Academic Journal
The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions
| العنوان: | The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions |
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| المؤلفون: | Yin, Shengnan, Ding, Ning, Ji, Yiding, Qiao, Zhenguo, Yuan, Jianmao, Chi, Jing, Jin, Long |
| المساهمون: | Suzhou "Science and Education" Youth Science and Technology Project, Jiangsu Medical Association Roentgen Imaging Research Fund Project |
| المصدر: | Acta Radiologica ; volume 65, issue 6, page 554-564 ; ISSN 0284-1851 1600-0455 |
| بيانات النشر: | SAGE Publications |
| سنة النشر: | 2024 |
| الوصف: | Background Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery. Purpose To investigate the potential of various machine learning models, incorporating radiomics and deep transfer learning, in predicting the nature of cholesterol and adenomatous gallbladder polyps. Material and Methods A retrospective analysis was conducted on clinical and imaging data from 100 patients with cholesterol or adenomatous polyps confirmed by surgery and pathology at our hospital between September 2015 and February 2023. Preoperative contrast-enhanced CT radiomics combined with deep learning features were utilized, and t-tests and least absolute shrinkage and selection operator (LASSO) cross-validation were employed for feature selection. Subsequently, 11 machine learning algorithms were utilized to construct prediction models, and the area under the ROC curve (AUC), accuracy, and F1 measure were used to assess model performance, which was validated in a validation group. Results The Logistic algorithm demonstrated the most effective prediction in identifying polyp properties based on 10 radiomics combined with deep learning features, achieving the highest AUC (0.85 in the validation group, 95% confidence interval = 0.68–1.0). In addition, the accuracy (0.83 in the validation group) and F1 measure (0.76 in the validation group) also indicated strong performance. Conclusion The machine learning radiomics combined with deep learning model based on enhanced CT proves valuable in predicting the characteristics of cholesterol and adenomatous gallbladder polyps. This approach provides a more reliable basis for preoperative diagnosis and treatment of these conditions. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1177/02841851241245970 |
| الاتاحة: | https://doi.org/10.1177/02841851241245970 https://journals.sagepub.com/doi/pdf/10.1177/02841851241245970 https://journals.sagepub.com/doi/full-xml/10.1177/02841851241245970 |
| Rights: | https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| رقم الانضمام: | edsbas.A63506F8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1177/02841851241245970 |
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