A CT-based radiomics model for predicting lymph node metastasis in hepatic alveolar echinococcosis patients to support lymph node dissection.

BACKGROUND: Hepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients. METHODS: A total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: A total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation. CONCLUSION: In this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.

Blood metabolites mediate the causal relationship between circulating CX3CL1 levels and prostate cancer: A 2-step Mendelian randomization study.

Chemokines influence the progression of prostate cancer (PCa) through multiple mechanisms. However, the effect of C-X3-C chemokine ligand 1 (CX3CL1) on PCa risk remains controversial. Our study aimed to investigate whether circulating CX3CL1 is causally associated with PCa and to identify metabolites that have mediating effects using the 2-step bidirectional Mendelian randomization (MR) analysis process. Inverse variance weighting (IVW) results were used as the primary observations, while additional sensitivity analyses were conducted. For each standard deviation increase exhibited by the circulating CX3CL1 levels, the risk of PCa was reduced by 0.4% (IVW: OR = 0.996, [95% CI = 0.994-0.998], P < .001), and blood alliin levels increased by 19% (IVW: OR = 1.185, [95% CI = 1.01-1.54], P = .003). For each standard deviation increase in the blood alliin levels, the risk of PCa was reduced by 0.1% (IVW: OR = 0.999, [95% CI = 0.997-0.999], P = .03). Therefore, the protective effect of circulating CX3CL1 on PCa may be mediated by blood alliin levels (mediated proportion = 6.7%). The results supported the notion that high levels of circulating CX3CL1 indicate a lower PCa risk and the idea that the food-derived antioxidant alliin may mediate this association. We emphasize that the use of CX3CL1 as a protective factor against PCa may provide new strategies for PCa prevention and care in the future.