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         <h2 class="title is-3">Abstract</h2>
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-            <strong>Importance:</strong>: Clinical imaging trials are crucial for evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach addresses these
+            <strong>Importance:</strong> Clinical imaging trials are crucial for evaluation of medical innovations, but the process is inefficient, expensive, and ethically-constrained. Virtual imaging trial (VIT) approach addresses 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.
-Objectives: To demonstrate that a virtual imaging trial platform can accurately emulate a major clinical
+<strong>Objectives:</strong> To demonstrate that a virtual imaging trial platform can accurately emulate a major clinical
 trial, specifically the National Lung Screening Trial (NLST) that compared computed tomography (CT)
 and chest radiography (CXR) imaging for lung cancer screening.
-Design, Setting, and Participants: A virtual patient population of 294 subjects was created from human
+<strong>Design, Setting, and Participants:</strong> A virtual patient population of 294 subjects was created from human
 models (XCAT) emulating the NLST, with two types of simulated cancerous lung nodules. Each virtual
 patient in the cohort was assessed using simulated CT and CXR systems to generate images reflecting the
 NLST imaging technologies. Deep learning models trained for lesion detection, AI CT-Reader, and AI
 CXR-Reader served as virtual readers.
-Main Outcomes and Measures: The primary outcome was the difference in the Receiver Operating
+<strong>Main Outcomes and Measures:</strong> The primary outcome was the difference in the Receiver Operating
 Characteristic Area Under the Curve (AUC) for CT and CXR modalities.
-Results: The study analyzed paired CT and CXR simulated images from 294 virtual patients. The AI CTReader outperformed the AI CXR-Reader across all levels of analysis. At the patient level, CT
+<strong>Results:</strong> The study analyzed paired CT and CXR simulated images from 294 virtual patients. The AI CTReader outperformed the AI CXR-Reader across all levels of analysis. At the patient level, CT
 demonstrated superior diagnostic performance with an AUC of 0.92 (95% CI: 0.90-0.95), compared to
 CXR’s AUC of 0.72 (0.67-0.77). Subgroup analyses of lesion types revealed CT had significantly better detection of homogeneous lesions (AUC 0.97, 95% CI: 0.95-0.98) compared to heterogeneous lesions
 (0.89; 0.86-0.93). Furthermore, when the specificity of the AI CT-Reader was adjusted to match the
 NLST sensitivity of 94% for CT, the VLST results closely mirrored the NLST findings, further
 highlighting the alignment between the two studies.
-Conclusion and Relevance: The VIT results closely replicated those of the earlier NLST, underscoring
+<strong>Conclusion and Relevance:</strong> The VIT results closely replicated those of the earlier NLST, underscoring
 its potential to replicate real clinical imaging trials. Integration of virtual trials may aid in the evaluation
 and improvement of imaging-based diagnosis.          </p>
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