OBJECTIVES Meningioma is the most common brain tumor in adults. Grade 1 meningiomas have excellent prognoses, but grades 2 and 3 usually have worse outcomes, higher recurrence rates, and higher mortality rates. Preoperative determination of tumor grade may be helpful in deciding the type of surgery and the rate of resection. Blood markers have been used to predict the rate of malignancy and prognosis of tumors in different regions, including the brain. The current study investigated the use of blood markers on predicting meningioma grade. PATIENTS AND METHODS Patients with newly diagnosed meningiomas were retrospectively reviewed. Data on the patients' demographics, tumor locations, blood markers, and tumor pathology grades was extracted. The relationship between preoperative findings and tumor grade was statistically analyzed, and using the same findings and an artificial neural network, the accuracy of tumor grade prediction was evaluated. RESULTS This study included 95 patients, 69 cases (72.4 %) of grade 1, 23 cases of grade 2 (24.4 %) and 3 cases of grade 3 (3.2 %) meningiomas. Monocyte and neutrophil counts as well as lymphocyte-to-monocyte ratio (LMR) were significantly different between low grade and high grade meningiomas, with higher monocyte and neutrophil counts and higher LMR associated with high grade meningiomas (p < 0.05). Evaluation of the data with an artificial neural network using RBF with 5 variables (age, monocyte count, LMR, platelet-to-lymphocyte ratio (PLR), and neutrophil count) indicated that tumor grade can be determined with 83 % accuracy using an artificial neural network. CONCLUSION A preoperative high monocyte count and high LMR are associated with high grade meningioma. An artificial neural network using preoperative data can acceptably be used to characterize meningioma tumor grades.

中文翻译：

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人工神经网络术前发现对脑膜瘤分级的预测价值

目的 脑膜瘤是成人中最常见的脑肿瘤。1 级脑膜瘤预后良好，但 2 级和 3 级通常预后更差、复发率更高、死亡率更高。术前确定肿瘤分级可能有助于决定手术类型和切除率。血液标记物已被用于预测不同区域（包括大脑）肿瘤的恶性率和预后。目前的研究调查了血液标志物在预测脑膜瘤分级中的应用。患者和方法 对新诊断的脑膜瘤患者进行回顾性研究。提取了患者的人口统计学、肿瘤位置、血液标志物和肿瘤病理分级的数据。对术前所见与肿瘤分级的关系进行统计学分析，并使用相同的发现和人工神经网络，评估肿瘤分级预测的准确性。结果 本研究共纳入 95 名患者，其中 1 级脑膜瘤 69 例 (72.4 %)、2 级脑膜瘤 23 例 (24.4 %) 和 3 级脑膜瘤 3 例 (3.2 %)。低级别和高级别脑膜瘤的单核细胞和中性粒细胞计数以及淋巴细胞与单核细胞的比率（LMR）显着不同，单核细胞和中性粒细胞计数较高，而高级别脑膜瘤的 LMR 较高（p < 0.05）。使用具有 5 个变量（年龄、单核细胞计数、LMR、血小板与淋巴细胞比率 (PLR) 和中性粒细胞计数）的 RBF 的人工神经网络对数据进行评估表明，使用人工神经网络可以以 83% 的准确度确定肿瘤等级。神经网络。结论 术前高单核细胞计数和高 LMR 与高级别脑膜瘤相关。使用术前数据的人工神经网络可用于表征脑膜瘤肿瘤等级。