Background: The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma.Methods: A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma.Results: Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation. Conclusions:The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.

Background Positron emission tomography (PET) imaging is a promising molecular neuroimaging technique and has been proposed as one of the criteria for glioma management. However, there is some controversy concerning the diagnostic accuracy of PET using different radiotracers to differentiate between glioma pseudoprogression (PsP) and true progression (TPR). The purpose of this meta-analysis was to systematically evaluate the methodological quality and clinical value of original studies for distinguishing PsP from TPR in glioma. Methods The Medline, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov were searched from inception until September 1, 2022. Retrieved clinical studies only investigated the PsP cases but did not include the cases of radiation necrosis or other treatment-related changes. Eligible studies were screened for data extraction and evaluated by 2 independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A random effects model was used to describe summary receiver operating characteristics. Meta-regression and subgroup analyses were applied to identify any sources of heterogeneity. Results The meta-analysis included 20 studies, comprising 317 (30.9%) patients with PsP and 708 (69.1%) with TPR. The summary sensitivity and specificity of general PET for identifying PsP were 0.86 [95% confidence interval (CI): 0.77–0.91] and 0.84 (95% CI: 0.79–0.88), respectively. The statistical heterogeneity was explained by sample size, study design, World Health Organization (WHO) grade, gold standard, and radiotracer type. The summary sensitivity and specificity of O-(2- 18 F-fluoroethyl)-L-tyrosine ( 18 F-FET PET) were 0.80 (95% CI: 0.68–0.88) and 0.81 (95% CI: 0.75–0.85), respectively. The maximum tumor-to-brain ratio (TBRmax) and the mean tumor-to-brain ratio (TBRmean) both showed excellent diagnostic performance in 18 F-FET studies, the summary sensitivity was 0.83 (95% CI: 0.72–0.91) and 0.79 (95% CI: 0.65–0.98), respectively, and the specificity was 0.76 (95% CI: 0.68–0.84) and 0.78 (95% CI: 0.64–0.88), respectively. Conclusions PET imaging is generally accurate in identifying glioma PsP. Considering the credibility of meta-evidence and the practicability of using radiotracer, 18 F-FET PET holds the highest clinical value, while TBRmax and TBRmean should be regarded as reliable parameters. PET used with the radiotracers and multiple-parameter combinations of PET with magnetic resonance imaging (MRI) and radiomics analysis have broad research and application prospects, whose diagnostic values for identifying glioma PsP warrant further investigation.

A simple and efficient pilot-scale process was developed for the synthesis and purification of α-asaronol ((E)-3′hydroxyasarone). 4.29 kg of α-asaronol 4 (purity 99.92%) was produced in one batch, starting with 2,4,5-trimethoxybenzaldehyde 1 and ethyl hydrogen malonate 2 as raw materials to form intermediate ethyl (E)-3-(2,4,5-trimethoxyphenyl)acrylate 3 (yield 93.3%) by the Knoevenagel condensation reaction, which was then reduced by diisobutylaluminum hydride to produce α-asaronol 4 with a yield of 89.2%. Liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analysis revealed four major impurities in the synthesis process, namely, (2,4,5-trimethoxyphenyl)methanol, 3-(2,4,5-trimethoxyphenyl)propan-1-ol, 5,5′-((1E,1′E)-oxybis(prop-1-ene-3,1-diyl)) bis(1,2,4-trimethoxybenzene), and diethyl 2-(2,4,5-trimethoxybenzyl)malonate. By adapting a commonly used recrystallization process through optimization, a large-scale purification method was developed for the purification of α-asaronol, achieving a purity of 99.92% by recrystallization. The pilot study lays the groundwork for the large-scale, high-yield, and high-purity preparation of the candidate drug.