  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 article, 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.
