Zhou Longmin, Jiang Wenting, Hou Pengwei, Cai Mingfa, Li Ziqi, Wang Shousen
Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University (The 900th Hospital), Fuzhou, CHN.
Department of Neurosurgery, School of Public Health, Shenyang Medical College, Shenyang, CHN.
Cureus. 2024 Nov 20;16(11):e74070. doi: 10.7759/cureus.74070. eCollection 2024 Nov.
Cerebral venous thrombosis (CVT) is a rare but significant condition, primarily affecting young adults, especially women. The diagnosis of CVT is challenging due to its nonspecific clinical presentation. Inflammatory biomarkers, such as the systemic immune-inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), may aid in early diagnosis. This study aimed to explore the role of these biomarkers and assess machine learning models for improving diagnostic accuracy.
This study included 100 CVT patients and 50 controls. Data collected included demographic information, biochemical markers, and clinical symptoms. Traditional statistical methods and machine learning models, including decision trees, random forests, AdaBoost, k-nearest neighbors, support vector machines (SVM), and artificial neural networks (ANN), were used to evaluate the diagnostic value of biomarkers.
The SII and NLR levels were significantly higher in CVT patients. The ANN model based on SII and PLR achieved the best diagnostic performance, with an area under the curve (AUC) of 0.94, showing high accuracy and reliability.
Inflammatory biomarkers, particularly SII, have significant predictive value in CVT diagnosis. Machine learning models, especially ANN, show promise in improving diagnostic accuracy. Future studies with larger sample sizes are needed to validate these findings further.
脑静脉血栓形成（CVT）是一种罕见但严重的疾病，主要影响年轻人，尤其是女性。由于其临床表现不具特异性，CVT的诊断具有挑战性。炎症生物标志物，如全身免疫炎症指数（SII）、血小板与淋巴细胞比值（PLR）和中性粒细胞与淋巴细胞比值（NLR），可能有助于早期诊断。本研究旨在探讨这些生物标志物的作用，并评估机器学习模型以提高诊断准确性。
本研究纳入了100例CVT患者和50例对照。收集的数据包括人口统计学信息、生化标志物和临床症状。使用传统统计方法和机器学习模型，包括决策树、随机森林、AdaBoost、k近邻、支持向量机（SVM）和人工神经网络（ANN），来评估生物标志物的诊断价值。
CVT患者的SII和NLR水平显著更高。基于SII和PLR的ANN模型具有最佳诊断性能，曲线下面积（AUC）为0.94，显示出高准确性和可靠性。
炎症生物标志物，尤其是SII，在CVT诊断中具有显著的预测价值。机器学习模型，尤其是ANN，在提高诊断准确性方面显示出前景。需要更大样本量的未来研究来进一步验证这些发现。
