Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression.
Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression.
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DOI：
10.1016/j.neuri.2022.100110
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发表时间：
2022-12-01
期刊：
影响因子：
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通讯作者：
DeLorenzo, Christine
中科院分区：
文献类型：
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作者：
Ali, Farzana Z;Wengler, Kenneth;DeLorenzo, Christine
前言：经2-脱氧-2-[18F]氟-D-葡萄糖(FDG)和磁共振波谱(MRS)预处理的正电子发射断层扫描(PET)可识别预测缓解(无抑郁)的生物标志物。然而，还没有这样的基于图像的生物标志物达到临床有效性。这项研究的目的是利用机器学习(ML)结合FDG-PET/MRS神经成像预处理来识别缓解的生物标志物，以减少无效试验对患者的痛苦和经济负担。方法：在开始治疗之前，对60名严重抑郁障碍(MDD)患者进行双盲、安慰剂对照、随机抗抑郁试验，同时进行PET/MRS神经成像。治疗8周后，那些在17项汉密尔顿抑郁量表上≤7的人被先验地指定为复发者(无抑郁，37%)。从PET获得22个脑区的葡萄糖摄取(代谢)代谢率。从MRS中定量计算前扣带回皮质谷氨酰胺、谷氨酸和γ-氨基丁酸(GABA)的浓度(Mm)。数据随机分为67%训练和交叉验证(n=40)和33%测试(n=20)。影像特征，以及年龄、性别、利手和治疗分配(选择性5-羟色胺再摄取抑制剂或SSRI与安慰剂)被输入极端梯度增强(XGBoost)分类器进行训练。结果：在测试数据中，该模型显示出62%的敏感性、92%的特异性和77%的加权准确性。PET对左侧海马区的预处理代谢最能预测病情缓解。结论：预处理神经成像大约需要60分钟，但有可能防止数周失败的治疗试验。这项研究有效地解决了神经成像分析中常见的问题，如样本量小、维度高和类别不平衡。
Introduction: Pretreatment positron emission tomography (PET) with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials.Methods: This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training.Results: In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission.Conclusions: The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.