Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Yawei Li, Xin Wu, Ping Yang, Guoqian Jiang, Yuan Luo

Research output: Contribution to journalReview articlepeer-review


The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

Original languageEnglish (US)
Pages (from-to)850-866
Number of pages17
JournalGenomics, Proteomics and Bioinformatics
Issue number5
StatePublished - Oct 2022


  • Feature extraction
  • Imaging dataset
  • Immunotherapy
  • Omics dataset
  • Prediction

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computational Mathematics


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