Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images
المؤلفون المشاركون
Huang, Meiyan
Zhang, Quan
Cao, Jianyun
Zhang, Junde
Bu, Junguo
Yu, Yuwei
Tan, Yujing
Feng, Qianjin
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-01
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Purpose.
To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images.
Methods.
Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study.
Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period.
After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans.
A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient.
The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features.
The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features.
Results.
For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set.
These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, p<0.05, except sensitivity).
For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set.
These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, p<0.05).
Conclusions.
Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Quan& Cao, Jianyun& Zhang, Junde& Bu, Junguo& Yu, Yuwei& Tan, Yujing…[et al.]. 2019. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Quan…[et al.]. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Quan& Cao, Jianyun& Zhang, Junde& Bu, Junguo& Yu, Yuwei& Tan, Yujing…[et al.]. Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on the Radiomics of Combinational Features and Multimodality MRI Images. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1130509
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130509
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