Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its...

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Автори:	Shengyu Fang, Ziwen Fan, Zhiyan Sun, Yiming Li, Xing Liu, Yuchao Liang, Yukun Liu, Chunyao Zhou, Qiang Zhu, Hong Zhang, Tianshi Li, Shaowu Li, Tao Jiang, Yinyan Wang, Lei Wang
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Frontiers Media S.A. 2021-02-01
Серія:	Frontiers in Oncology
Предмети:	low-grade glioma machine-learning nested cross-validation radiomics TERT promoter mutation
Онлайн доступ:	https://www.frontiersin.org/articles/10.3389/fonc.2020.606741/full
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