This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
zunfu Ke
Sun Yat-sen University First Affiliated Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Objectives: Thymoma-associated myasthenia gravis (TAMG) is the most common paraneoplastic syndromeof thymoma. The screening of TAMG before thymoma resection is required to avoid severe perioperative complications, especiallyrespiratory failure. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) to detect TAMGin thymoma patients.
Methods:A large cohort of 230 thymoma patientswere enrolled. 182 thymoma patients (81 with TAMG, 101 without TAMG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five machine learning models with radiomics features were performed to detectTAMG in thymoma patients. A comprehensive analysis by integrating 3D-DenseNet-DL model and general CT image features,named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model.
Results: By elaborately comparing the prediction efficacy,the 3D-DenseNet-DL effectively identified TAMG patients, with a mean area under ROC curve (AUC), accuracy, sensitivity and specificity of 0.734, 0.724, 0.787 and 0.672, respectively. The effectiveness of the 3D-DenseNet-DL-based multi-model was further improved as evidenced bythe following metrics: AUC 0.766, accuracy 0.790, sensitivity 0.739 and specificity 0.801. External verification results confirmed the feasibility of this DL-based multi-model with metrics: AUC 0.730, accuracy 0.732, sensitivity 0.700 and specificity 0.690,respectively.
Conclusions: Our 3D-DenseNet-DL model can effectively detect TAMG in patients with thymoma based on preoperative CT images. This model may serve as a non-invasive screening method or as a supplement to the conventional diagnostic criteria for identifyingTAMG.
Key points:
Thymoma-associated myasthenia gravis (TAMG) is a common paraneoplastic syndrome.
3D-DenseNet-DL model can effectively detect TAMG based on preoperative CT images.
This model may serve as a supplement for identifying TAMG.
Keywords
Thymoma, Myasthenia gravis, Artificial Intelligence (Computer Vision Systems), Deep Learning,Tomography, X Ray Computed
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
zunfu Ke
Sun Yat-sen University First Affiliated Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 11 May, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Objectives: Thymoma-associated myasthenia gravis (TAMG) is the most common paraneoplastic syndromeof thymoma. The screening of TAMG before thymoma resection is required to avoid severe perioperative complications, especiallyrespiratory failure. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) to detect TAMGin thymoma patients.
Methods:A large cohort of 230 thymoma patientswere enrolled. 182 thymoma patients (81 with TAMG, 101 without TAMG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five machine learning models with radiomics features were performed to detectTAMG in thymoma patients. A comprehensive analysis by integrating 3D-DenseNet-DL model and general CT image features,named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model.
Results: By elaborately comparing the prediction efficacy,the 3D-DenseNet-DL effectively identified TAMG patients, with a mean area under ROC curve (AUC), accuracy, sensitivity and specificity of 0.734, 0.724, 0.787 and 0.672, respectively. The effectiveness of the 3D-DenseNet-DL-based multi-model was further improved as evidenced bythe following metrics: AUC 0.766, accuracy 0.790, sensitivity 0.739 and specificity 0.801. External verification results confirmed the feasibility of this DL-based multi-model with metrics: AUC 0.730, accuracy 0.732, sensitivity 0.700 and specificity 0.690,respectively.
Conclusions: Our 3D-DenseNet-DL model can effectively detect TAMG in patients with thymoma based on preoperative CT images. This model may serve as a non-invasive screening method or as a supplement to the conventional diagnostic criteria for identifyingTAMG.
Key points:
Thymoma-associated myasthenia gravis (TAMG) is a common paraneoplastic syndrome.
3D-DenseNet-DL model can effectively detect TAMG based on preoperative CT images.
This model may serve as a supplement for identifying TAMG.
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
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