Clinical value of MRI radiomics model in prognosis of patients with acute cerebral infarction
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摘要:
目的 研究讨论扩散加权成像（DWI）序列联合T2 Flair影像组学模型对急性基底节区脑梗死患者非溶栓治疗预后的临床价值。 方法 纳入2021年滨州医学院附属医院神经内科经过头颅MRI扫描的患者78例作为研究对象，患者均经临床症状及辅助检查证实为急性基底节区脑梗死。基于研究对象入院时和非溶栓治疗1周后出院时的改良Rankin量表评分变化，分成预后好组、预后差组。采用3D Slicer软件手动勾画DWI图像及T2 Flair图像高信号的梗死部位区域，采用Radiomics插件对所勾取的影像资料进行组学特征提取，结合患者出入院时的改良Rankin量表评分分别行单因素分析；再通过Logistic回归曲线算法进行多因素分析，利用ROC曲线分析各种因素的预测效能，评估研究对象的预后价值。 结果 DWI、T2 Flair序列双重组合影像组学模型，训练组的曲线下面积（AUC）为0.9189，测试组为0.7846。吸烟和组学积分是急性基底节区脑梗死非溶栓治疗后预后不良试组AUC为的独立危险因素，训练组ROC临床模型的AUC为0.6497，测试组为0.5468。训练组Nomogram模型的AUC为0.9297，测试组AU为0.8154。Nomogram模型与临床模型的差异、Nomogram模型与影像组学模型的差异均有统计学意义（P<0.05）。 结论 联合DWI、T2 Flair序列的影像组学模型可以评估急性脑梗死患者非溶栓治疗的预后，Nomogram模型比临床预测模型以及组学模型的预测效能更好。 -
关键词:
- 改良Rankin量表评分 /
- 弥散加权成像 /
- 影像组学 /
- 急性基底节区脑梗死 /
- 预后
Abstract:Objective To explore the clinical value of the diffusion weighted imaging (DWI) and T2 Flair radiomics model in prognosis of patients with acute cerebral infarction in the basal ganglia region after non-thrombolytic therapy. Methods Seventy-eight patients with acute cerebral infarction in the basal ganglia region which were given the brain MRI scan from the Department of Neurology of Binzhou Medical University Hospital in 2021 were included. They were diagnosed as acute cerebral infarction by the clinical symptoms and auxiliary examinations. According to the changes of modified Rankin scale scores at admission and discharge, the patients who did not accept thrombolytic therapy for one week were divided into good prognosis group and poor prognosis group. Firstly, 3D Slicer was used to delineate the infarct region of high signal intensity in DWI images and T2 Flair images. The Radiomics module was used to collect the radiomics features from the delineated images. Combined with modified Rankin scale scores of patients on admission and discharge, the data were analyzed by univariate analysis and then carried on multivariate analysis respectively by multivariate logistic regression algorithm. The predictive efficacy of various factors was analyzed by ROC curve to assess the prognostic value of the study subjects. Results By establishing the combined radiomics model of 3.0T MRI-DWI sequence and T2 Flair sequence, the area under the ROC curve (AUC) of the prediction model in the training group was 0.9189 respectively, and the prediction model in the validation group was 0.7846. Smoking and rad-scores were found to be independent risk factors for poor prognosis after nonthrombolytic therapy in patients with acute cerebral infarction. In clinical model, the AUC of the training group and the test group were 0.6497 and 0.5468, respectively. The nomogram model was constructed based on these models, and the AUC of the training group and the test group were 0.9297 and 0.8154, respectively. The differences between nomogram model and clinical model, nomogram model and radiography model were statistically significant (P<0.05). Conclusion The radiomics model combined with DWI and T2 Flair sequence can evaluate the prognosis of non- thrombolytic therapy in patients with acute cerebral infarction in the basal ganglia region. Nomogram model has better prediction efficiency than clinical prediction model and omics model. -
图 3 LASSO回归模型影像组学特征系数剖面图
Figure 3. LASSO regression model feature coefficient section.
图 4 影像组学特征及其对应的系数所占比重
Figure 4. The proportion of the feature and its corresponding coefficient.
图 5 组学模型预测急性脑梗死预后的ROC曲线
Figure 5. The ROC curve of predicting ACI prognosis by the OMICS model.
图 7 Nomogram预测急性脑梗死预后的ROC曲线
Figure 7. ROC curve of nomogram predicting prognosis of ACI.
图 8 组学模型、Nomogram及临床模型的决策曲线
Figure 8. Decision curve of group model, nomogram and clinical model.
表 1 临床模型与Nomogram预测效能对比（De-long检验）
Table 1. Comparison of predictive efficacy of clinical models and nomograms (De-long test)
组别 AUC临床模型 AUCNomogram Z P 训练组 0.597 0.93 5.928 < 0.001 测试组 0.559 0.815 2.406 0.016 
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