Li Xue, Cai Yuyan, Chen Xiaoyi, Ming Yue, He Wenzhang, Liu Jing, Pu Huaxia, Chen Xinyue, Peng Liqing
Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
Diagnostics (Basel). 2023 Jul 25;13(15):2474. doi: 10.3390/diagnostics13152474.
Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: 144; test set: 60) atrial fibrillation patients before ablation, including 102 LAAT and 102 circulatory stasis patients. Radiomics software was used to segment whole LAA on single-phase CTA images and extract features. Models were built and compared via a multivariable logistic regression algorithm and area under of the receiver operating characteristic curves (AUCs), respectively. For the radiomics model, radiomics clinical model, radiomics radiological model, and combined model, the AUCs were 0.82, 0.86, 0.90, 0.93 and 0.82, 0.82, 0.84, 0.85 in the training set and the test set, respectively ( < 0.05). One clinical feature (rheumatic heart disease) and four radiological features (transverse diameter of left atrium, volume of left atrium, location of LAA, shape of LAA) were added to the combined model. The combined model exhibited excellent differential diagnostic performances between LAAT and circulatory stasis without increasing extra radiation exposure. The single-phase, CTA-based radiomics analysis shows potential as an effective tool for accurately detecting LAAT in patients with atrial fibrillation before ablation.
在常规临床实践中，仅基于单相计算机断层血管造影（CTA）来区分左心耳血栓（LAAT）和左心耳循环淤滞是困难的。放射组学为它们的识别提供了一个有前景的工具。我们回顾性纳入了204例（训练集：144例；测试集：60例）消融术前的房颤患者，其中包括102例LAAT患者和102例循环淤滞患者。使用放射组学软件在单相CTA图像上分割整个左心耳并提取特征。分别通过多变量逻辑回归算法和受试者操作特征曲线下面积（AUC）构建并比较模型。对于放射组学模型、放射组学临床模型、放射组学放射学模型和联合模型，训练集和测试集的AUC分别为0.82、0.86、0.90、0.93以及0.82、0.82、0.84、0.85（<0.05）。将一个临床特征（风湿性心脏病）和四个放射学特征（左心房横径、左心房容积、左心耳位置、左心耳形状）添加到联合模型中。联合模型在LAAT和循环淤滞之间表现出优异的鉴别诊断性能，且不会增加额外的辐射暴露。基于单相CTA的放射组学分析显示出作为一种有效工具在准确检测消融术前房颤患者LAAT方面的潜力。
