Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection.
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2025-07
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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.
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Tushar, Fakrul Islam, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, et al. (2025). Virtual lung screening trial (VLST): An in silico study inspired by the national lung screening trial for lung cancer detection. Medical image analysis, 103. p. 103576. 10.1016/j.media.2025.103576 Retrieved from https://hdl.handle.net/10161/33677.
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Scholars@Duke
Sheng Luo
William Paul Segars
Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.
These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.
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