Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI

Abstract Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To dat...

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Autores principales: Ka Young Shim, Sung Won Chung, Jae Hak Jeong, Inpyeong Hwang, Chul-Kee Park, Tae Min Kim, Sung-Hye Park, Jae Kyung Won, Joo Ho Lee, Soon-Tae Lee, Roh-Eul Yoo, Koung Mi Kang, Tae Jin Yun, Ji-Hoon Kim, Chul-Ho Sohn, Kyu Sung Choi, Seung Hong Choi
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/980697b8918e4f6fac400772d53da387
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https://doaj.org/article/980697b8918e4f6fac400772d53da387

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