Virtual Lung Screening Trial (VLST): An In Silico Replica of the National Lung Screening Trial for Lung Cancer Detection.

Importance: Clinical imaging trials are crucial for definitive evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach address these limitations by emulating the components of a clinical trial. An in silico rendition of the National Lung Screening Trial (NCLS) via Virtual Lung Screening Trial (VLST) demonstrates the promise of VITs to expedite clinical trials, reduce risks to subjects, and facilitate the optimal use of imaging technologies in clinical settings.

Design, setting, and participants: A diverse virtual patient population of 294 subjects was created from human models (XCAT) emulating the characteristics of cases on NLST, with two types of simulated lung nodules. The cohort was assessed using simulated CT and CXR systems to generate images that reflect the NLST imaging technologies. Deep learning models trained for lesion detection in CXR and CT served as virtual readers.

Results: The study analyzed 294 CT and CXR simulated images from 294 virtual patients, with a lesion-level AUC of 0.81 (95% CI: 0.79-0.84) for CT and 0.56 (95% CI: 0.54-0.58) for CXR. At the patient level, CT demonstrated an AUC of 0.84 (95% CI: 0.80-0.89), compared to 0.52 (95% CI: 0.45-0.58) for CXR. Subgroup analyses on CT results indicated superior detection of homogeneous lesions (lesion-level AUC 0.97) than heterogeneous lesions (lesion-level AUC 0.72). Performance was particularly high for identifying larger nodules (AUC of 0.98 for nodules > 8 mm). The VLST results closely mirrored the NLST, particularly in size-based detection trends, with CT achieving high AUCs for nodules > 8 mm and similar challenges in detecting smaller nodules.

Conclusion and relevance: The VIT results closely replicated those of the earlier NLST, underscoring its potential to replicate real clinical imaging trials.