Radiomics of Lung Cancer


Project Summary:

“Radiomics” is the process of extracting structured and mineable data from biomedical images, and then using these data to provide more power for cancer diagnosis and prognosis, as well as prediction and monitoring response to anti-cancer therapies (1). The most well developed area in radiomics is analysis of CT scans from lung cancer patients, which has been the subject of dozens of high impact publications by our group and others (2,3). Through quantitative analyses of lung cancers, predominately non-small cell (NSCLC) adenocarcinoma, radiomics can predict survival, progression, and recurrence with accuracies approaching 90%, and can also define expression of common mutations with accuracies over 80% (4-7). 


  1. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278(2):563-77.
  2. Grossmann P, Grove O, El-Hachem N, Rios-Velazquez E, Parmar C, Leijenaar RTH, et al. Identification of Molecular Phenotypes in Lung Cancer by Integrating Radiomics and Genomics. eLIFE 2017;ePub 07-12-2017.
  3. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.
  4. Li Q, Kim J, Balagurunathan Y, Liu Y, Latifi K, Stringfield O, et al. Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys 2017.
  5. Wang H, Schabath MB, Liu Y, Han Y, Li Q, Gillies RJ, et al. Clinical and CT characteristics of surgically resected lung adenocarcinomas harboring ALK rearrangements or EGFR mutations. Eur J Radiol 2016;85(11):1934-40.
  6. Liu Y, Kim J, Qu F, Liu S, Wang H, Balagurunathan Y, et al. CT Features Associated with Epidermal Growth Factor Receptor Mutation Status in Patients with Lung Adenocarcinoma. Radiology 2016;280(1):271-80.
  7. Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS one 2015;10(3):e0118261.


Project Members


Robert J Gillies, PhD

PI, Chair of Department