Ye Yu, Chen Jiao, Qiu Xiaoming, Chen Jun, Ming Xianfang, Wang Zhen, Zhou Xin, Song Lei
Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Huangshi, China.
GE Health Care, Wuhan, China.
Heliyon. 2024 Apr 24;10(9):e30214. doi: 10.1016/j.heliyon.2024.e30214. eCollection 2024 May 15.
Accumulating small unruptured intracranial aneurysms are detected due to the improved quality and higher frequency of cranial imaging, but treatment remains controversial. While surgery or endovascular treatment is effective for small aneurysms with a high risk of rupture, such interventions are unnecessary for aneurysms with a low risk of rupture. Consequently, it is imperative to accurately identify small aneurysms with a low risk of rupture. The purpose of this study was to develop a clinically practical model to predict small aneurysm ruptures based on a radiomics signature and clinical risk factors.
A total of 293 patients having an aneurysm with a diameter of less than 5 mm, including 199 patients (67.9 %) with a ruptured aneurysm and 94 patients (32.1 %) without a ruptured aneurysm, were included in this study. Digital subtraction angiography or surgical treatment was required in all cases. Data on the clinical risk factors and the features on computed tomography angiography images associated with the aneurysm rupture status were collected simultaneously. We developed a clinical-radiomics model to predict aneurysm rupture status using multivariate logistic regression analysis. The combined clinical-radiomics model was constructed by nomogram analysis. The diagnostic performance, clinical utility, and model calibration were evaluated by operating characteristic curve analysis, decision curve analysis, and calibration analysis.
A combined clinical-radiomics model (Area Under Curve [AUC], 0.85; 95 % confidence interval [CI], 0.757-0.947) showed effective performance in the operating characteristic curve analysis. In the validation cohort, the performance of the combined model was better than that of the radiomics model (AUC, 0.75; 95 % CI, 0.645-0.865; Delong's test p-value = 0.01) and the clinical model (AUC, 0.74; 95 % CI, 0.625-0.851; Delong's test p-value <0.01) alone. The results of the decision curve, nomogram, and calibration analyses demonstrated the clinical utility and good fitness of the combined model.
Our study demonstrated the effectiveness of a clinical-radiomics model for predicting rupture status in small aneurysms.
由于头颅成像质量的提高和检查频率的增加，越来越多的小型未破裂颅内动脉瘤被检测出来，但对于其治疗仍存在争议。虽然手术或血管内治疗对于具有高破裂风险的小型动脉瘤是有效的，但对于低破裂风险的动脉瘤，此类干预是不必要的。因此，准确识别低破裂风险的小型动脉瘤至关重要。本研究的目的是基于影像组学特征和临床风险因素开发一种临床实用的模型来预测小型动脉瘤破裂。
本研究共纳入293例直径小于5毫米的动脉瘤患者，其中199例（67.9%）为破裂动脉瘤患者，94例（32.1%）为未破裂动脉瘤患者。所有病例均需进行数字减影血管造影或手术治疗。同时收集临床风险因素以及与动脉瘤破裂状态相关的计算机断层血管造影图像特征数据。我们使用多因素逻辑回归分析开发了一个临床-影像组学模型来预测动脉瘤破裂状态。通过列线图分析构建联合临床-影像组学模型。通过操作特征曲线分析、决策曲线分析和校准分析评估诊断性能、临床实用性和模型校准。
联合临床-影像组学模型（曲线下面积[AUC]，0.85；95%置信区间[CI]，0.757-0.947）在操作特征曲线分析中显示出有效性能。在验证队列中，联合模型的性能优于单独的影像组学模型（AUC，0.75；95%CI，0.645-0.865；德龙检验p值=0.01）和临床模型（AUC，0.74；95%CI，0.625-0.851；德龙检验p值<0.01）。决策曲线、列线图和校准分析结果证明了联合模型的临床实用性和良好拟合度。
我们的研究证明了临床-影像组学模型在预测小型动脉瘤破裂状态方面的有效性。
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