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Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol.

Jonathan Benzaquen1, Paul Hofman2, Stephanie Lopez3

  • 1Department of Pulmonary Medicine and Thoracic Oncology, FHU OncoAge, IHU RespirERA, Centre Hospitalier Universitaire de Nice, Nice, France.

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Summary

This study investigates if artificial intelligence can speed up lung cancer diagnosis. It aims to reduce the time from nodule detection to classification, improving patient outcomes in lung cancer screening (LCS).

Keywords:
Computed tomographyONCOLOGYRespiratory tract tumours

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Thoracic Radiology

Background:

  • Lung cancer (LC) is a leading cause of cancer mortality worldwide.
  • Low-dose CT (LDCT) screening reduces LC mortality by 20-25% in high-risk individuals.
  • Current lung cancer screening (LCS) faces challenges including radiologist shortages, high false-positive rates, and prolonged nodule evaluation periods.

Purpose of the Study:

  • To evaluate the effectiveness of a 3D convolutional neural network (AI) in assisting multidisciplinary teams (MDTs) for lung nodule classification.
  • To determine if AI assistance can accelerate the definitive classification of lung nodules detected during screening.
  • To reduce the anxiety and costs associated with prolonged nodule indeterminacy in lung cancer screening.

Main Methods:

  • An open-label, randomized controlled trial involving 2722 patients aged 50-80 years with a history of significant smoking.
  • Participants are randomized to either standard LCS workflow or LCS workflow augmented with AI-assisted nodule analysis.
  • The primary endpoint is the reduction in time from lung nodule detection to its definitive classification (benign or malignant).

Main Results:

  • The study aims to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification.
  • Artificial intelligence has shown promise in retrospective studies for detecting and characterizing lung nodules.
  • This prospective study will provide real-world evidence on the utility of AI in LCS.

Conclusions:

  • Implementing AI in LCS could potentially streamline the diagnostic process.
  • Accelerated nodule classification may alleviate patient anxiety and reduce healthcare costs.
  • This research could pave the way for wider adoption of AI in lung cancer screening programs.