Unraveling the Predictors of Enlarged Perivascular Spaces: A Comprehensive Logistic Regression Approach in Cerebral Small Vessel Disease.

Background: This study addresses the predictive modeling of Enlarged Perivascular Spaces (EPVS) in neuroradiology and neurology, focusing on their impact on Cerebral Small Vessel Disease (CSVD) and neurodegenerative disorders. Methods: A retrospective analysis was conducted on 587 neurology inpatients, utilizing LASSO regression for variable selection and logistic regression for model development. The study included comprehensive demographic, medical history, and laboratory data analyses. Results: The model identified key predictors of EPVS, including Age, Hypertension, Stroke, Lipoprotein a, Platelet Large Cell Ratio, Uric Acid, and Albumin to Globulin Ratio. The predictive nomogram demonstrated strong efficacy in EPVS risk assessment, validated through ROC curve analysis, calibration plots, and Decision Curve Analysis. Conclusion: The study presents a novel, robust EPVS predictive model, providing deeper insights into EPVS mechanisms and risk factors. It underscores the potential for early diagnosis and improved management strategies in neuro-radiology and neurology, highlighting the need for future research in diverse populations and longitudinal settings.

Integrating clinical and biochemical markers: a novel nomogram for predicting lacunes in cerebral small vessel disease.

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation.

The diagnostic significance of cerebrospinal fluid cytology and circulating tumor DNA in meningeal carcinomatosis.

Objective: The objective of this research is to investigate the clinical application value of cerebrospinal fluid (CSF) cytology and circulating tumor DNA (ctDNA) in lung adenocarcinoma (LUAD) meningeal metastasis-meningeal carcinomatosis (MC), and to further explore the possible molecular mechanisms and drug treatment targets of LUAD meningeal metastasis by next-generation sequencing (NGS). Methods: We retrospectively analyzed LUAD with MC in 52 patients. CSF cytology was carried out using the slide centrifugation precipitation method and May-Grüwald-Giemsa (MGG) staining. Tumor tissue, plasma and CSF ctDNA of some MC patients were detected by NGS. Results: Of the 52 MC patients, 46 (88.46%) were positive for CSF cytology and 34 (65.38%) were positive for imaging, with statistically significant differences in diagnostic positivity (P < 0.05). In 32 of these patients, CSF cytology, cerebrospinal fluid ctDNA, plasma ctDNA and MRI examination were performed simultaneously, and the positive rates were 84.38, 100, 56.25, and 62.50% respectively, the difference was statistically significant (P < 0.001). Analysis of the NGS profiles of tumor tissues, plasma and CSF of 12 MC patients: the mutated gene with the highest detection rate was epidermal growth factor receptor (EGFR) and the detection rate were 100, 58.33, and 100% respectively in tumor tissues, plasma and CSF, and there were 6 cases of concordance between plasma and tissue EGFR mutation sites, with a concordance rate of 50.00%, and 12 cases of concordance between CSF and tissue EGFR mutation sites, with a concordance rate of 100%. In addition, mutations not found in tissue or plasma were detected in CSF: FH mutation, SETD2 mutation, WT1 mutation, CDKN2A mutation, CDKN2B mutation, and multiple copy number variants (CNV), with the most detected being CDKN2A mutation and MET amplification. Conclusion: CSF cytology is more sensitive than traditional imaging in the diagnosis of meningeal carcinomatosis and has significant advantages in the early screening and diagnosis of MC patients. CSF ctDNA can be used as a complementary diagnostic method to negative results of CSF cytology and MRI, and CSF ctDNA can be used as an important method for liquid biopsy of patients with MC, which has important clinical significance in revealing the possible molecular mechanisms and drug treatment targets of meningeal metastasis of LUAD.