Non small cell lung cancer, NSCLC, is the most prevalent of cancers and has one of the highest mortality rates. Thus, any advance in the ability to predict response and individualize treatment will have great impact. NSCLC patients are routinely imaged with PET and CT for staging and monitoring, respectively. The major hypothesis of the current work is that quantitative analysis of these clinical images can be prognostic and predictive of response to specific therapies. If true, these results would have medical significance through improved care and outcomes. This would also have socioeconomic significance as it would allow advanced, evidence based medicine to be practiced using standard-of-care images. To test this hypothesis, this project will extract mineable imaging data from two powerful patient databases at the Moffitt Cancer Center in Tampa, FL and the MAASTRO clinic in Maastricht, the Netherlands. These databases contain images, gene expression profiling and outcomes data from hundreds of stage III and IV NSCLC patients. Over 100 features will be extracted from each image using developmental commercial software. Features extracted retrospectively from the Moffitt dataset will be quantitatively analyzed to generate predictive models for gene expression patterns and progression-free survival. These models will be tested in the MAASTRO data set and re-tested using prospective data from Moffitt acquired under rigorous conditions. An important outcome of this work will define the rigor and resolution needed for images to be useful in predictive models. With the right combination of features, the needed rigor and resolution may be readily achievable in a clinical setting. A capstone experiment will add image feature extraction to a theragnostic trial that matches therapy to individual patient expression patterns for two proteins that predict response to specific therapies. The hypothesis to be tested is that image features can segment patients to specific therapy regimens without the molecular biopsy data.

Public Health Relevance

This work will determine if quantitative analysis of images obtained during clinical standards of care can be used to prognose outcome or predict response to specific therapies in lung cancer. If true, this would increase the utility of clinical imaging in this disease and potentially improve the care for up to 215,000 patients annually without necessarily increasing in the cost.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA143062-05
Application #
8617727
Study Section
Special Emphasis Panel (ZCA1-SRLB-C (O1))
Program Officer
Nordstrom, Robert J
Project Start
2010-03-09
Project End
2015-02-28
Budget Start
2014-03-01
Budget End
2015-02-28
Support Year
5
Fiscal Year
2014
Total Cost
$455,793
Indirect Cost
$107,283
Name
H. Lee Moffitt Cancer Center & Research Institute
Department
Type
DUNS #
139301956
City
Tampa
State
FL
Country
United States
Zip Code
33612
Liu, Shuang; Xie, Yiting; Jirapatnakul, Artit et al. (2017) Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks. J Med Imaging (Bellingham) 4:041308
Li, Qian; Balagurunathan, Yoganand; Liu, Ying et al. (2017) Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial. Clin Lung Cancer :
Li, Qian; Kim, Jongphil; Balagurunathan, Yoganand et al. (2017) Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy. Med Phys 44:4341-4349
Shafiq-Ul-Hassan, Muhammad; Zhang, Geoffrey G; Latifi, Kujtim et al. (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44:1050-1062
Tunali, Ilke; Stringfield, Olya; Guvenis, Albert et al. (2017) Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients. Oncotarget 8:96013-96026
Yip, Stephen S F; Liu, Ying; Parmar, Chintan et al. (2017) Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 7:3519
O'Connor, James P B; Aboagye, Eric O; Adams, Judith E et al. (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169-186
Li, Qian; Kim, Jongphil; Balagurunathan, Yoganand et al. (2017) CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy. Radiat Oncol 12:158
Liu, Ying; Balagurunathan, Yoganand; Atwater, Thomas et al. (2017) Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules. Clin Cancer Res 23:1442-1449
Beichel, Reinhard R; Smith, Brian J; Bauer, Christian et al. (2017) Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Med Phys 44:479-496

Showing the most recent 10 out of 74 publications