Machine Learning for Lung Cancer Diagnosis,Treatment,and Prognosis
- Author:
Li YAWEI
1
;
Wu XIN
;
Yang PING
;
Jiang GUOQIAN
;
Luo YUAN
Author Information
1. Department of Preventive Medicine,Feinberg School of Medicine,Northwestern University,Chicago,IL 60611,USA
- Keywords:
Omics dataset;
Imaging dataset;
Feature extraction;
Prediction;
Immunotherapy
- From:
Genomics, Proteomics & Bioinformatics
2022;20(5):850-866
- CountryChina
- Language:Chinese
-
Abstract:
T he 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.
