Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

Geewon Lee, Ho Yun Lee, Hyunjin Park, Mark L. Schiebler, Edwin J.R. van Beek, Yoshiharu Ohno, Joon Beom Seo, Ann Leung

Research output: Contribution to journalReview articlepeer-review

212 Citations (Scopus)


With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.

Original languageEnglish
Pages (from-to)297-307
Number of pages11
JournalEuropean journal of radiology
Publication statusPublished - 01-01-2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging


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