• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA; Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, USA. Electronic address: fakrulislam.tushar@duke.edu.
• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA.
• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA; Medical Physics Graduate Program, Duke University, USA.
• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA; Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, USA.
• Dept. of Biostatistics and Bioinformatics, Duke University School of Medicine, USA.
• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA; Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, USA; Medical Physics Graduate Program, Duke University, USA.
• Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Dept. of Radiology, Duke University School of Medicine, USA; Dept. of Electrical & Computer Engineering, Pratt School of Engineering, Duke University, USA; Medical Physics Graduate Program, Duke University, USA; Dept. of Radiation Oncology, Duke University School of Medicine, USA.

Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an in silico study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95 % CI: 0.90-0.95) compared to the AI CXR-Reader's AUC of 0.72 (95 % CI: 0.67-0.77). Furthermore, at the same 94 % CT sensitivity reported by the NLST, the VLST specificity of 73 % was similar to the NLST specificity of 73.4 %. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics.