Robert Gillies

PHD

Moffitt Cancer Center

Chair, Cancer Imaging and Metabolism

NLST (Learn more about this study)

NLST-7

Dec 17, 2012

Radiomics of Lung Screening

Can Radiomics accurately identify normal (true negative) subjects who will not develop
cancer within the NLST screening and follow-up periods?

This project will analyze the Moffitt dataset with other screening datasets who underwent initial and two annual NLSTscreenings and who did not develop lung cancer within the screening or follow-up periods. Features will be developed and extracted from left and right lung fields of all patients and algorithms will be developed to analyze the longitudinal progression of individual features. These analyses will be able to quantitatively describe the natural range of all features as well as their reproducibility. The impact of these studies will be identification of a panel of features that characterize patients who have “lower risk” wherein the screening interval could be lengthened to, e.g. 5-10 years.

Aim 1. Develop robust methods for segmenting lung fields of normal patients. Although we have developed methods for segmenting lung cancers, we have not yet optimized segmentation of the entire lungs. Our newly-developed multi-seed point approach will be used.

Aim 2. Collate and validate Moffitt dataset. The data from the NLST trial was prospectively structured to produce high levels of compliance and follow up. However, to avoid mining of unreliable data, considerable effort will be expended to collate and verify the CT and clinical data available in the Moffitt dataset. Inclusion criteria include (a) the presence of at least three longitudinal scans; (b) extractable radiology reports; and (c) sufficient clinical data to classify participants as true negative, equivocal negative or true positive. These data will be tabulated and entered into the database developed by the core.

Aim 3. Expand Feature set. To date, we have expanded the feature set for lung cancers to over 320 2- and 3-Dimensional elements. These are specific for lung lesions and may not capture the additional features that are relevant to normal lung fields. The major feature to be developed in this aim will be quantitative descriptors of the normal bronchiolar tree and terminal bronchiolar architecture.

Aim 4. Extract and analyze data from the Moffitt data set. Features will be extracted from all validated patients in the Moffitt dataset and deposited into the database developed by the core. Both L and R lung fields will be extracted from at least three longitudinal scans from each participant. Data will be parsed for unequivocally negative normal participants and these will be used to develop a baseline normal set of features. These will be used to determine which features do not exhibit progression throughout the screening and follow-up periods. In addition to Radiomic variables, feature selection will consider other potentially confounding variables archived by the Core, including demographic variables (age, race, sex), physiologic variables (BMI, BP, airway obstruction), and respiratory exposure variables (smoking, asbestos exposures). Patterns of selected features will be compared to participants who were marginally normal, i.e. those with moderate to severe airway obstruction (COPD) and severe inflammatory residua.

Aim 5. Extract Quantitative descriptors in Pathology Images:
We would like use the digital pathology images (H&E or others) to extract quantitative descriptors at the individual cell level and quantitatively describe the surrounding characteristics. These metrics across the population would be additional information to aid the CT extracted features in the model to predict and or prognosticate patient characteristics to become malignant over time. Pathology features will also be related to CT extracted features both to understand the relationship at two different scales.

Moffitt Cancer Center:
Robert Gillies, Ph. D (PI)
Robert Gatenby, M.D, Ph.D

Steven Estrich, Ph.D
Greg Bloom, Ph.D
Yoga Balagurunathan, Ph.D
Yuhua Gu, Ph.D

Moffitt NLST Collaborators:
Melvyn Tockman, M.D, Ph.D
Lynn Coppage, M.D, Ph.D

University of South Florida:
Lawrence Hall, Ph.D
Dmitry Goldgoff, Ph.D

Mastro Cancer Center, Netherlands,

Definiens, Inc
Rene Korn