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.
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.
|Liu, Ying; Wang, Hua; Li, Qian et al. (2018) Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study. Radiology 286:298-306|
|Alahmari, Saeed S; Cherezov, Dmitry; Goldgof, Dmitry et al. (2018) Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE Access 6:77796-77806|
|Paul, Rahul; Hall, Lawrence; Goldgof, Dmitry et al. (2018) Predicting Nodule Malignancy using a CNN Ensemble Approach. Proc Int Jt Conf Neural Netw 2018:|
|Liu, Ying; Kim, Jongphil; Balagurunathan, Yoganand et al. (2018) Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas. Med Phys 45:2518-2526|
|Balagurunathan, Yoganand; Beers, Andrew; Kalpathy-Cramer, Jayashree et al. (2018) Semi-automated pulmonary nodule interval segmentation using the NLST data. Med Phys 45:1093-1107|
|Li, Qian; Balagurunathan, Yoganand; Liu, Ying et al. (2018) 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 19:148-156.e3|
|Paul, Rahul; Hawkins, Samuel H; Schabath, Matthew B et al. (2018) Predicting malignant nodules by fusing deep features with classical radiomics features. J Med Imaging (Bellingham) 5:011021|
|Cherezov, Dmitry; Hawkins, Samuel H; Goldgof, Dmitry B et al. (2018) Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. Cancer Med 7:6340-6356|
|Paul, Rahul; Liu, Ying; Li, Qian et al. (2018) Representation of Deep Features using Radiologist defined Semantic Features. Proc Int Jt Conf Neural Netw 2018:|
|Napel, Sandy; Mu, Wei; Jardim-Perassi, Bruna V et al. (2018) Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer 124:4633-4649|
Showing the most recent 10 out of 85 publications