Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

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

Tumor heterogeneity is a central cause of treatment resistance and poor prognosis.Habitat imaging,an emerging paradigm in medical image analysis,decomposes tumors into distinct subregions-known as"habitats"-with varying pathophysiological features through unsupervised clustering and other computational methods.This approach enables spatial visualization and quantitative characterization of the tumor microenvironment,offering a feasible tool for non-invasive assessment of tumor heterogeneity.This commentary outlines the technical principles of habitat imaging,elaborates on its methodology for generating habitat maps based on multimodal imaging and clustering algorithms,and highlights its performance-particularly when integrated with radiomics-in predicting treatment response,survival outcomes and genetic profiles.Although it shows promising potential across various cancer types,including glioblastoma and breast cancer,the clinical translation of habitat imaging still faces multiple challenges.Future progress will require standardization,improved biological validation and rigorous clinical trials to advance precision oncology.

Tumor heterogeneity is a central cause of treatment resistance and poor prognosis.Habitat imaging,an emerging paradigm in medical image analysis,decomposes tumors into distinct subregions-known as"habitats"-with varying pathophysiological features through unsupervised clustering and other computational methods.This approach enables spatial visualization and quantitative characterization of the tumor microenvironment,offering a feasible tool for non-invasive assessment of tumor heterogeneity.This commentary outlines the technical principles of habitat imaging,elaborates on its methodology for generating habitat maps based on multimodal imaging and clustering algorithms,and highlights its performance-particularly when integrated with radiomics-in predicting treatment response,survival outcomes and genetic profiles.Although it shows promising potential across various cancer types,including glioblastoma and breast cancer,the clinical translation of habitat imaging still faces multiple challenges.Future progress will require standardization,improved biological validation and rigorous clinical trials to advance precision oncology.

Objective To investigate the effects of different inspired oxygen concentrations on periop-erative cerebrovascular function in patients with a history of ischemic stroke.Methods A total of 150 patients scheduled for elective surgery with a history of ischemic stroke were enrolled from Renji Hospital,Shanghai Jiao Tong University School of Medicine,between June 2020 and March 2024.Using a random number table,patients were allocated into two groups:F30 group(intraoperative fraction of inspired oxygen,FiO2=30%)and F80 group(FiO2=80%),with 75 patients in each group.Bilateral middle cerebral artery(MCA)blood flow was continu-ously monitored using transcranial Doppler(TCD),including mean flow velocity(Vm),resistance index(RI),and pulsatility index(PI).Cerebral oxygen saturation(rScO2)was measured using a FORE-SIGHT oximeter.Arterial blood gas analysis was performed preoperatively,1 hour after induction,and before extubation to assess pH,partial pressure of arterial carbon dioxide(PaCO2),oxygenation index(OI),base excess(BE),hematocrit(Hct),and lactate(Lac).Peripheral blood samples were collected 24 hours postoperatively to measure thrombox-ane A2(TXA2)and prostacyclin(PGI2)levels.At 1 month postoperatively,telephone follow-up was conducted to evaluate the risk of recurrent cerebral ischemic events using the ABCD2 score and Essen Stroke Risk Score(ESRS).Results No significant differences were observed in baseline characteristics between the two groups.Perioperative arterial blood gas parameters did not differ significantly between groups(P>0.05).Compared with the F30 group,the F80 group exhibited a smaller reduction in mean flow velocity(Vm)of the affected MCA at the end of surgery(8.18%±3.34%vs.13.57%±5.32%,P<0.05),while no significant intergroup differences were found in RI or PI.At 1 hour after induction and before extubation,rScO2 of the affected hemisphere was signifi-cantly increased in the F80 group as compared with the F30 group(P<0.05),whereas no significant difference was observed in the contralateral hemisphere.Before extubation and on postoperative day 1,TXA2 levels were significantly lower and PGI2 levels higher in F80 group compared with F30 group(P<0.05).The proportion of patients at high risk of cerebral ischemia by ABCD2 and ESRS at 1 month postoperatively did not differ between groups(P>0.05).Conclusion In patients with a history of stroke,intraoperative administration of 80%FiO2under general anesthesia better maintains perioperative cerebral hemodynamic stability and cerebral oxygen satura-tion,improves cerebrovascular endothelial function,but does not significantly affect the short-term incidence of postoperative cerebrovascular events compared with 30%FiO2.

