Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.
Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis.
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DOI：
10.1038/s41398-021-01286-x
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发表时间：
2021-03-15
影响因子：
6.8
通讯作者：
van Wingen GA
中科院分区：
文献类型：
--
作者：
Cohen SE;Zantvoord JB;Wezenberg BN;Bockting CLH;van Wingen GA
目前还没有工具可以预测患有重度抑郁症（MDD）的患者是否会对某种治疗有反应。磁共振成像（MRI）数据的机器学习分析显示出预测个体患者反应的潜力，这可能使个性化治疗决策成为可能，并提高治疗效果。在这里，我们评估了mri引导下MDD反应预测的准确性。我们对所有使用MRI预测重度抑郁症患者抗抑郁治疗单受试者反应的研究进行了系统回顾和荟萃分析。使用双变量模型计算分类效果，并以曲线下面积、敏感性和特异性表示。此外，我们分析了不同干预措施和MRI模式在分类性能上的差异。对包括957例患者在内的22个样本进行荟萃分析显示，双变量汇总接收者工作曲线下的总面积为0.84 (95% CI 0.81-0.87)，敏感性为77% (95% CI 71-82)，特异性为79% （95% CI 73-84）。尽管电惊厥治疗结果预测的分类性能（n = 285, 80%敏感性，83%特异性）高于药物治疗结果预测（n = 283, 75%敏感性，72%特异性），但不同治疗或MRI模式之间的分类性能无显著差异。利用机器学习分析MRI数据预测治疗反应是有希望的，但尚不应应用于临床实践。未来的研究需要更广泛的样本和外部验证来确定MRI在MDD中实现个性化患者护理的潜力。
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81–0.87), sensitivity of 77% (95% CI 71–82), and specificity of 79% (95% CI 73–84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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影响因子：
10.6
作者：
Kawakami, Norito;Abdulghani, Emad Abdulrazaq;Alonso, Jordi;Bromet, Evelyn J.;Bruffaerts, Ronny;Caldas-de-Almeida, Jose Miguel;Chiu, Wai Tat;de Girolamo, Giovanni;de Graaf, Ron;Fayyad, John;Ferry, Finola;Florescu, Silvia;Gureje, Oye;Hu, Chiyi;Lakoma, Matthew D.;LeBlanc, William;Lee, Sing;Levinson, Daphna;Malhotra, Savita;Matschinger, Herbert;Elena Medina-Mora, Maria;Nakamura, Yosikazu;Browne, Mark A. Oakley;Okoliyski, Michail;Posada-Villa, Jose;Sampson, Nancy A.;Viana, Maria Carmen;Kessler, Ronald C.
通讯作者：
Kessler, Ronald C.
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
1.7
作者：
Costafreda, Sergi G.;Khanna, Akash;Fu, Cynthia H. Y.
通讯作者：
Fu, Cynthia H. Y.
影响因子：
5.3
作者：
Grieve, Stuart M.;Korgaonkar, Mayuresh S.;Rush, A. John
通讯作者：
Rush, A. John
影响因子：
20.8
作者：
Kessler RC;Bromet EJ
通讯作者：
Bromet EJ