Wang Dawei, Chen Rong, Wang Wenjiang, Yang Yue, Yu Yaxi, Liu Lan, Yang Fei, Cui Shujun
Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, 075000, China.
Hebei North University, Zhangjiakou, Hebei, 075000, China.
J Thromb Thrombolysis. 2025 Feb;58(2):331-339. doi: 10.1007/s11239-024-03044-4. Epub 2024 Sep 28.
To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CTPA) images for short-term adverse outcomes in patients with acute pulmonary embolism (APE). This retrospective study enrolled 132 patients with APE confirmed by CTPA. Thrombus segmentation and texture feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature dimensionality reduction and selection, with optimal λ values determined using leave-one-fold cross-validation to identify texture features with non-zero coefficients. ML models (logistic regression, random forest, decision tree, support vector machine) and DL models (ResNet 50 and Vgg 19) were used to construct the prediction models. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The cohort included 84 patients in the good prognosis group and 48 patients in the poor prognosis group. Univariate and multivariate logistic regression analyses showed that diabetes, RV/LV ≥ 1.0, and Qanadli index form independent risk factors predicting poor prognosis in patients with APE(P < 0.05). A total of 750 texture features were extracted, with 4 key features identified through screening. There was a weak positive correlation between texture features and clinical parameters. ROC curves analysis demonstrated AUC values of 0.85 (0.78-0.92), 0.76 (0.67-0.84), and 0.89 (0.83-0.95) for the clinical, texture feature, and combined models, respectively. In the ML models, the random forest model achieved the highest AUC (0.85), and the support vector machine model achieved the lowest AUC (0.62). And the AUCs for the DL models (ResNet 50 and Vgg 19) were 0.91 (95%CI: 0.90-0.92) and 0.94(95%CI: 0.93-0.95), respectively. Vgg 19 model demonstrated exceptional precision (0.93), recall (0.76), specificity (0.95) and F1 score (0.84). Both ML and DL models based on thrombus texture features from CTPA images demonstrated higher predictive efficacy for short-term adverse outcomes in patients with APE, especially the random forest and Vgg 19 models, potentially assisting clinical management in timely interventions to improve patient prognosis.
探讨基于计算机断层扫描肺动脉造影（CTPA）图像的传统机器学习（ML）和深度学习（DL）算法对急性肺栓塞（APE）患者短期不良结局的预测价值。这项回顾性研究纳入了132例经CTPA确诊的APE患者。使用3D-Slicer软件进行血栓分割和纹理特征提取。采用最小绝对收缩和选择算子（LASSO）算法进行特征降维和选择，通过留一法交叉验证确定最优λ值，以识别具有非零系数的纹理特征。使用ML模型（逻辑回归、随机森林、决策树、支持向量机）和DL模型（ResNet 50和Vgg 19）构建预测模型。使用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估模型性能。该队列包括84例预后良好组患者和48例预后不良组患者。单因素和多因素逻辑回归分析显示，糖尿病、右心室/左心室≥1.0以及Qanadli指数是预测APE患者预后不良的独立危险因素（P<0.05）。共提取了750个纹理特征，通过筛选确定了4个关键特征。纹理特征与临床参数之间存在弱正相关。ROC曲线分析显示，临床、纹理特征和联合模型的AUC值分别为0.85（0.78 - 0.92）、0.76（0.67 - 0.84）和0.89（0.83 - 0.95）。在ML模型中，随机森林模型的AUC最高（0.85），支持向量机模型的AUC最低（0.62）。DL模型（ResNet 50和Vgg 19）的AUC分别为0.91（95%CI：0.90 - 0.92）和0.94（95%CI：0.93 - 0.95）。Vgg 19模型表现出出色的精度（0.93）、召回率（0.76）、特异性（0.95）和F1分数（0.84）。基于CTPA图像血栓纹理特征的ML和DL模型对APE患者短期不良结局均显示出较高的预测效能，尤其是随机森林和Vgg 19模型，可能有助于临床管理进行及时干预以改善患者预后。
