Department of Radiology, Shaoxing People's Hospital, Shaoxing, China.
Department of Neurology, Shaoxing People's Hospital, Shaoxing, China.
World Neurosurg. 2024 Oct;190:e199-e211. doi: 10.1016/j.wneu.2024.07.090. Epub 2024 Jul 23.
To develop and validate a clinical-radiomics nomogram for predicting early ischemic stroke risk in patients who sustain a transient ischemic attack (TIA).
A retrospective training dataset (n = 76) and a prospective validation dataset (n = 34) of patients with TIA were studied. Image processing was performed using ITK-snap and Artificial Intelligent Kit. Radiomics features were selected in R. A nomogram predicting recurrent TIA/stroke in 90 days as a recurrent ischemic event was established. Model performance was assessed by computing the receiver operating characteristic curve and decision curve analysis (DCA).
We found a higher proportion of diabetes and hypertension in the patients with recurrent TIA compared with the stable patients in both the training and validation datasets (P < 0.05). Recurrent patients had significantly higher ABCD2 scores and plaque scores compared to stable patients. ABCD2 score and necrotic/lipid core area were independent risk factors for recurrent ischemic events (odds ratio [OR], 2.75; 95% confidence interval [CI], 1.47-6.40; and OR, 1.20; 95% CI, 1.07-1.41, respectively). The radiomics model had area under the curve values of 0.737 (95% CI, 0.715-0.878) in the training dataset and 0.899 (95% CI, 0.706-0.936) in the validation dataset, which was superior to the ABCD2 score and plaque model for predicting stroke recurrence (P < 0.05). The nomogram predicting recurrent ischemic events was 0.923 (95% CI, 0.895-0.978) in the training dataset and 0.935 (95% CI, 0.830-0.959) in the validation dataset. DCA confirmed the clinical value of this nomogram.
The nomogram, based on clinical ABCD2 score, carotid plaque components and radiomics score, shows good performance in predicting the risk of recurrent ischemic events in patients with TIA.
开发和验证用于预测短暂性脑缺血发作（TIA）患者早期缺血性卒中风险的临床-放射组学列线图。
本研究纳入了 TIA 患者的回顾性训练数据集（n=76）和前瞻性验证数据集（n=34）。使用 ITK-snap 和人工智能工具包进行图像处理。在 R 中选择放射组学特征。建立预测 90 天内复发性 TIA/卒中作为复发性缺血事件的列线图。通过计算受试者工作特征曲线和决策曲线分析（DCA）评估模型性能。
我们发现，在训练和验证数据集的复发性 TIA 患者中，糖尿病和高血压的比例均高于稳定患者（P<0.05）。复发性患者的 ABCD2 评分和斑块评分明显高于稳定患者。ABCD2 评分和坏死/脂质核心区是复发性缺血事件的独立危险因素（比值比[OR]，2.75；95%置信区间[CI]，1.47-6.40；OR，1.20；95%CI，1.07-1.41）。放射组学模型在训练数据集的曲线下面积为 0.737（95%CI，0.715-0.878），在验证数据集的曲线下面积为 0.899（95%CI，0.706-0.936），优于 ABCD2 评分和斑块模型对预测卒中复发（P<0.05）。训练数据集和验证数据集预测复发性缺血事件的列线图分别为 0.923（95%CI，0.895-0.978）和 0.935（95%CI，0.830-0.959）。DCA 证实了该列线图的临床价值。
基于临床 ABCD2 评分、颈动脉斑块成分和放射组学评分的列线图，在预测 TIA 患者复发性缺血事件风险方面具有良好的性能。
