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Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism

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Abstract

This preliminary study investigated the feasibility of a combined model constructed using radiomic features based on computed tomography (CT) and clinical features to predict adverse clinical outcomes in acute pulmonary embolism (APE). Currently, there is no widely recognized predictive model. Patients with confirmed APE who underwent CT pulmonary angiography were retrospectively categorized into good and poor prognosis groups. Seventy-four patients were randomized into a training (n = 51) or validation (n = 23) cohort. Feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator regression was used to identify the optimal radiomics features and calculate the radiomics scores; subsequently, the radiomics model was developed. A combined predictive model was constructed based on radiomics scores and selected clinical features. The predictive efficacy of the three models (radiomics, clinical and combined) was assessed by plotting receiver operating characteristic curves. Furthermore, the calibration curves were graphed and the decision curve analysis was performed. Four radiomic features were screened to calculate the radiomic score. Right ventricular to left ventricular ratio (RV/LV) ≥ 1.0 and radiomics score were independent risk factors for adverse clinical outcomes. In the training and validation cohorts, the areas under the curve (AUCs) for the RV/LV ≥ 1.0 (clinical) and radiomics score prediction models were 0.778 and 0.833 and 0.907 and 0.817, respectively. The AUCs for the combined model of RV/LV ≥ 1.0 and radiomics score were 0.925 and 0.917, respectively. The combined and radiomics models had high clinical assessment efficacy for predicting adverse clinical outcomes in APE, demonstrating the clinical utility of both models. Calibration curves exhibited a strong level of consistency between the predictive and observed probabilities of poor and good prognoses in the combined model. The combined model of RV/LV ≥ 1.0 and radiomics score based on CT could accurately and non-invasively predict adverse clinical outcomes in patients with APE.

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Data availability

The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

Not applicable.

Funding

This study was supported by a grant from the Science and Technology Project of the Hebei Provincial Science and Technology Department (21377769D).

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Authors and Affiliations

  1. Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China

    Fei Yang, Mengmeng Ji & Zhiying Pang

  2. Hebei North University, Zhangjiakou, 075000, Hebei, China

    Rong Chen, Yue Yang & Zhixiang Yang

  3. Department of Nuclear Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China

    Yaying Su

  4. Department of Cardiothoracic Surgery, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, 075000, Hebei, China

    Dawei Wang

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  1. Fei Yang
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  3. Yue Yang
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  4. Zhixiang Yang
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  5. Yaying Su
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  6. Mengmeng Ji
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  8. Dawei Wang
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Contributions

(I) Conception and design: YF and WDW; (II) administrative support: WDW; (III) provision of study materials or patients: JMM and PZY; (IV) collection and assembly of data: CR and YY; (V) data analysis and interpretation: SYY and YZX; (VI) manuscript writing: all authors; (VII) final approval of the manuscript: all authors.

Corresponding author

Correspondence to Dawei Wang.

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The authors declare no conflict of interest.

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This study involving human participants was reviewed and approved by the Institutional Review Board of First Affiliated Hospital of Hebei North University.

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Yang, F., Chen, R., Yang, Y. et al. Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism. J Thromb Thrombolysis 57, 428–436 (2024). https://doi.org/10.1007/s11239-023-02929-0

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