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.

Objective To investigate the efficacy and safety of hybrid surgery for the recanalization of carotid artery occlusion after stenting.Methods Clinical data and results of 17 patients with occlusion after carotid artery stenting and treated with hybrid surgery from June 2016 to April 2023 at the Department of Neurosurgery Cerebral Blood Flow Reconstruction Center of Xuanwu Hospital,Capital Medical University were retrospectively analyzed.According to whether the recanalization was successful,17 patients were divided into the the successful recanalization group and the failed recanalization group.Successful recanalization was defined as achieving modified thrombolysis in cerebral infarction(mTICI)grade ≥2b and residual stenosis＜50％.Baseline data(age,sex,body mass index,smoking history,alcohol consumption history,hypertension history,diabetes history,hyperlipidemia history,coronary heart disease history),clinical data(National Institutes of Health Stroke Scale[NIHSS]score at admission,fasting blood glucose,low density lipoprotein,high density lipoprotein,total cholesterol,triglyceride,occlusion side and segment,combination with severe stenosis or occlusion of the contralateral carotid artery,opening of the anterior communicating artery,opening of the posterior communicating artery,compensation of the external and internal carotid artery,compensation of the pia artery,stump morphology,and time from imaging diagnosis of occlusion to recanalization)were documented and compared between groups.The recanalization of occlusive vessels and perioperative complications were recorded.Imaging and clinical follow-up were performed 3,6 months and≥1 year after surgery.Results Among the 17 patients,the ratio of successful recanalization was 13/17.One patient had re-occlusion after operation,which was re-opened after thrombolysis,but neck hematoma with dyspnea occurred,and recovered after emergency operation.There was no postoperative stroke or death.The incidence of perioperative complications was 1/17.Compared with the successful recanalization group,the levels of high density lipoprotein and total cholesterol in the failed recanalization group were higher,and the differences between the groups were statistically significant(high density lipoprotein[1.3±0.3]mmol/L vs.[0.9±0.3]mmol/L,t=-2.139;total cholesterol:[4.2±0.8]mmol/L vs.[3.1±0.7]mmol/L,t=-2.649;both P＜0.05);There were no significant differences in other baseline data and clinical data(all P＞0.05).Imaging follow-up was completed in 9 of the 13 patients in the successful recanalization group,and the follow-up time was 3.8-36.9 months,with a median follow-up time of 22.8(12.8,34.7)months.Among them,1 patient(1/9)developed restenosis of recanalization vessels at 33.0 months after surgery and underwent stent implantation again.Conclusions The preliminary analysis showed that the occlusion after carotid artery stenting had better recanalization success and lower perioperative complications.In patients with chronic occlusion after carotid stenting,the application of a hybrid surgery for opening may be attempted under multimodal imaging assessment.

Objective: To evaluate the image quality of novel dark-blood computed tomography angiography (CTA) imaging combined with deep learning reconstruction (DLR) compared to delayed-phase CTA images with hybrid iterative reconstruction (HIR), to visualize the cervical artery wall in patients with Takayasu arteritis (TAK). Materials and Methods: This prospective study continuously recruited 53 patients with TAK (mean age: 33.8 ± 10.2 years; 49 females) between January and July 2022 who underwent head-neck CTA scans. The arterial- and delayed-phase images were reconstructed using HIR and DLR. Subtracted images of the arterial-phase from the delayed-phase were then added to the original delayed-phase using a denoising filter to generate the final-dark-blood images. Qualitative image quality scores and quantitative parameters were obtained and compared among the three groups of images: Delayed-HIR, Dark-blood-HIR, and Dark-blood-DLR. Results: Compared to Delayed-HIR, Dark-blood-HIR images demonstrated higher qualitative scores in terms of vascular wall visualization and diagnostic confidence index (all P < 0.001). These qualitative scores further improved after applying DLR (Dark-blood-DLR compared to Dark-blood-HIR, all P < 0.001). Dark-blood DLR also showed higher scores for overall image noise than Dark-blood-HIR (P < 0.001). In the quantitative analysis, the contrast-to-noise ratio (CNR) values between the vessel wall and lumen for the bilateral common carotid arteries and brachiocephalic trunk were significantly higher on Darkblood-HIR images than on Delayed-HIR images (all P < 0.05). The CNR values were significantly higher for Dark-blood-DLR than for Dark-blood-HIR in all cervical arteries (all P < 0.001). Conclusion Compared with Delayed-HIR CTA, the dark-blood method combined with DLR improved CTA image quality and enhanced visualization of the cervical artery wall in patients with TAK.

