期刊
EBIOMEDICINE
卷 72, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ebiom.2021.103583
关键词
Glioma; Deep learning; Diffusion tensor imaging; Prognosis; Pathway
资金
- National Natural Science Foundation of China [U20A20171, 82102149, 61901458, 81702465, 61571432, U1804172, U1904148]
- Science and Technology Program of Henan Province [182102310113, 192102310123, 192102310050]
- Key Program of Medical Science and Technique Foundation of Henan Province [SBGJ202002062]
- Joint Construction Program of Medical Science and Technique Foundation of Henan Province [LHGJ20190156]
- Youth Innovation Promotion Association of the Chinese Academy of Sciences [2018364]
- Guangdong Key Project [2018B030335001]
A deep learning signature (DLS) from diffusion tensor imaging (DTI) was developed for predicting overall survival in patients with infiltrative gliomas, with five key biological pathways identified. The DLS was associated with survival, independent predictor, and improved survival prediction when incorporated into existing risk system. Therapies targeting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS.
Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. (C) 2021 The Authors. Published by Elsevier B.V.
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