traumatic acute abdomen admitted in the emergency department of Shanxi Provincial People ′s Hospital from January to May 2021 were selected as the research objects, 114 cases as the baseline review group and 123 cases as the after-effect evaluation group. Following the clinical evidence practice application of JBI Evidence-Based Nursing Center Systematic standard procedures, using self before-after control study to compare the knowledge and behavior of triage nurses before and after evidence application, and compliance with each review index; using a non-contemporaneous controlled trial to compare the changes of pre-examination and triage time and triage accuracy between two groups. Results:After the application of evidence, the score of the triage nurses on the pre-examination and triage of non-traumatic acute abdomen increased from 98.00±6.56 in the after-effect evaluation group to 114.20±3.88 in the base-line review group, and the difference was statistically significant ( t=8.62, P<0.05); after the application of evidence, the compliance with indicators 1-9,12,13,15-17 was significantly improved and the difference was significant ( χ2 values were 11.46-123.06, all P <0.05). After the application of evidence, the compliance rate of those indicators <80% increased to more than 80% except indicator 6, the accuracy of patient triage rose from 84.21%(96/114) to 93.50%(115/123) with a statistically significant difference ( χ2=5.22, P<0.05); after the application of the evidence, the triage time was 2.00(1.00,4.00) min, shorter than 3.00(2.00,4.63) min in the base-line review group, and the difference was statistically significant ( Z=-3.18, P<0.05). Conclusions:The application of the best evidence of pre-examination and triage for non-traumatic acute abdomen can improve the nursing practice of non-traumatic acute abdomen triage, improve the accuracy of non-traumatic acute abdomen triage, and shorten the triage time.