| Autor: |
Junyan Liu, Md Tauhidul Islam, Shengtian Sang, Liang Qiu, Lei Xing |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
npj Precision Oncology, Vol 7, Iss 1, Pp 1-10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2397-768X |
| DOI: |
10.1038/s41698-023-00468-8 |
| Popis: |
Abstract The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The “black box” nature of deep learning is a major concern in healthcare field. This model’s interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a ‘black box’ approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink