Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas
المؤلفون المشاركون
Zeng, Weiming
Deng, Jin
Shi, Yuhu
Guo, Shunjie
Kong, Wei
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-05
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively.
However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity.
The multimodal approach has shown effective application prospects in solving these problems.
In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs).
Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network.
The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone.
Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively.
Therefore, the fusing features have the potential to predict lung metastasis for STSs.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Deng, Jin& Zeng, Weiming& Shi, Yuhu& Kong, Wei& Guo, Shunjie. 2020. Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139599
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Deng, Jin…[et al.]. Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1139599
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Deng, Jin& Zeng, Weiming& Shi, Yuhu& Kong, Wei& Guo, Shunjie. Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1139599
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139599
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر