The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program

Abstract

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

FIGURE 1

The ELCAP design allows for direct comparison of different screening tests (eg, imaging tests or biomarkers) by providing the baseline round followed by as many repeat rounds of screening as needed to assess the particular comparison.

FIGURE 2

Kaplan-Meier survival rates for patients with adenocarcinoma (n=279) by the percentage of the lepidic (formerly BAC component) component.

FIGURE 3

A and B, The increasing frequency of lung cancers diagnosed in solid NCNs in (B) annual rounds (85%) compared with those in the (A) baseline round (66%) demonstrates that cancers in solid nodules are on average more aggressive. Cancers in nonsolid and part-solid NCNs decreased in annual rounds (B) comparing with the baseline round (A), indicating that these were less aggressive cancers.

FIGURE 4

Provides the probability of diagnosing lung cancer by the largest NCN average diameter (mm) and consistency in the I-ELCAP database, separately on the (A) baseline round and (B) annual repeat rounds.

FIGURE 5

Multiple “pocket phantoms” (left upper image). One pocket phantom was placed on the chest of a lung cancer patient (left lower image). The images on the right show the considerable increases in the volume of precision manufactured spheres embedded in the pocket phantom.

FIGURE 6

A scan of 3 identical precision manufactured phantoms (CTLX1) shows the 3D spatial warping in helical acquisition. Spatial warping was observed to be dependent on the distance of the object from the iso-center of the scanner. It is not as marked when the object is close to the iso-center (image on the right) but increases with increasing distance (100 mm, 200 mm) from the iso-center (images on the left). Spatial warping also regularly alternates between compression (red arrow) and expansion (blue arrow) in the Z dimension depending on distance along this dimension.

FIGURE 7

Precision follow-up time. The degree of overlap between the green, orange, and red bands shows the overlap of the different VDTs of nodule growth for given follow-up times. The width of the bands was set to illustrate a high level of image quality in terms of CT image resolution and distance between voxels. The solid bands indicate the 1 standard deviation of confidence regions that future nodule measurements will fall into for stable (green), 400-day (orange), and 180-day (red) VDTs. The outer dashed lines for each band indicate 2 standard deviation confidence intervals.