Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy

Abstract Background Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the...

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Bibliographic Details
Main Authors: Qi Wan, Clifford Lindsay, Chenxi Zhang, Jisoo Kim, Xin Chen, Jing Li, Raymond Y. Huang, David A. Reardon, Geoffrey S. Young, Lei Qin
Format: Article
Language:English
Published: BMC 2025-01-01
Series:Cancer Imaging
Subjects:
High-grade glioma
Convolutional neural networks
Deep learning
Radiomics
Overall survival
Online Access:https://doi.org/10.1186/s40644-024-00818-0
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https://doi.org/10.1186/s40644-024-00818-0

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