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Virtual Imaging Trials, known as VITs, provide a computational substitute for clinical trials. These traditional trials tend to be sluggish, costly, and frequently deficient in definitive evidence, all the while subjecting participants to ionizing radiation. Our VIT platform meticulously mimics essential components of the imaging process, encompassing everything from virtual patients and scanners to simulated readers. Within the scope of this intended research, we aim to authenticate our virtual imaging trial platform by duplicating the results of the National Lung Screening Trial (NLST) for lung cancer screening through the emulation of low-dose computed tomography (CT) and chest radiography (CXR) procedures. The methodology involves creating 66 unique computational phantoms, each with inserted simulated lung nodules. Replicating NLST CT imaging via Duke Legacy W20 scanner matched essential properties. Virtual imaging was done through DukeSim. A LUNA16-trained virtual reader, combining a 3D RetinaNet model (front-end) with a ResNet-10 false positive reduction model (back-end), evaluated the virtually imaged data, ensuring rigorous assessment. The back-end model achieved a sensitivity of over 95% at fewer than 3 false positives per scan for both the clinical and virtual imaged CTs. Notably, nodule diameter-based analysis showcases even higher sensitivity for nodules measuring 10 mm or more. In conclusion, the integration of diverse computational and imaging techniques, culminating in a virtual reader, demonstrates promising sensitivity. To capture both arms of the trial, future research will compare virtual reader performance on CT with CXR. This affirms the transformative potential of virtual imaging trials in advancing evidence-based medicine, offering an efficient and ethically conscious approach to medical research and development.
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