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
Obuchowski, Nancy A; Reeves, Anthony P; Huang, Erich P et al. (2015) Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons. Stat Methods Med Res 24:68-106
Aerts, Hugo J W L; Velazquez, Emmanuel Rios; Leijenaar, Ralph T H et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006
Balagurunathan, Yoganand; Kumar, Virendra; Gu, Yuhua et al. (2014) Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27:805-23
Oberije, Cary; Nalbantov, Georgi; Dekker, Andre et al. (2014) A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol 112:37-43
Parmar, Chintan; Rios Velazquez, Emmanuel; Leijenaar, Ralph et al. (2014) Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107
Roelofs, Erik; Dekker, Andre; Meldolesi, Elisa et al. (2014) International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. Radiother Oncol 110:370-4
Buijsen, Jeroen; van Stiphout, Ruud G; Menheere, Paul P C A et al. (2014) Blood biomarkers are helpful in the prediction of response to chemoradiation in rectal cancer: a prospective, hypothesis driven study on patients with locally advanced rectal cancer. Radiother Oncol 111:237-42
Balagurunathan, Yoganand; Gu, Yuhua; Wang, Hua et al. (2014) Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. Transl Oncol 7:72-87
Zhou, Mu; Hall, Lawrence; Goldgof, Dmitry et al. (2014) Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 7:5-13
Roelofs, Erik; Persoon, Lucas; Nijsten, Sebastiaan et al. (2013) Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial. Radiother Oncol 108:174-9

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