Objective:To investigate the targets and mechanisms of cycloastragenol in ameliorating azithromycin-induced drug-induced liver injury(DILI)based on network pharmacology and in vitro experiment validation.Methods:Potential targets of cycloastragenol and DILI were predicted using databases.The common and key targets were screened and subjected to GO and KEGG enrichment analyses,as well as molecular docking validation.Primary hepatocytes from C57BL/6 mice were isolated.The optimal concentration and time for azithromycin-induced DILI in mouse primary hepatocytes were determined using CCK8 and ROS assays.The expression of genes and proteins such as NF-κB p65,p-NF-κB p65,AMPKα,and p-AMPKα was assessed using RT-qPCR and Western blot to evaluate the intervention effect of cycloastragenol(10-50 μmol/L).Results:Network pharmacology analysis identified 10 key genes related to cycloastragenol's improvement of DILI,including heat shock protein 90AA1(HSP90AA1),matrix metalloproteinase 2(MMP2),etc.GO enrichment analysis suggested that cycloastragenol primarily regulates biological processes such as membrane potential and chemical synaptic transmission,and affects cellular components such as neuronal cell bodies and distal axons,and related kinase activities.KEGG enrichment analysis showed that it mainly exerts intervention effects through neuro-signaling pathways and IL-17 signaling pathways.Molecular docking demonstrated strong binding of cycloastragenol to HSP90AA1,MMP2,NF-κB p65,AMPKα,nuclear factor erythroid 2-related factor 2(Nrf2),heme oxygenase 1(HO-1),and NAD(P)H:quinone oxidoreductase 1(NQO1),with a binding energy≤-5.0 kcal/mol for Nrf2.In vitro experiments showed that azithromycin(50 μmol/L,12 h)significantly reduced hepatocyte viability and increased ROS levels(P＜0.01).Different concentrations of cycloastragenol significantly improved the activity of mouse primary hepatocytes,reduced the generation of intracellular ROS,downregulated the phosphorylation level of NF-κB p65,and upregulated the mRNA and protein levels of AMPKα,Nrf2,HO-1,NQO1(P＜0.05).Conclusions:Cycloastragenol may alleviate azithromycin-induced hepatocyte oxidative stress and inflammation by inhibiting NF-κB phosphorylation and activating the AMPK/Nrf2/HO-1/NQO1 pathway,with its mechanism likely closely linked to targeting Nrf2.However,the complex mechanisms of DILI may involve additional unverified pathways.Therefore,further studies are necessary to validate the efficacy and safety of cycloastragenol in animal models.

Objective To investigate the effects of different inspired oxygen concentrations on periop-erative cerebrovascular function in patients with a history of ischemic stroke.Methods A total of 150 patients scheduled for elective surgery with a history of ischemic stroke were enrolled from Renji Hospital,Shanghai Jiao Tong University School of Medicine,between June 2020 and March 2024.Using a random number table,patients were allocated into two groups:F30 group(intraoperative fraction of inspired oxygen,FiO2=30%)and F80 group(FiO2=80%),with 75 patients in each group.Bilateral middle cerebral artery(MCA)blood flow was continu-ously monitored using transcranial Doppler(TCD),including mean flow velocity(Vm),resistance index(RI),and pulsatility index(PI).Cerebral oxygen saturation(rScO2)was measured using a FORE-SIGHT oximeter.Arterial blood gas analysis was performed preoperatively,1 hour after induction,and before extubation to assess pH,partial pressure of arterial carbon dioxide(PaCO2),oxygenation index(OI),base excess(BE),hematocrit(Hct),and lactate(Lac).Peripheral blood samples were collected 24 hours postoperatively to measure thrombox-ane A2(TXA2)and prostacyclin(PGI2)levels.At 1 month postoperatively,telephone follow-up was conducted to evaluate the risk of recurrent cerebral ischemic events using the ABCD2 score and Essen Stroke Risk Score(ESRS).Results No significant differences were observed in baseline characteristics between the two groups.Perioperative arterial blood gas parameters did not differ significantly between groups(P>0.05).Compared with the F30 group,the F80 group exhibited a smaller reduction in mean flow velocity(Vm)of the affected MCA at the end of surgery(8.18%±3.34%vs.13.57%±5.32%,P<0.05),while no significant intergroup differences were found in RI or PI.At 1 hour after induction and before extubation,rScO2 of the affected hemisphere was signifi-cantly increased in the F80 group as compared with the F30 group(P<0.05),whereas no significant difference was observed in the contralateral hemisphere.Before extubation and on postoperative day 1,TXA2 levels were significantly lower and PGI2 levels higher in F80 group compared with F30 group(P<0.05).The proportion of patients at high risk of cerebral ischemia by ABCD2 and ESRS at 1 month postoperatively did not differ between groups(P>0.05).Conclusion In patients with a history of stroke,intraoperative administration of 80%FiO2under general anesthesia better maintains perioperative cerebral hemodynamic stability and cerebral oxygen satura-tion,improves cerebrovascular endothelial function,but does not significantly affect the short-term incidence of postoperative cerebrovascular events compared with 30%FiO2.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

With the increasing utilization of cancer therapy, the incidence of lung injury associated with these treatments continues to rise. The recognition of pulmonary toxicity related to cancer therapy has become increasingly critical, for which interstitial lung disease (ILD) is a common cause of mortality. Cancer therapy-related ILD (CT-ILD) can result from a variety of treatments including chemotherapy, targeted therapy, immune checkpoint inhibitors, antibody-drug conjugates, and radiotherapy. CT-ILD may progress rapidly and even be life-threatening; therefore, prompt diagnosis and timely treatment are crucial for effective management. This review aims to provide valuable information on the risk factors associated with CT-ILD; elucidate its underlying mechanisms; discuss its clinical features, imaging, and histological manifestations; and emphasize the clinical-related views of its diagnosis. In addition, this review provides an overview of grading, typing, and staging treatment strategies used for the management of CT-ILD.
Humans ; Lung Diseases, Interstitial/diagnosis* ; Neoplasms/therapy* ; Risk Factors ; Immune Checkpoint Inhibitors/adverse effects* ; Antineoplastic Agents/therapeutic use*

Objective:To evaluate the diagnostic value of native T1 mapping in differentiating Oxford classification (MEST-C) scores in patients with IgA nephropathy.Methods:In this prospective study, patients who underwent both T1 mapping and renal biopsy at the First Medical Center of the Chinese PLA General Hospital between April 2023 and October 2024 were consecutively enrolled. Two radiologists, blinded to clinical and pathological information, measured renal T1 mapping parameters, including cortical T1 (cT1), medullary T1 (mT1), the corticomedullary difference (ΔT1), and the corticomedullary ratio (T1 ratio). Clinical and renal biopsy data based on the Oxford classification from patients with IgA nephropathy were collected. The Oxford classification includes five indicators: Mesangial hypercellularity (M), Endocapillary hypercellularity (E), Segmental glomerulosclerosis or adhesion (S), Tubular atrophy/interstitial fibrosis (T), and Cellular or fibrocellular crescents (C). Spearman correlation analysis was applied to evaluate the associations between MEST-C scores and T1 parameters. The diagnostic performance of T1 parameters for discriminating among scores of the Oxford classification was analyzed using the receiver operating characteristic (ROC) curve.Results:A total of 124 patients with IgA nephropathy were included in this study [66 males, 58 females; age 19-70 years, 39 (30, 51) years]. Except for the E indicator, M, S, T, and C were significantly correlated with renal T1 values ( ρ=0.177-0.414, all P<0.05). cT1 showed the best diagnostic efficacy for the S score, with an area under the curve (AUC) of 0.798, a sensitivity of 68.7%, and a specificity of 88.0%. The best T1 parameter for differentiating the T score was the T1 ratio, with an AUC of 0.687, a sensitivity of 57.9%, and a specificity of 79.1%. Conclusion:Native T1 mapping can be used for the non-invasive assessment of the S and T scores in the Oxford classification of patients with IgA nephropathy.