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A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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Funding

This work was funded by the National Key Research and Development Program of China [2018YFC1002600], the Science and Technology Planning Project of Guangdong Province, China [No.2017A070701013, 2017B090904034, 2017B030314109, 2018B090944002, 2019B020230003], Guangdong Peak Project [DFJH201802].

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Correspondence to Xiaowei Xu, Meiping Huang or Jian Zhuang.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

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Yao, Z., Hu, X., Liu, X. et al. A machine learning-based pulmonary venous obstruction prediction model using clinical data and CT image. Int J CARS 16, 609–617 (2021). https://doi.org/10.1007/s11548-021-02335-y

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  • DOI: https://doi.org/10.1007/s11548-021-02335-y

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