Zeyang Yao1, Xinrong Hu2, Xiaobing Liu3, Wen Xie1, Yuhao Dong4, Hailong Qiu3, Zewen Chen3, Yiyu Shi2, Xiaowei Xu5, Meiping Huang6, Jian Zhuang7. 1. School of Medicine, South China University of Technology Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. 2. Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. 3. Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. 4. Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. 5. Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. xiao.wei.xu@foxmail.com. 6. Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. huangmeiping@126.com. 7. Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Dongchuan Rd 96, Guangzhou, 510080, China. zhuangjian5413@163.com.
Abstract
PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT images jointly. METHOD: Patients diagnosed with supracardiac TPAVC from January 1, 2009, to December 31, 2018, in Guangdong Province People's Hospital were enrolled. Logistic regression were applied for clinical data features selection, while a convolutional neural network was used to extract CT images features. The prediction model was established by integrating the above two kinds of features for PVO prediction. And the proposed methods were evaluated using fourfold cross-validation. RESULT: Finally, 131 patients were enrolled in our study. Results show that compared with traditional approaches, the machine learning-based joint method using clinical data and CT image achieved the highest average AUC score of 0.943. In addition, the joint method also achieved a higher sensitivity of 0.828 and a higher positive prediction value of 0.864. CONCLUSION: Using clinical data and CT images jointly can improve the performance significantly compared with other methods that using only clinical data or CT images. The proposed machine learning-based joint method demonstrates the practicability of fully using multi-modality clinical data.
PURPOSE: In this study, we try to consider the most common type of total anomalous pulmonary venous connection and established a machine learning-based prediction model for postoperative pulmonary venous obstruction by using clinical data and CT images jointly. METHOD:Patients diagnosed with supracardiac TPAVC from January 1, 2009, to December 31, 2018, in Guangdong Province People's Hospital were enrolled. Logistic regression were applied for clinical data features selection, while a convolutional neural network was used to extract CT images features. The prediction model was established by integrating the above two kinds of features for PVO prediction. And the proposed methods were evaluated using fourfold cross-validation. RESULT: Finally, 131 patients were enrolled in our study. Results show that compared with traditional approaches, the machine learning-based joint method using clinical data and CT image achieved the highest average AUC score of 0.943. In addition, the joint method also achieved a higher sensitivity of 0.828 and a higher positive prediction value of 0.864. CONCLUSION: Using clinical data and CT images jointly can improve the performance significantly compared with other methods that using only clinical data or CT images. The proposed machine learning-based joint method demonstrates the practicability of fully using multi-modality clinical data.
Entities:
Keywords:
Deep learning; Prediction; Pulmonary venous obstruction; Total anomalous pulmonary venous connection