It is well known that clinical parameters such as clinical stage are correlated with survival outcomes among cancer patients. However, there is much variability among patients, and we are unable to accurately or reliably predict survival for individual patients - a necessary step for personalized cancer medicine. There is a strong need for accurate, reliable outcome predictions for patients, caregivers, and clinical staff. Even the addition of genetic data has yet to make a clinically significant increasein the reliability of our outcome prediction for individual patients. Recent research, including our own, has shown that image features extracted from pre-treatment CT images can be used to predict treatment outcomes for non-small cell lung cancer patients, esophageal cancer patients, and others. A limitation to current studies is the lack of a common platform that would enable research to share results and quickly and easily apply techniques to their own patient datasets. Our proposed project will create open-source software tools that will integrate with current open-source tools that are available for radiation therapy research. We will also carry out an in-depth investigation into the various sources of uncertainty involved in calculating image features, allowing researchers to avoid using features that have high dependence on imaging parameters (such as pixel size). Nearly 100% of NCI- funded clinical trials include pre-treatment CT imaging. Our preliminary work will provide the tools to allow researchers involved in these studies to investigate the use of quantitative image features for predicting treatment outcome.

Public Health Relevance

The proposed project will develop tools to extract quantitative image features from CT images. We will also develop an in-depth understanding of the robustness of these features when images are taken using different CT scanners and different imaging protocols. These tools will facilitate research into the use of image features to predict treatment outcome, supporting decision making by the patient, caregiver and clinical staff.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Small Research Grants (R03)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Redmond, George O
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Texas MD Anderson Cancer Center
United States
Zip Code
Mackin, Dennis; Ger, Rachel; Dodge, Cristina et al. (2018) Effect of tube current on computed tomography radiomic features. Sci Rep 8:2354
Ger, Rachel B; Cardenas, Carlos E; Anderson, Brian M et al. (2018) Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. J Vis Exp :
Yang, Jinzhong; Zhang, Lifei; Fave, Xenia J et al. (2016) Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 48:1-8
Zhang, Lifei; Fried, David V; Fave, Xenia J et al. (2015) IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341-53
Fave, Xenia; Mackin, Dennis; Yang, Jinzhong et al. (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 42:6784-97
Mackin, Dennis; Fave, Xenia; Zhang, Lifei et al. (2015) Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol 50:757-65
Mohamed, Abdallah S R; Ruangskul, Manee-Naad; Awan, Musaddiq J et al. (2015) Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms. Radiology 274:752-63
Fried, David V; Tucker, Susan L; Zhou, Shouhao et al. (2014) Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 90:834-42