The Use of Molecular and Imaging Biomarkers in Lung Cancer RiskPrediction

[摘要] High-dimensional genomics, genetics and proteomics techniqueshave been widely used in cancer research for over two decades.Correspondingly, various genomic, genetic and proteomic signatureshave been discovered in the cancer’s diagnosis, prognosis andprediction. For instance, over the last decade, considerable eوٴort andresources have been devoted to characterize the genomic, genetic, andproteomic proٽOes of lung cancers [1-3]. Нese studies can enable us tohave a deep understanding of the molecular heterogeneity of thisdisease and help create new therapeutic targets that will facilitatepersonalize targeted therapy. Now, as non-invasive medical imagetechnologies are likely to become routine in screening high-riskpopulations, the use of imaging features may greatly assist the therapyguidance and the monitoring of development and progression of lungcancer and its response to treatment. Similar to other – omicstechnologies, radiomics refers to the high-throughput extraction andanalysis of a large amount of quantitative features from advancedmedical images with the assistance from compute science, and canprovide a comprehensive quDntiٽcDtion of the tumor phenotype [4-6].