التفاصيل البيبلوغرافية
| العنوان: |
3D DenseNet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients With Thymoma |
| المؤلفون: |
Zhenguo Liu, Ying Zhu, Yujie Yuan, Lei Yang, Kefeng Wang, Minghui Wang, Xiaoyu Yang, Xi Wu, Xi Tian, Rongguo Zhang, Bingqi Shen, Honghe Luo, Huiyu Feng, Shiting Feng, Zunfu Ke |
| المصدر: |
Frontiers in Oncology, Vol 11 (2021) |
| بيانات النشر: |
Frontiers Media S.A., 2021. |
| سنة النشر: |
2021 |
| المجموعة: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| مصطلحات موضوعية: |
thymoma, myasthenia gravis, deep learning—artificial neural network, computed tomography, imaging—computed tomography, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| الوصف: |
BackgroundMyasthenia gravis (MG) is the most common paraneoplastic syndromes of thymoma and closely related to thymus abnormalities. Timely detecting of the risk of MG would benefit clinical management and treatment decision for patients with thymoma. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) as a non-invasive method to detect MG in thymoma patients.MethodsA large cohort of 230 thymoma patients in a hospital affiliated with a medical school were enrolled. 182 thymoma patients (81 with MG, 101 without MG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five radiomic models were performed to detect MG in thymoma patients. A comprehensive analysis by integrating machine learning and semantic CT image features, named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model.FindingsBy elaborately comparing the prediction efficacy, the 3D-DenseNet-DL effectively identified MG patients and was superior to other five radiomic models, 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 by the 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.InterpretationOur 3D-DenseNet-DL model can effectively detect MG in patients with thymoma based on preoperative CT imaging. This model may serve as a supplement to the conventional diagnostic criteria for identifying thymoma associated MG. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2234-943X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fonc.2021.631964/full; https://doaj.org/toc/2234-943X |
| DOI: |
10.3389/fonc.2021.631964 |
| URL الوصول: |
https://doaj.org/article/0f226856cde84af8b72b0c0068bdb49d |
| رقم الانضمام: |
edsdoj.0f226856cde84af8b72b0c0068bdb49d |
| قاعدة البيانات: |
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