Optimized workflow for low dose thoracic CT lung cancer screening: automated detection, measurement, temporal matching and volumetry and mass analysis, individualized prediction of cancer risk, structured reporting with follow-up recommendation

C. Jacobs, J. Kuhnigk, E.M. van Rikxoort, T. Twellmann, P. de Jong, H. Gietema, B. Lassen, C. Schaefer-Prokop, M. Prokop and B. van Ginneken

in: Annual Meeting of the Radiological Society of North America, 2012

Abstract

BACKGROUND Today, lung cancer is the most common and most deadly cancer in men and women worldwide. The recent positive results of the National Lung Screening Trial (NLST) [1] have provided clear scientific evidence that screening with low dose chest CT reduces lung cancer mortality. The National Comprehensive Cancer Network has updated their recommendations for screening and now strongly recommend the use of low-dose CT screening for individuals at high risk for lung cancer [2]. At least one health insurance company has started to cover the cost of lung screening. In its current form, however, large scale introduction of CT lung screening would put an enormous burden on radiologists. Building upon our clinical and technical experience in reading, image analysis and data processing for large screening trials in Europe (over 30,000 CT scans from 10,000 participants) and a careful review of the existing commercially available lung workstations, we have developed a new dedicated chest reading workstation with a number of innovations that allows for an optimized high throughput workflow to report on low dose chest CT scans. The application is currently available as a research prototype and is in use at five sites. It is used in clinical research and includes automated detection, linking, volumetry, interval change analysis, and estimation of malignancy for each nodule finding. A structured report for each patient is produced with follow-up recommendations according to several guidelines, including the upcoming revised Fleischner Society guidelines for the management of pulmonary nodules. METHODOLOGY/APPLICATION The workstation that will be demonstrated includes a number of innovations and enhancements compared to currently commercially available software. - Each scan is preprocessed and scan quality is automatically assessed. Scans with low quality, artifacts or underlying interstitial lung disease are automatically flagged. - Each scan is elastically registered to all prior scans of the same patient. Findings in prior scans are propagated and linked to findings in the current scan. All scans and processing results are preloaded in the background to ensure rapid reading. - Highly effective computerized detection (CAD) of solid nodules [3] and sub-solid nodules [4] is integrated. - High throughput reading with CAD as a first reader is supported. Users can accept/reject at a setting of on average 10 to 15 candidate lesions per scan and thus report much quicker than traditional thin sliding MIP viewing of the entire volume (also supported). - Each nodule is automatically characterized as solid, part-solid, or non-solid and nodules with benign characteristics are automatically flagged. Detected benign characteristics include calcifications and peri-fissural opacities (lymph nodes). - Volumetry, volume growth rate, mass and mass growth rate are automatically computed with advanced segmentation algorithms [5] that have been extended to handle sub-solid lesions and segment the solid core of part-solid nodules. If necessary, the user can interactively adjust segmentations and compare side by side with the corresponding finding in all available prior scans to detect and correct segmentation inconsistencies. - Findings are summarized in a structured report in HTML and PDF format that is stored in the database and can be sent to requesting physicians. Follow-up recommendation according to various screening algorithms and guidelines of leading Societies are included. DEMONSTRATION STRATEGY The exhibit will be accompanied by an informational poster that will highlight the key differences between the proposed workflow and current clinical practice. The poster will also explain algorithmic concepts that underlie the automated analysis. Attendees will be able to gain hands-on experience with the workstation and read cases and use nodule detection, automated and interactive volumetry, see the results of classification of different nodule types and produce structured reports with follow-up recommendations, all within minutes.