Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI
المؤلفون المشاركون
Tian, Hongan
Wu, Hui
Wu, Guangyao
Xu, Guobin
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-15
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Objectives.
To investigate the predictors of telomerase reverse transcriptase (TERT) promoter mutations in adults suffered from high-grade glioma (HGG) through radiomics analysis, develop a noninvasive approach to evaluate TERT promoter mutations.
Methods.
126 adult patients with HGG (88 in the training cohort and 38 in the validation cohort) were retrospectively enrolled.
Totally 5064 radiomics features were, respectively, extracted from three VOIs (necrosis, enhanced, and edema) in MRI.
Firstly, an optimal radiomics signature (Radscore) was established based on LASSO regression.
Secondly, univariate and multivariate logistic regression analyses were performed to investigate important potential variables as predictors of TERT promoter mutations.
Besides, multiparameter models were established and evaluated.
Eventually, an optimal model was visualized as radiomics nomogram for clinical evaluations.
Results.
6 radiomics features were selected to build Radscore signature through LASSO regression.
Among them, 5 were from necrotic VOIs and 1 was from enhanced ones.
With univariate and multivariate analysis, necrotic volume percentages of core (CNV), Age, Cho/Cr, Lac, and Radscore were significantly higher in TERTm than in TERTw (p<0.05).
4 models were built in our study.
Compared with Model B (Age, Cho/Cr, Lac, and Radscore), Model A (Age, Cho/Cr, Lac, Radscore, and CNV) has a larger AUC in both training (0.955 vs.
0.917, p=0.049) and validation (0.889 vs.
0.868, p=0.039) cohorts.
It also has higher performances in net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) evaluation.
Conclusively, Model A was visualized as a radiomics nomogram.
Calibration curve shows a good agreement between estimated and actual probabilities.
Conclusions.
Age, Cho/Cr, Lac, CNV, and Radscore are important indicators for TERT promoter mutation predictions in HGG.
Tumor necrosis seems to be closely related to TERT promoter mutations.
Radiomics nomogram based on multiparameter MRI and CNV has higher prediction accuracies.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tian, Hongan& Wu, Hui& Wu, Guangyao& Xu, Guobin. 2020. Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1133517
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tian, Hongan…[et al.]. Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1133517
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tian, Hongan& Wu, Hui& Wu, Guangyao& Xu, Guobin. Noninvasive Prediction of TERT Promoter Mutations in High-Grade Glioma by Radiomics Analysis Based on Multiparameter MRI. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1133517
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1133517
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