Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis

TL;DR

This paper reviews how machine learning techniques are transforming lung cancer diagnosis, treatment, and prognosis by analyzing complex data from imaging and sequencing technologies, highlighting recent advances, challenges, and future opportunities.

Contribution

It provides a comprehensive overview of machine learning applications in lung cancer, emphasizing new methods for early detection, diagnosis, prognosis, and immunotherapy.

Findings

Enhanced early detection methods using ML

Improved prognosis prediction accuracy

Integration of multi-omics data for therapy planning

Abstract

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.