Virtual Lung Screening Trial (VLST): An In Silico Replica of the National Lung Screening Trial for Lung Cancer Detection

Fakrul Islam Tushar, Liesbeth Vancoillie, Cindy McCabe, Amareswararao Kavuri, Lavsen Dahal, Brian Harrawood, Milo Fryling, Mojtaba Zarei, Saman Sotoudeh-Paima, Fong Chi Ho, Dhrubajyoti Ghosh, Michael R. Harowicz, Tina D. Tailor, Sheng Luo, W. Paul Segars, Ehsan Abadi, Kyle J. Lafata, Joseph Y. Lo+, Ehsan Samei+
Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology,Duke University School of Medicine, Durham, NC, 27708, USA
+Indicates Co-Senior Authors
Paper SPIE Phy. MedI(2024) VITM(2024) RSNA 2024 Resources arXiv

Virtual Lung Screening Trial (VLST) workflow, showcasing AI-driven in silico trials in lung cancer screening—part of CVIT’s Monthly Forum. Watch the full forum here.

Abstract

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 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 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 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 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 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.

Project Presentation-VLST

VLST-NotebookLM Podcast

Pre-print

SPIE Phy. MedI(2024) Poster

Virtual Imaging Trials in Medicine – International Summit (VITM24) Poster

Won the Best Poster Award

RSNA Annual Meeting (2024) Abstarct

BibTeX

@misc{tushar2024virtuallungscreeningtrial,
      title={Virtual Lung Screening Trial (VLST): An In Silico Replica of the National Lung Screening Trial for Lung Cancer Detection}, 
      author={Fakrul Islam Tushar and Liesbeth Vancoillie and Cindy McCabe and Amareswararao Kavuri and Lavsen Dahal and Brian Harrawood and Milo Fryling and Mojtaba Zarei and Saman Sotoudeh-Paima and Fong Chi Ho and Dhrubajyoti Ghosh and Michael R. Harowicz and Tina D. Tailor and Sheng Luo and W. Paul Segars and Ehsan Abadi and Kyle J. Lafata and Joseph Y. Lo and Ehsan Samei},
      year={2024},
      eprint={2404.11221},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2404.11221}, 
}