Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

Objective To investigate the mechanism of action of Guizhi Fuling pill in treating chro-nic prostatitis(CP)using network pharmacology and molecular docking techniques.Methods Compo-nents of Guizhi Fuling pill were collected from the Traditional Chinese Medicines Systems Pharmacolo-gy Platform(TCMSP),and target information was obtained from the SwissTarget database.Targets for chronic prostatitis were screened from the GeneCards,OMIM,CTD,and DisGeNET disease data-bases.A protein-protein interaction(PPI)network was established and analyzed.Gene ontology(GO)functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway en-richment analysis were performed using the DAVID database.The Cytoscape software was employed to construct an association network linking the components of Guizhi Fuling Pill,their targets,and the targets of chronic prostatitis.Molecular docking was conducted using AutoDock Vina software to verify the binding stability between the components of Guizhi Fuling pill and their targets.Results After screening and deduplication in the TCMSP database,76 components of Guizhi Fuling Pill were iden-tified,and 655 component targets were retrieved from the SwissTarget database.There were 190 intersecting targets between GuizhiFuling Pill and chronic prostatitis.GO analysis indicated that Guizhi Fuling Pill may treat chronic prostatitis by participating in processes such asapoptosis,ATP binding,and signal transduction.KEGG analysis suggested that Guizhi Fuling Pill can regulate pathways such as phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)and mitogen-acti-vated protein kinase(MAPK)to intervene in chronic prostatitis.Molecular docking data demonstra-ted that the components of Guizhi Fuling pill exhibited stable conformations with their targets.Con-clusion The components of Guizhi Fuling Pill can stably bind to their targets and exert therapeutic effects on chronic prostatitis through multiple targets and pathways.

DSA is an essential technology for diagnosing and treating cerebrovascular diseases.Detailed vascular structures and hemodynamic information can be acquired through image post-processing technology from raw DSA images.Presently,DSA image analysis technology encompasses several methodologies,including automatic vascular segmentation and feature extraction,hemodynamic parameter derivation,and more intricate multimodal imaging fusion.This review elaborated on the development status of these techniques at the current stage and their probable application in clinical practice.

Endovascular treatment has become the first-line treatment for stroke caused by acute intracranial large vessel occlusion(LVO).Acute intracranial LVO caused by intracranial atherosclerosis(ICAS)is a common cause of thrombus removal in Chinese people,but it was difficult and complex for the surgical operation treatment.This consensus was based on the latest progress of domestic clinical research on ICAS-LVO and combined with the experience summary of clinical experts to summarize the identification,imaging features,surgical strategies,and perioperative management of ICAS-LVO.It aims to quickly identify ICAS-LVO,standardize its endovascular treatment strategies and techniques,reduce the disability and mortality rates of patients,and provide assistance for standardized clinical management.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

DSA is an essential technology for diagnosing and treating cerebrovascular diseases.Detailed vascular structures and hemodynamic information can be acquired through image post-processing technology from raw DSA images.Presently,DSA image analysis technology encompasses several methodologies,including automatic vascular segmentation and feature extraction,hemodynamic parameter derivation,and more intricate multimodal imaging fusion.This review elaborated on the development status of these techniques at the current stage and their probable application in clinical practice.

Endovascular treatment has become the first-line treatment for stroke caused by acute intracranial large vessel occlusion(LVO).Acute intracranial LVO caused by intracranial atherosclerosis(ICAS)is a common cause of thrombus removal in Chinese people,but it was difficult and complex for the surgical operation treatment.This consensus was based on the latest progress of domestic clinical research on ICAS-LVO and combined with the experience summary of clinical experts to summarize the identification,imaging features,surgical strategies,and perioperative management of ICAS-LVO.It aims to quickly identify ICAS-LVO,standardize its endovascular treatment strategies and techniques,reduce the disability and mortality rates of patients,and provide assistance for standardized clinical management.

Background and objective Cystic lung cancer,a special type of lung cancer,has been paid more and more attention.The most common pathological type of cystic lung cancer is adenocarcinoma.The invasiveness of cystic lung adenocarcinoma is vital for the selection of clinical treatment and prognosis.The aim of this study is to analyze the multiple clinical features of cystic lung adenocarcinoma,explore the independent risk factors of its invasiveness,and establish a risk pre-diction model.Methods A total of 129 cases of cystic lung adenocarcinoma admitted to the Department of Thoracic Surgery of the First Affiliated Hospital of Nanjing Medical University from January 2021 to July 2022 were retrospectively analyzed and divided into pre-invasive group[atypical adenomatous hyperplasia(AAH),adenocarcinoma in situ(AIS)and minimally invasive adenocarcinoma(MIA)]and invasive group[invasive adenocarcinoma(IAC)]according to pathological findings.There were 47 cases in the pre-invasive group,including 19 males and 28 females,with an average age of(51.23±14.96)years.There were 82 cases in the invasive group,including 60 males and 22 females,with an average age of(61.27±11.74)years.Mul-tiple clinical features of the two groups were collected,including baseline data,imaging data and tumor markers.Univariate analysis,LASSO regression and multivariate Logistic regression analysis were used to screen out the independent risk factors of the invasiveness of cystic lung adenocarcinoma,and the risk prediction model was established.Results In univariate analysis,age,gender,smoking history,history of emphysema,neuron-specific enolase(NSE),number of cystic airspaces,lesion di-ameter,cystic cavity diameter,nodule diameter,solid components diameter,cyst wall nodule,smoothness of cyst wall,shape of cystic airspace,lobulation,short burr sign,pleural retraction,vascular penetration and bronchial penetration were statisti-cally different between the pre-invasive group and invasive groups(P<0.05).The above variables were processed by LASSO regression dimensionality reduction and screened as follows:age,gender,smoking history,NSE,number of cystic airspaces,lesion diameter,cystic cavity diameter,cyst wall nodule,smoothness of cyst wall and lobulation.Then the above variables were included in multivariate Logistic regression analysis.Cyst wall nodule(P=0.035)and lobulation(P=0.001)were found to be independent risk factors for the invasiveness of cystic lung adenocarcinoma(P<0.05).The prediction model was established as follows:P=e^x/(1+e^x),x=-7.927+1.476* cyst wall nodule+2.407* lobulation,and area under the curve(AUC)was 0.950.Conclusion Cyst wall nodule and lobulation are independent risk factors for the invasiveness of cystic lung adenocarcinoma,which have certain guiding significance for the prediction of the invasiveness of cystic lung adenocarcinoma.

