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LUADnet: a deep learning model for prediction of clinical outcomes in lung adenocarcinoma based on gene expression signatures

   
@article{TLCR116518,
	author = {Cheng Cheng and Zhanlue Liang and Renjie Xu and Yanlin Gu and Shicheng Wu and Haoyu Wang and Nini Shi and Die Zhang and Huilin Zhong and Yiwen Tao and Weimin Li},
	title = {LUADnet: a deep learning model for prediction of clinical outcomes in lung adenocarcinoma based on gene expression signatures},
	journal = {Translational Lung Cancer Research},
	volume = {15},
	number = {4},
	year = {2026},
	keywords = {},
	abstract = {Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC), and immunochemotherapy is widely utilized in its treatment. However, there are several drawbacks, including immune escape, immune-related adverse events (irAEs), a significant economic burden, and unfavorable outcomes. Therefore, it is crucial to identify patients who are likely to respond to non-immunotherapy.},
	issn = {2226-4477},	url = {https://tlcr.amegroups.org/article/view/116518}
}