Computed tomography-based radiomics predicts the fibroblastrelated gene EZH2 expression level and survival of hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma(HCC)is the most common subtype of liver cancer.The primary treatment strategies for HCC currently include liver transplantation and surgical resection.However,these methods often yield unsatisfactory outcomes,leading to a poor prognosis for many patients.This underscores the urgent need to identify and evaluate novel therapeutic targets that can improve the prognosis and survival rate of HCC patients.AIM To construct a radiomics model that can accurately predict the EZH2 expression in HCC.METHODS Gene expression,clinical parameters,HCC-related radiomics,and fibroblastrelated genes were acquired from public databases.A gene model was developed,and its clinical efficacy was assessed statistically.Drug sensitivity analysis was conducted with identified hub genes.Radiomics features were extracted and machine learning algorithms were employed to generate a radiomics model related to the hub genes.A nomogram was used to illustrate the prognostic significance of the computed Radscore and the hub genes in the context of HCC patient outcomes.RESULTS EZH2 and NRAS were independent predictors for prognosis of HCC and were utilized to construct a predictive gene model.This model demonstrated robust performance in diagnosing HCC and predicted an unfavorable prognosis.A negative correlation was observed between EZH2 expression and drug sensitivity.Elevated EZH2 expression was linked to poorer prognosis,and its diagnostic value in HCC surpassed that of the risk model.A radiomics model,developed using a logistic algorithm,also showed superior efficiency in predicting EZH2 expression.The Radscore was higher in the group with high EZH2 expression.A nomogram was constructed to visually demonstrate the significant roles of the radiomics model and EZH2 expression in predicting the overall survival of HCC patients.CONCLUSION EZH2 plays significant roles in diagnosing HCC and therapeutic efficacy.A radiomics model,developed using a logistic algorithm,efficiently predicted EZH2 expression and exhibited strong correlation with HCC prognosis.

Nomogram for predicting short-term response to anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration:An observational study

BACKGROUND Anti-vascular endothelial growth factor(anti-VEGF)therapy is critical for managing neovascular age-related macular degeneration(nAMD),but understanding factors influencing treatment efficacy is essential for optimizing patient outcomes.AIM To identify the risk factors affecting anti-VEGF treatment efficacy in nAMD and develop a predictive model for short-term response.METHODS In this study,65 eyes of exudative AMD patients after anti-VEGF treatment for≥1 mo were observed using optical coherence tomography angiography.Patients were classified into non-responders(n=22)and responders(n=43).Logistic regression was used to determine independent risk factors for treatment response.A predictive model was created using the Akaike Information Criterion,and its performance was assessed with the area under the receiver operating characteristic curve,calibration curves,and decision curve analysis(DCA)with 500 bootstrap re-samples.RESULTS Multivariable logistic regression analysis identified the number of junction voxels[odds ratio=0.997,95%confidence interval(CI):0.993-0.999,P=0.010]as an independent predictor of positive anti-VEGF treatment outcomes.The predictive model incorporating the fractal dimension,number of junction voxels,and longest shortest path,achieved an area under the curve of 0.753(95%CI:0.622-0.873).Calibration curves confirmed a high agreement between predicted and actual outcomes,and DCA validated the model's clinical utility.CONCLUSION The predictive model effectively forecasts 1-mo therapeutic outcomes for nAMD patients undergoing anti-VEGF therapy,enhancing personalized treatment planning.