Neoplasms of perivascular epithelioid cells (PEComas) are characterized by epithelioid to spindle cells with eosinophilic to clear cytoplasm, an intimate relationship with blood vessels, and coexpression of myoid and melanocytic immunohistochemical markers. While most reported hepatic PEComas, such as angiomyolipoma (AML), behave in a benign fashion, emerging PEComas cases without typical characteristics require further clarification. We report a case of primary hepatic perivascular epithelioid cell tumors-not otherwise specified (HPEComas-NOS) with untypical pathological and immunohistochemical features compared to those of the benign hepatic AML cases. HPEComas-NOS may represent a special type of PEComas classified as having "malignant potential" or at "high risk of aggressive behavior", suggesting the need for further clarification of hepatic PEComas and long-term follow-up of patients with HPEComas-NOS.
Female ; Humans ; Liver Neoplasms ; Middle Aged ; Perivascular Epithelioid Cell Neoplasms

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

UDP-glucuronosyltransferase 1A1 (UGT1A1) plays a key role in detoxification of many potentially harmful compounds and drugs. UGT1A1 inhibition may bring risks of drug-drug interactions (DDIs), hyperbilirubinemia and drug-induced liver injury. This study aimed to investigate and compare the inhibitory effects of icotinib and erlotinib against UGT1A1, as well as to evaluate their potential DDI risksUGT1A1 inhibition. The results demonstrated that both icotinib and erlotinib are UGT1A1 inhibitors, but the inhibitory effect of icotinib on UGT1A1 is weaker than that of erlotinib. The ICvalues of icotinib and erlotinib against UGT1A1-mediated NCHN--glucuronidation in human liver microsomes (HLMs) were 5.15 and 0.68 μmol/L, respectively. Inhibition kinetic analyses demonstrated that both icotinib and erlotinib were non-competitive inhibitors against UGT1A1-mediated glucuronidation of NCHN in HLMs, with thevalues of 8.55 and 1.23 μmol/L, respectively. Furthermore, their potential DDI risksUGT1A1 inhibition were quantitatively predicted by the ratio of the areas under the concentration-time curve (AUC) of NCHN. These findings are helpful for the medicinal chemists to design and develop next generation tyrosine kinase inhibitors with improved safety, as well as to guide reasonable applications of icotinib and erlotinib in clinic, especially for avoiding their potential DDI risksUGT1A1 inhibition.