Cao Xin, Zeng Yanwei, Wang Junying, Cao Yunxi, Wu Yifan, Xia Wei
Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China.
Greater Bay Area Institute of Precision Medicine (Guangzhou), Guangzhou 511466, China.
J Clin Med. 2022 Jun 23;11(13):3623. doi: 10.3390/jcm11133623.
The differential diagnosis of a cerebral dissecting aneurysm (DA) and a hemorrhagic saccular aneurysm (SA) often depends on the intraoperative findings; thus, improved non-invasive imaging diagnosis before surgery is essential to distinguish between these two aneurysms, in order to provide the correct formulation of surgical procedure. We aimed to build a radiomic model based on high-resolution vessel wall magnetic resonance imaging (VW-MRI) and a machine-learning algorithm. In total, 851 radiomic features from 146 cases were analyzed retrospectively, and the ElasticNet algorithm was used to establish the radiomic model in a training set of 77 cases. A clinico-radiological model using clinical features and MRI features was also built. Then an integrated model was built by combining the radiomic model and clinico-radiological model. The area under the ROC curve (AUC) was used to quantify the performance of models. The models were evaluated using leave-one-out cross-validation in a training set, and further validated in an external test set of 69 cases. The diagnostic performance of experienced radiologists was also assessed for comparison. Eight features were used to establish the radiomic model, and the radiomic model performs better (AUC = 0.831) than the clinico-radiological model (AUC = 0.717), integrated model (AUC = 0.813), and even experienced radiologists (AUC = 0.801). Therefore, a radiomic model based on VW-MRI can reliably be used to distinguish DA and hemorrhagic SA, and, thus, be widely applied in clinical practice.
大脑夹层动脉瘤（DA）和出血性囊状动脉瘤（SA）的鉴别诊断通常取决于术中所见；因此，术前改进的非侵入性影像诊断对于区分这两种动脉瘤至关重要，以便正确制定手术方案。我们旨在基于高分辨率血管壁磁共振成像（VW-MRI）和机器学习算法构建一个放射组学模型。回顾性分析了146例患者的851个放射组学特征，并使用弹性网络算法在77例患者的训练集中建立放射组学模型。还构建了一个使用临床特征和MRI特征的临床-放射学模型。然后将放射组学模型和临床-放射学模型相结合构建一个综合模型。使用ROC曲线下面积（AUC）来量化模型的性能。在训练集中使用留一法交叉验证对模型进行评估，并在69例患者的外部测试集中进一步验证。还评估了经验丰富的放射科医生的诊断性能以作比较。使用八个特征建立了放射组学模型，该放射组学模型的表现优于临床-放射学模型（AUC = 0.717）、综合模型（AUC = 0.813），甚至经验丰富的放射科医生（AUC = 0.801）（AUC = 0.831）。因此，基于VW-MRI的放射组学模型可可靠地用于区分DA和出血性SA，从而在临床实践中得到广泛应用。
