Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective: This study investigated the value of multiparametric MRI (mpMRI) in predicting Gleason score (GS) upgrading and downgrading in radical prostatectomy (RP) compared with presurgical biopsy. Materials and Methods: Clinical and mpMRI data were retrospectively collected from 219 patients with prostate disease between January 2015 and December 2021. All patients underwent systematic prostate biopsy followed by RP. MpMRI included conventional diffusion-weighted and dynamic contrast-enhanced imaging. Multivariable logistic regression analysis was performed to analyze the factors associated with GS upgrading and downgrading after RP. Receiver operating characteristic curve analysis was used to estimate the area under the curve (AUC) to indicate the performance of the multivariable logistic regression models in predicting GS upgrade and downgrade after RP. Results: The GS after RP was upgraded, downgraded, and unchanged in 92, 43, and 84 patients, respectively. The AUCs of the clinical (percentage of positive biopsy cores [PBCs], time from biopsy to RP) and mpMRI models (prostate cancer [PCa] location, Prostate Imaging Reporting and Data System [PI-RADS] v2.1 score) for predicting GS upgrading after RP were 0.714 and 0.749, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, tPSA, PCa location, and PIRADS v2.1 score) was 0.816, which was larger than that of the clinical factors alone (P < 0.001). The AUCs of the clinical (age, percentage of PBCs, ratio of free/total PSA [F/T]) and mpMRI models (PCa diameter, PCa location, and PI-RADS v2.1 score) for predicting GS downgrading after RP were 0.749 and 0.835, respectively. The AUC of the combined diagnostic model (age, percentage of PBCs, F/T, PCa diameter, PCa location, and PI-RADS v2.1 score) was 0.883, which was larger than that of the clinical factors alone (P < 0.001). Conclusion Combining clinical factors and mpMRI findings can predict GS upgrade and downgrade after RP more accurately than using clinical factors alone.

Objective:To construct a machine-learning model for predicting the progression of pancreatic cystic lesions (PCLs) based on clinical and CT features, and to evaluate its predictive performance in internal/external testing cohorts.Methods:Baseline clinical and radiological data of 200 PCLs in 177 patients undergoing abdominal thin slice enhanced CT examination at Peking Union Medical College Hospital from July 2014 to December 2022 were retrospectively collected. PCLs were divided into progressive and non-progressive groups according to whether the signs indicated for surgery by the guidelines of the European study group on PCLs were present during three-year follow-up. 200 PCLs were randomly divided into training (150 PCLs) and internal testing cohorts (50 PCLs) at the ratio of 1∶3. 15 PCLs in 14 patients at Jinling Affiliated Hospital of Medical School of Nanjing University from October 2011 to May 2020 were enrolled as external testing cohort. The clinical and CT radiological features were recorded. Multiple feature selection methods and machine-learning models were implemented and combined to identify the optimal machine-learning model based on the 10-fold cross-validation method. Receiver operating characteristics (ROC) curve was drawn and area under curve (AUC) was calculated. The model with the highest AUC was determined as the optimal model. The optimal model's predictive performance was evaluated on testing cohort by calculating AUC, sensitivity, specificity and accuracy. Permutation importance was used to assess the importance of optimal model features. Calibration curves of the optimal model were established to evaluate the model's clinical applicability by Hosmer-Lemeshow test.Results:In training and internal testing cohorts, the progressive and non-progressive groups were significantly different on history of pancreatitis, lesions size, main pancreatic duct diameter and dilation, thick cyst wall, presence of septation and thick septation (all P value <0.05) In internal testing cohort, the two groups were significantly different on gender, lesion calcification and pancreatic atrophy (all P value <0.05). In external testing cohort, the two groups were significantly different on lesions size and pancreatic duct dilation (both P<0.05). The support vector machine (SVM) model based on five features selected by F test (lesion size, thick cyst wall, history of pancreatitis, main pancreatic duct diameter and dilation) achieved the highest AUC of 0.899 during cross-validation. SVM model for predicting the progression of PCLs demonstrated an AUC of 0.909, sensitivity of 82.4%, specificity of 72.7%, and accuracy of 76.0% in the internal testing cohort, and 0.944, 100%, 77.8%, and 86.7% in the external testing cohort. Calibration curved showed that the predicted probability by the model was comparable to the real progression of PCLs. Hosmer-Lemeshow goodness-of-fit test affirmed the model's consistency with actual PCLs progression in testing cohorts. Conclusions:The SVM model based on clinical and CT features can help doctors predict the PCLs progression within three-year follow-up, thus achieving efficient patient management and rational allocation of medical resource.

To establish a medical imaging teacher competency model and evaluate its application value in group teaching for undergraduates.

