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@@ -148,7 +148,7 @@ <h2 class="subtitle has-text-centered">
         <h2 class="title is-3">Abstract</h2>
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-            Importance: 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
@@ -165,8 +165,13 @@ <h2 class="title is-3">Abstract</h2>
 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
 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
-          </p>
+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
+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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