Objective:To investigate the correlation between serum proprotein convertase subtilisin/Kexin type 9 (PCSK9) level and white matter hyperintensities (WMHs) in healthy population.Methods:Consecutive healthy individuals underwent routine physical examinations at the Department of Neurology, Jinling Hospital Affiliated to Medical School of Nanjing University (April 2023 to December 2023) and Hexi Branch of Nanjing First Hospital (March 2024 to April 2024) were included prospectively. Enzyme-linked immunosorbent assay was used to detect serum PCSK9 level. The Fazekas scale was used to assess the severity of WMHs (total score 0-6) and they were divided into no or mild WMHs group (0-2) and moderate to severe WMHs group (3-6). Multivariate logistic regression analysis was used to determine the independent correlation between the serum PCSK9 level and the severity of WMHs. Results:A total of 177 subjects were enrolled, including 110 males (62.1%), aged 66.7±10.1 years. The median serum PCSK9 level was 203.9 ng/L. According to the Fazekas score, there were 102 patients (51.6%) in the no or mild WMHs group, and 75 (42.4%) in the moderate to severe WMHs group. One way analysis of variance showed that serum PCSK9 level significantly increased with the increase of WMHs total score ( P=0.001). The serum PCSK9 level in the moderate to severe WMHs group was significantly higher than that in the no or mild WMHs group (437.2±260.4 ng/L vs. 217.9±141.7 ng/L; P=0.001). Multivariate logistic regression analysis showed that after adjusting for age, gender, and other confounding factors, there was a significant independent correlation between higher serum PCSK9 level and moderate to severe WMHs (odds ratio 3.201, 95% confidence interval 2.107-5.082; P=0.001). Conclusion:Higher serum PCSK9 level is an independent risk factor for moderate to severe WMHs in healthy individuals.

Objective:To investigate the application value of the Nottingham Hip Fracture Score (NHFS) in China and establish a formula specifically designed to assess the risk for 30-day mortality after surgery for hip fracture patients in China.Methods:A retrospective study was conducted to analyze the clinical data of 824 hip fracture patients who had been treated at Department of Orthopaedics, The First Hospital Affiliated to Zhengzhou University from August 2019 to May 2022. There were 312 males and 512 females with a median age of 73 (63, 82) years. The clinical data were compared between patients with different survival outcomes. The 30-day mortality was calculated by the formula according to the patients' NHFS, and compared with the actual one to validate the effectiveness of the original prediction model. The patients were divided into a training group ( n=577) and a validation group ( n=247). Binary logistic regression analysis was performed to establish a new prediction model for the patients in the training group. The discrimination, calibration, and clinical effectiveness of the predictive model were assessed in both the training and validation groups. Results:Multivariate logistic regression analysis showed that advanced age (≥86 years old) ( OR=3.775, 95% CI: 1.099 to 12.972, P=0.035), male ( OR=3.151, 95% CI: 1.574 to 6.306, P=0.001), admission hemoglobin concentration ≤100 g/L ( OR=2.402, 95% CI: 1.189 to 4.850, P=0.015), dependence on others for care before admission ( OR=2.673, 95% CI: 1.298 to 5.505, P=0.008), and comorbidities ≥2 ( OR=4.988, 95% CI: 1.874 to 13.274, P=0.001) were identified as risk factors for postoperative 30-day mortality (all P<0.05). In validation of the original prediction model, the C-index was found to be 0.764, indicating good discrimination. However, there was a significant discrepancy between the mortality forecast by the original prediction model and the actual mortality ( P<0.05), indicating poor calibration. After the prediction model was recalibrated, 30-day mortality (%) = 100/[1 + e (5.818-NHFS×0.599)]. After the new prediction model was validated in both the training and validation groups, the C-indexes were 0.762 and 0.780, indicating a good level of discrimination. The predicted 30-day mortality by the prediction model was closely aligned with the actual mortality ( P>0.05), demonstrating good calibration. When the threshold probabilities of the training and the validation groups were 0 to 26% and 0 to 35%, respectively, the patients might benefit from clinical intervention, showing clinical effectiveness of the model. Conclusions:The NHFS can predict the risk for 30-day mortality after hip fracture surgery. The new NHFS prediction model after calibration has a good predictive value for 30-day mortality after hip fracture surgery in Chinese population.