Objective To explore the effects of wogonin on cognitive function and neuroinflamma-tion in VaD rats by regulating AMPK/SIRT1 pathway.Methods 90 VaD rats were randomly di-vided into model group,low-,medium-and high-dose wogonin groups(50,100,200 mg/kg),and high dose+AMPK inhibitor(20 mg/kg)group,18 animals per group.Another 18 normal rats served as sham operation group.ELISA was utilized to measure the contents of IL-1β,TNF-α and IL-6 in hippocampal tissues.Western blotting was conducted to detect the expression of Iba-1,p-AMPK,AMPK and SIRT1 in hippocampal tissues.Results Compared with the sham operation group,the model group had longer escape latency,and elevated contents of IL-1β,TNF-α and IL-6,number of Iba-1 positive cells and expression level of Iba-1 protein in hippocampal tissue,shorter stay time at original platform,and lower expression of p-AMPK/AMPK and SIRT1 in hippocampal tissues(P＜0.05).Treatment of three doses of wogonin,in a dose-dependent man-ner,reduced escape latency,and decreased levels of IL-1β,TNF-α and IL-6,number of Iba-1 posi-tive cells and expression of Iba-1 protein in hippocampal tissue,but longer stay time at original platform,and enhanced expression of p-AMPK/AMPK and SIRT1 protein in hippocampal tissue when compared with the model group(P＜0.05).The high dose+AMPK inhibitor group showed longer escape latency(47.64±5.39 s vs 26.45±3.27 s),shorter stay time at original platform(21.78±3.51 s vs 35.22±5.02 s),increased levels of IL-1β,TNF-α and IL-6,number of Iba-1 positive cells and expression of Iba-1 protein in hippocampal tissue,and lower expression levels of p-AMPK/AMPK and SIRT1 in hippocampal tissue when compared with the high-dose group(P＜0.05).Conclusion Wogonin may improve cognitive function and hippocampal tissue patho-logical damage in VaD rats by activating the AMPK/SIRT1 pathway,and inhibit neuroinflammation.

Objective:To investigate the effect of lycopene on blood-brain barrier and nerve damage in rats with cerebral small vessel disease(CSVD)by regulating Janus kinase 2(JAK2)/signal transducer and activator of transcription 3(STAT3)/vascular endo-thelial growth factor(VEGF)signaling pathway.Methods:Fifty CSVD rat models were prepared by in vitro injection of the same germ-line microemboli,and were randomly grouped into model group,low-dose lycopene(65 mg/kg)group,high-dose lycopene(85 mg/kg)group,AG490(JAK2 inhibitor,3.5 mg/kg)group,and high-dose lycopene(85 mg/kg)+AG490(3.5 mg/kg)group,with 10 cases in each group,another 10 SD rats were regarded as sham operation group.After grouping with lycopene and AG490,cognitive ability of rats was detected by Morris water maze test and dark avoidance test;permeability of blood-brain barrier in rats was detected by Evans blue method;the number of rat hippocampal neurons was detected by Nissl staining;levels of inflammatory factors prostaglandin E2(PGE2),tumor necrosis factor-α(TNF-α),and oxidative stress factors catalase(CAT),superoxide dismutase(SOD),malondialde-hyde(MDA)in rats brain tissue were detected by kits;expressions of matrix metalloproteinase(MMP)2,MMP9,tight junction-related proteins(ZO-1,Occludin)and JAK2/STAT3/VEGF pathway-related proteins in rats brain were detected by Western blotting.Results:Compared with sham operation group,the times of rats crossing the platform,the time of staying in the target quadrant,step through latency,the number of hippocampal neurons,levels of CAT and SOD in brain tissue,protein expressions of ZO-1,Occludin and VEGF,and p-JAK2/JAK2,p-STAT3/STAT3 were obviously decreased in model group(P<0.05),while the error times,Evans blue content,levels of brain tissue PGE2,TNF-α and MDA,and protein expressions of MMP2 and MMP9 were obviously increased(P<0.05).Compared with model group,the times of rats crossing the platform,the time of staying in the target quadrant,step through la-tency,the number of hippocampal neurons,levels of CAT and SOD in brain tissue,protein expressions of ZO-1,Occludin and VEGF,and p-JAK2/JAK2,p-STAT3/STAT3 were all increased in low-dose lycopene group and high-dose lycopene group(P<0.05),while the error times,Evans blue content,levels of brain tissue PGE2,TNF-α and MDA,and protein expressions of MMP2 and MMP9 were all decreased(P<0.05),and high dose lycopene was more effective;the change trend of each index in AG490 group was opposite to that in lycopene groups,and AG490 could reverse the effect of lycopene.Conclusion:Lycopene can inhibit inflammation and oxidative stress in CSVD rats by activating JAK2/STAT3/VEGF signaling pathway,thereby reducing the blood-brain barrier and nerve damage,and improving their cognitive ability.

Intraspinal metastasis from malignant carcinomas in other body parts is rarely repor-ted.Intraspinal metastases are often epidural,with primary tumors mostly from the lung and prostate.The extr-amedullary subdural metastasis of thymic carcinoma is particularly rare and prone to misdiagnosis due to overlap-ping imaging features with primary intraspinal tumors.This article reports one case of intraspinal metastasis of thy-mic carcinoma,with the main diagnostic clues including a history of thymic carcinoma,fast growth rate,and ir-regular shape.