Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

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 learningbased 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 highlightthe challenges and opportunities for future applications of machine learning in lung cancer.

Automated summary

The recent development of imaging and sequencing technologies has brought systematic advances in the clinical study of lung cancer. However, the human mind is limited in effectively handling and utilizing the enormous amounts of accumulated data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets to enhance various aspects of lung cancer diagnosis and therapy. This review provides an overview of machine learning-based approaches in early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice of lung cancer, as well as highlighting the challenges and opportunities for future applications.