Two-stage Cox-nnet: biologically interpretable neural-network model for prognosis prediction and its application in liver cancer survival using histopathology and transcriptomic data.
Journal
NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
received:
31
07
2020
revised:
01
02
2021
accepted:
24
02
2021
entrez:
29
3
2021
pubmed:
30
3
2021
medline:
30
3
2021
Statut:
epublish
Résumé
Pathological images are easily accessible data with the potential of prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Here, we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis prediction model. We compare this model with the conventional Cox proportional hazards (Cox-PH) model, CoxBoost, Random Survival Forests and DeepSurv, using
Identifiants
pubmed: 33778491
doi: 10.1093/nargab/lqab015
pii: lqab015
pmc: PMC7985035
doi:
Types de publication
Journal Article
Langues
eng
Pagination
lqab015Subventions
Organisme : NIEHS NIH HHS
ID : K01 ES025434
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD084633
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012907
Pays : United States
Informations de copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
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