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Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy

Published: 25 September 2024 Publication History

Highlights

Predicting radiation pneumonitis in lung cancer patients undergoing radiotherapy helps improve the management of lung cancer patients.
Combining dosiomics, radiomics, and deep learning enhances the prediction of radiation pneumonitis in lung cancer patients treated with radiotherapy.
The developed artificial intelligence prediction models have enhanced the radiologists' ability to classify radiation pneumonitis.

Abstract

Background and objective

To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management.

Methods

Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction.

Results

The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively.

Conclusions

The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.

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    Information & Contributors

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    Published In

    cover image Computer Methods and Programs in Biomedicine
    Computer Methods and Programs in Biomedicine  Volume 254, Issue C
    Sep 2024
    544 pages

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    Elsevier North-Holland, Inc.

    United States

    Publication History

    Published: 25 September 2024

    Author Tags

    1. Lung cancer
    2. Radiation pneumonitis
    3. Radiomics
    4. Dosiomics
    5. Deep learning

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