Post-stroke cognitive impairment (PSCI) is a common complication of stroke, which seriously affects the quality of life of patients. Therefore, early and accurate prediction of PSCI is crucial for implementing targeted intervention measures. Quantitative electroencephalography (qEEG), as a non-invasive and objective neurophysiological technique, can provide objective basis for early prediction and targeted intervention of PSCI during the critical period of 72 hours to 1 month after stroke onset. This article reviews the current application status of qEEG in predicting PSCI, aiming to provide theoretical basis for early identification of patients with high-risk PSCI.

Objective To compare the accuracy of consolidation/tumor ratio(CTR)measured at different CT thresholds for the prediction of invasiveness in small lung cancer with diameter≤2 cm using artificial intelligence-assisted measurements,and to explore the CTR thresholds and the corresponding CT thresholds for predicting lung cancer invasiveness.Methods Clinical data from 59 lung cancer patients(78 lung nodules in total)treated at Wuwei Hospital of Traditional Chinese Medicine from January 2021 to May 2023 were collected to analyze the prediction efficacy of CTR on invasiveness in small lung cancer with diameter≤2 cm measured at CT thresholds of-400,-350,-300,-250,-200,-150 HU.ROC curves were plotted to determine the optimal critical value for invasiveness prediction,followed by the corresponding CT threshold.Results The highest diagnostic efficacy for the invasiveness of lung nodules was achieved at a CT threshold of-250 HU,with an area under the curve of 0.931,sensitivity of 77.5％,specificity of 100％,and an optimal CTR threshold of 0.322.Conclusion For small lung cancers with a maximum diameter≤2 cm,CTR measured at a CT threshold of-250 HU can accurately predict lung cancer invasiveness.At CTR>0.322,the nodule is more likely to be microinvasive or invasive adenocarcinoma.