Table 1 Consensus aspirations for advancing quantitative imaging to improve lung cancer screening and quantitative tool development and validation

1) Create a public database of 50,000 lung cancer screening imaging datasets

 • Leverage available data and phantom validation tools to ensure this is a high-quality image collection.

 •Ensure that there is sufficient diversity (e.g., demographic, geographic, disease states, scanner models).

 •Ensure that major lung cancer sub-populations (e.g., small cell) are represented in such a collection.

 •Provide reliable sources of “Ground truth” information including lung nodule biopsy results.

2) Increase resource to support and curate new and existing databases and related technology infrastructure

 •Centralized Database Example: The Cancer Imaging Archive (TCIA).

 •Distributed Database Example: Early Lung Imaging Confederation (ELIC).

 •Technology Example: Automated insertion of lung nodules in existing CT scans.

3) Investigate a New Global Patient Data Electronic Submission Infrastructure

 •Allow global patients to opt-in to having their data used by researchers with appropriate de-identification.

 •Hospitals and healthcare facilities will inform patients of this opportunity to advance research.

 •A global opt-in strategy will need informed consent templates for different regions of the world.

4) Improve the QIBA Small Lung Nodule Profile

 •Add support for volume measurement of part-solid lung nodules.

 •Add support for the ability to use multiple CT scanners over time to measure lung nodules.