| 林丽丹,王晓阳,黄志峰,陈建州,邱思凡,陈雅玲,许尚文.脑功能网络特征联合临床指标机器学习模型预测药物难治性内侧颞叶癫痫患者术后转归[J].中国医学影像技术,2025,41(9):1488~1493 |
| 脑功能网络特征联合临床指标机器学习模型预测药物难治性内侧颞叶癫痫患者术后转归 |
| Machine learning models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy |
| 投稿时间：2025-03-07 修订日期：2025-09-10 |
| DOI：10.13929/j.issn.1003-3289.2025.09.007 |
| 中文关键词: 癫痫,颞叶|磁共振成像|外科手术|治疗转归|预测 |
| 英文关键词:epilepsy, temporal lobe|magnetic resonance imaging|surgical procedures, operative|treatment outcome|forecasting |
| 基金项目:福建省社会发展引导性(重点)项目(2023Y0066)、福建省科技创新联合资金项目(2024Y9647)。 |
| 作者 | 单位 | E-mail | | 林丽丹 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | | | 王晓阳 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | | | 黄志峰 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | | | 陈建州 | 上海联影智能医疗科技有限公司, 上海 201815 | | | 邱思凡 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | | | 陈雅玲 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | | | 许尚文 | 福建医科大学福总临床医学院, 福建 福州 350025
中国人民解放军联勤保障部队第九○○医院放射诊断科, 福建 福州 350025 | xu_swen@163.com |
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| 中文摘要: |
| 目的 观察基于脑功能脑网络特征联合临床指标的机器学习(ML)模型预测药物难治性内侧颞叶癫痫(DR-mTLE)患者术后转归的价值。方法 回顾性纳入接受手术治疗的84例单侧DR-mTLE患者,根据术后1年随访结果将其纳入无癫痫发作(SF)组(n=55)与癫痫发作(NSF)组(n=29);分析临床资料,筛选术后转归独立预测因素。以图论分析法基于术前颅脑静息态功能MRI构建脑功能网络并提取587个特征,采用5折交叉验证划分训练集与测试集,获得与术后转归相关的最优脑功能网络特征;分别采用高斯过程(GP)、逻辑回归(LR)、支持向量机(SVM)及二次判别分析(QDA)分类器,以上述特征联合临床相关独立预测因素构建ML模型,评估各模型预测效能、校准度及临床价值。结果 病程及病灶位置均为术后转归的临床相关独立预测因子(OR=0.928、5.710,P=0.010、0.016)。小脑蚓部3区介数中心性、右侧豆状苍白球度中心性、左侧颞下回节点效率及左侧顶下缘角回节点聚类系数为最优脑功能网络特征。GP、LR、SVM及QDA模型在测试集的平均曲线下面积(AUC)分别为0.868、0.864、0.875及0.870;校准曲线及决策曲线分析显示各ML模型校准度良好、具有较高临床净获益。结论 基于脑功能网络特征联合临床指标的ML模型可有效预测DR-mTLE患者术后转归。 |
| 英文摘要: |
| Objective To observe the value of machine learning (ML) models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy (DR-mTLE). Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free (SF) group (n=55) and non-seizure free (NSF) group (n=29) according to one-year postoperative follow-up. Clinical data were analyzed to screen independent predictors of postoperative outcomes. Based on brain preoperative resting-state functional MRI, brain functional networks were constructed using graph theory analysis, and 587 features were extracted. Five-fold cross validation was used to divide the data into training set and test set, then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected. Combining with clinically relevant independent predictors, ML models were constructed using classifiers including Gaussian process (GP), logistic regression (LR), support vector machine (SVM) and quadratic discriminant analysis (QDA), respectively, and the prediction efficacy, calibration and clinical value of each ML model were evaluated. Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients (OR=0.928, 5.710, P=0.010, 0.016). Four optimal brain function network features were selected, including betweenness centrality of the third zone of cerebellar vermis, degree centrality of right globus pallidus, nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule. The average area under the curve (AUC) of GP, LR, SVM and QDA models in test set was 0.868, 0.864, 0.875 and 0.870, respectively. Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit. Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients. |
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