OBJECTIVE: To explore the clinical characteristics and genetic etiology of a patient with adolescent-onset hypomyelinated leukodystrophy with atrophy of basal ganglia and cerebellum (H-ABC). METHODS: A patient who was diagnosed with H-ABC in March 2018 at the First Affiliated Hospital of Nanjing Medical University was selected as the study subject. Clinical data was collected. Peripheral venous blood samples of the patient and his parents were collected. The patient was subjected to whole exome sequencing (WES). Candidate variant was verified by Sanger sequencing. RESULTS: The patient, a 31-year-old male, had manifested with developmental retardation, cognitive decline and abnormal gait. WES revealed that he has harbored a heterozygous c.286G>A variant of the TUBB4A gene. Sanger sequencing confirmed that neither of his parents has carried the same variant. Analysis with SIFT online software indicated the amino acid encoded by this variant is highly conserved among various species. This variant has been recorded by the Human Gene Mutation Database (HGMD) with a low population frequency. The 3D structure constructed by PyMOL software showed that the variant has a harmful effect on the structure and function of the protein. According to the guidelines formulated by the American College of Medical Genetics and Genomics (ACMG), the variant was rated as likely pathogenic. CONCLUSION The c.286G>A (p.Gly96Arg) variant of the TUBB4A gene probably underlay the hypomyelinating leukodystrophy with atrophy of basal ganglia and cerebellum in this patient. Above finding has enriched the spectrum of TUBB4A gene variants and enabled early definitive diagnosis of this disorder.
Male ; Humans ; Adolescent ; Adult ; Magnetic Resonance Imaging ; Basal Ganglia/pathology* ; Cerebellum ; Atrophy/pathology* ; Mutation ; Tubulin/genetics*

OBJECTIVE: To demonstrate the mechanism of mechanical ventilation (MV) induced endoplasmic reticulum stress (ERS) promoting mechanical ventilation-induced pulmonary fibrosis (MVPF), and to clarify the role of angiotensin receptor 1 (AT1R) during the process. METHODS: The C57BL/6 mice were randomly divided into four groups: Sham group, MV group, AT1R-shRNA group and MV+AT1R-shRNA group, with 6 mice in each group. The MV group and MV+AT1R-shRNA group mechanically ventilated for 2 hours after endotracheal intubation to establish MVPF animal model (parameter settings: respiratory rate 70 times/minutes, tidal volume 20 mL/kg, inhated oxygen concentration 0.21). The Sham group and AT1R-shRNA group only underwent intubation after anesthesia and maintained spontaneous breathing. AT1R-shRNA group and MV+AT1R-shRNA group were airway injected with the adeno-associated virus one month before modeling to inhibit AT1R gene expression in lung tissue. The expressions of AT1R, ERS signature proteins [immunoglobulin heavy chain-binding protein (BIP), protein disulfide isomerase (PDI)], fibrosis signature proteins [collagen I (COL1A1), α-smooth muscle actin (α-SMA)] in lung tissues were detected by immunofluorescence and Western blotting. Hematoxylin-eosin (HE) staining was used to evaluate lung injury and Masson staining was used to evaluate pulmonary fibrosis. RESULTS: Compared with the Sham group, the degree of pulmonary fibrosis and lung injury were more significant in the MV group. In the MV group, the protein expressions of AT1R, BIP, PDI, COL1A1 and α-SMA were increased (AT1R/β-actin: 1.40±0.02 vs. 1, BIP/β-actin: 2.79±0.07 vs. 1, PDI/β-actin: 2.07±0.02 vs. 1, COL1A1/α-Tubulin: 2.60±0.15 vs. 1, α-SMA/α-Tubulin: 2.80±0.25 vs. 1, all P < 0.01). The number of E-cad⁺/AT1R⁺ and E-cad⁺/BIP⁺ cells in lung tissue increased, and the fluorescence intensity of COL1A1 and α-SMA increased. Compared with the MV group, the degree of pulmonary fibrosis and lung injury were significantly relieved in the MV+AT1R-shRNA group. In the MV+AT1R-shRNA group, the protein expressions of AT1R, BIP, PDI, COL1A1 and α-SMA were decreased (AT1R/β-actin: 0.53±0.03 vs. 1.40±0.02, BIP/β-actin: 1.73±0.15 vs. 2.79±0.07, PDI/β-actin: 1.04±0.07 vs. 2.07±0.02, COL1A1/α-Tubulin: 1.29±0.11 vs. 2.60±0.15, α-SMA/α-Tubulin: 1.27±0.10 vs. 2.80±0.25, all P < 0.01). The number of E-cad⁺/AT1R⁺ and E-cad⁺/BIP⁺ cells in lung tissue decreased, and the fluorescence intensity of COL1A1 and α-SMA decreased. There was no statistically significant difference in the indicators between AT1R-shRNA group and Sham group. CONCLUSIONS MV up-regulate the expression of AT1R in alveolar epithelial cells, activate the AT1R pathway, induce ERS and promote the progression of MVPF.
Mice ; Animals ; Pulmonary Fibrosis/chemically induced* ; Lung Injury ; Respiration, Artificial/adverse effects* ; Actins/metabolism* ; Tubulin ; Mice, Inbred C57BL ; Endoplasmic Reticulum Stress ; RNA, Small Interfering