MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

Analysis of Autophagy-Related Signatures in The Tumor Immune Microenvironment and Identification of Clinical Prognostic Markers in Bladder Carcinoma

Objectives: Bladder carcinoma (BLCA) is one of the most common malignant diseases of urinary system. Our study aimed to investigate the autophagy-related signatures in the tumor immune microenvironment and construct effective prognosis prediction model.Methods: RNA sequencing data and corresponding clinical information of BLCA were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Autophagy-related genes were extracted from TCGA dataset for consensus clustering analysis, and differences in survival rate were analyzed. STIMATE algorithm was used to analyze the tumor microenvironment (TME) and immune cell infiltration was compared between different clusters. Differentially expressed genes (DEGs) between different clusters were identified, followed by function annotation. Independent prognostic signatures were further revealed to construct prognostic prediction model.Results: We identified 35 autophagy-related genes associated with prognosis. Survival rate of samples in cluster 1 was significant lower than that in cluster 2. Cluster 2 had markedly lower tumor purity and significantly higher estimate score and stromal score than cluster 1. The proportions of T cells CD8, macrophages M1, T cells CD4 memory activated, NK cells activated, and dendritic cells activated were higher in cluster 1. There were 1,275 DEGs which were mainly enriched in functions and pathways related to immune response and cancer. Seven genes (ATF6, CAPN2, NAMPT, NPC1, P4HB, PIK3C3, and RPTOR) were further identified as the independent prognostic signatures to construct risk score prediction model, which had good prediction performance.Conclusion: Prognosis prediction model based on 7 autophagy-related genes may have great value in BLCA prognosis prediction.

A Novel Prognostic Signature Based on Ferroptosis-Related Genes Predicts the Prognosis of Patients With Advanced Bladder Urothelial Carcinoma

Bladder Urothelial Carcinoma (BLCA) is the major subtype of bladder cancer, and the prognosis prediction of BLCA is difficult. Ferroptosis is a newly discovered iron-dependent cell death pathway. However, the clinical value of ferroptosis-related genes (FRGs) on the prediction of BLCA prognosis is still uncertain. In this study, we aimed to construct a novel prognostic signature to improve the prognosis prediction of advanced BLCA based on FRGs. In the TCGA cohort, we identified 23 differentially expressed genes (DEGs) associated with overall survival (OS) via univariate Cox analysis (all P < 0.05). 8 optimal DEGs were finally screened to generate the prognostic risk signature through LASSO regression analysis. Patients were divided into two risk groups based on the median risk score. Survival analyses revealed that the OS rate in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was determined as an independent predictor of OS by the multivariate Cox regression analysis (Hazard ratio > 1, 95% CI = 1.724-2.943, P < 0.05). Many potential ferroptosis-related pathways were identified in the enrichment analysis in BLCA. With the aid of an external FAHWMU cohort (n = 180), the clinical predication value of the signature was further verified. In conclusion, the prognosis of advanced BLCA could be accurately predicted by this novel FRG-signature.

Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.