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Aerts, H.J.W.L., Velazquez, E.R., Leijenaar, R.T.H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., et al. (2014) Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach. Nature Communications, 5, Article No. 4644.
https://doi.org/10.1038/ncomms5644

has been cited by the following article:

• TITLE: Advances in the Application of Deep Learning in Prognostic Models for Non-Small Cell Lung Cancer

  AUTHORS: Fengxia Chen

  KEYWORDS: Deep Learning, Non-Small Cell Lung Cancer, Prognostic Model

  JOURNAL NAME: Health, Vol.17 No.3, March 17, 2025

  ABSTRACT: Non-small cell lung cancer (NSCLC) is one of the cancers with the highest incidence and mortality rates worldwide. Accurate prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning, as a breakthrough technology in artificial intelligence, has shown good performance and great potential in the application of NSCLC prognostic models. Research on the application of deep learning in the prediction of NSCLC survival and recurrence, therapeutic efficacy, distant metastasis, and complications has made certain progress and is showing a trend of multi-omics and multi-modal integration. However, there are still some deficiencies. In the future, in-depth exploration should be carried out, model validation should be strengthened, and clinical practical problems should be solved.