Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography.

OBJECTIVES: to predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). METHODS: a total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. RESULTS: four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). CONCLUSIONS: the radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.

Prognosis and chemotherapy drug sensitivity in liver hepatocellular carcinoma through a disulfidptosis-related lncRNA signature.

Disulfidptosis, a new type of regulated cell death associated with the actin cytoskeleton, provides a new therapeutic tool for cancers. The direct relationship between disulfidptosis-related lncRNAs(DRLs) in liver hepatocellular carcinoma(HCC) remains unclear. We acquired transcriptomic data, corresponding clinical data, and tumor mutation data of HCC from the TCGA database. First of all, DRLs were determined through correlation analysis. Then, a prognostic model containing six DRLs was created by adopting univariate Cox regression, LASSO algorithm and multivariate Cox regression analysis. Based on the model, 424 HCC patients were divided into high- and low-risk groups. Next, we structured ROC curves and PCA through combining the model and clinical data. Enrichment analysis and immune infiltration analysis were adopted to further explore the relationship between the model and prognosis. In addition, we explored the relationship between the model and tumor mutation burden (TMB). There were significant differences between high- and low- risk groups, and patients in the high-risk group showed poor prognosis. Enrichment analysis suggested that metabolic progress was obviously different between the two groups. According to the analysis of immune infiltration, there were several differences in immune cells, function, and checkpoints. Patients with high-risk and high TMB demonstrated the least favorable prognosis. The two risk groups both manifested visiblly in chemotherapy drug sensitivity. To sum up, we set up a DRL-based signature and that may provide a predictable value for the prognosis and use of chemotherapy drugs for HCC patients.

Computed tomography angiography-based radiomics model to identify high-risk carotid plaques.

Background: Extracranial atherosclerosis is one of the major causes of stroke. Carotid computed tomography angiography (CTA) is a widely used imaging modality that allows detailed assessments of plaque characteristics. This study aimed to develop and test radiomics models of carotid plaques and perivascular adipose tissue (PVAT) to distinguish symptomatic from asymptomatic plaques and compare the diagnostic value between radiomics models and traditional CTA model. Methods: A total of 144 patients with carotid plaques were divided into symptomatic and asymptomatic groups. The traditional CTA model was built by the traditional radiological features of carotid plaques measured on CTA images which were screened by univariate analysis and multivariable logistic regression. We extracted and screened radiomics features from carotid plaques and PVAT. Then, a support vector machine was used for building plaque and PVAT radiomics models, as well as a combined model using traditional CTA features and radiomics features. The diagnostic value between radiomics models and traditional CTA model was compared in identifying symptomatic carotid plaques by Delong method. Results: The area under curve (AUC) values of traditional CTA model were 0.624 and 0.624 for the training and validation groups, respectively. The plaque radiomics model and PVAT radiomics model achieved AUC values of 0.766, 0.740 and 0.759, 0.618 in the two groups, respectively. Meanwhile, the combined model of plaque and PVAT radiomics features and traditional CTA features had AUC values of 0.883 and 0.840 for the training and validation groups, respectively, and the receiver operating characteristic curves of combined model were significantly better than those of traditional CTA model in the training group (P<0.001) and validation group (P=0.029). Conclusions: The combined model of the radiomics features of carotid plaques and PVAT and the traditional CTA features significantly contributes to identifying high-risk carotid plaques compared with traditional CTA model.

Comparison of the imaging and clinical characteristics between Initial and Recurrent Alcoholic Acute Pancreatitis: a retrospective cross-sectional study.

MATERIALS: Patients with alcoholic acute pancreatitis in our hospital were recruited from Jan 2019 to July 2022 and divided into IAAP and RAAP groups. All patients underwent Contrast-Enhanced Computerized Tomography (CECT) or Magnetic Resonance Imaging (MRI) after administration. Imaging manifestations, local complications, severity scores on the Modified CT/MR Severity Index (MCTSI/MMRSI), Extrapancreatic Inflammation on CT/MR (EPIC/M), clinical severity [Bedside Index for Severity in Acute Pancreatitis (BISAP) Acute Physiology and Chronic Health Evaluation (APACHE-II)], and clinical prognosis were compared between the two groups.Results: 166 patients were recruited for this study, including 134 IAAP (male sex 94%) and 32 RAAP patients (male sex 100%). On CECT or MRI, IAAP patients were more likely to develop ascites and Acute Necrosis collection (ANC) than RAAP patients (ascites:87.3%vs56.2%; P = .01; ANC:38%vs18.7%; P < .05). MCTSI/MMRSI and EPIC/M scores were higher in IAAP than in RAAP patients(MCTSI/MMRSI:6.2vs5.2; P < .05; EPIC/M:5.4vs3.8; P < .05).Clinical severity scores (APACHE-II and BISAP), length of stay, and systemic complications [Systemic Inflammatory Response Syndrome (SIRS), respiratory failure] were higher in the IAAP group than in the RAAP group (P < .05). No mortality outcomes were reported in either group while hospitalized.Conclusions: Patients with IAAP had more severe disease than those with RAAP. These results may be helpful for differentiating care paths for IAAP and RAAP, which are essential for management and timely treatment in clinical practice.

The Complement and Coagulation Cascades Pathway is Associated with Acute Necrotizing Pancreatitis by Genomics and Proteomics Analysis.

Purpose: Acute pancreatitis can be classified histologically as interstitial edema pancreatitis (IEP) or as acute necrotizing pancreatitis (ANP). ANP has a higher mortality and long-term or short-term sequelae than IEP. Therefore, this work aims to explore the differences in pathogenesis between ANP and IEP and it has great clinical importance for the treatment and prevention of ANP. Methods: In this work, whole blood samples from IEP and ANP patients were analyzed by whole gene sequencing (WGS). Serum samples from IEP and ANP patients were evaluated via enzyme-linked immunosorbent assay (ELISA). Meanwhile, pancreatic tissues of IEP and ANP rat models were subjected to data independent acquisition (DIA) proteomics assays. Then, the WGS analysis and DIA proteomics assay data were analyzed comprehensively. Results: Six pathways were found to be significantly different in the ANP/IEP groups through WGS analysis. DIA proteomics found eleven different pathways. In both assays, the complement and coagulation cascades pathway was the most significantly different (p < 0.01) pathway between the two groups. WGS analysis showed base mutations in ten genes in the complement and coagulation cascades pathway. These results were consistent with the ten proteins detected by DIA proteomics analysis, which were significantly upregulated in the ANP/IEP groups. In addition, five of these proteins, complement C3, complement Factor I, alpha-2-macroglobulin, complement C9, and serpin family C member 1, were successfully verified by parallel reaction monitoring analysis and ELISA. Conclusion: C3, CFI, A2m, C9, and Serpinc1, which belong to complement and coagulation cascades pathway, may promote pancreatic necrosis and aggravate the severity of ANP.