Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images

• Published in: Journal of thrombosis and thrombolysis
• Main Authors: Wang, Dawei, Chen, Rong, Wang, Wenjiang, Yang, Yue, Yu, Yaxi, Liu, Lan, Yang, Fei, Cui, Shujun
• Format: Journal Article
• Language: English
• Published: Netherlands 28-09-2024
• Subjects: Deep learning; Acute pulmonary embolism; Tomography, X-ray computed; Machine learning; Short-term adverse outcomes
• ISSN: 1573-742X; 1573-742X
• DOI: 10.1007/s11239-024-03044-4

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.