Dennis Robert

M.B.B.S, M.M.S.T.

Qure.ai Technologies Private Limited

Director of Clinical Research

NLST (Learn more about this study)

NLST-1400

Mar 5, 2025

Assessing the Usefulness of AI in Lung Cancer Detection on Chest Radiographs: A Retrospective Study Using NLST Data

Lung cancer remains the leading cause of cancer-related mortality worldwide, and early detection is crucial for improving survival. While the National Lung Screening Trial (NLST) established the superiority of low-dose CT (LDCT) over chest X-ray (CXR) for lung cancer screening, CXRs continue to be widely used in resource-limited settings.

Artificial intelligence (AI)-powered radiology tools, such as Qure.ai’s qXR, have demonstrated potential in detecting lung abnormalities on CXRs. However, their effectiveness in high-risk screening populations has not been rigorously validated. This study will evaluate qXR’s performance in lung cancer detection by comparing AI-generated findings from NLST CXRs with radiologist interpretations and confirmed lung cancer diagnoses. The study will utilize NLST ACRIN chest X-rays data to assess qXR's performance in three key tasks:

- Identifying lung cancer cases accurately to improve early detection.

- Differentiating benign abnormalities from malignant findings to reduce unnecessary follow-ups.

- Comparing qXR’s diagnostic accuracy with radiologists to assess its potential clinical impact.

A retrospective case-control design will be used, with participants classified into three groups:

- Cancer-Positive: Participants with a valid screening result (screen positive or screen negative) who were diagnosed with lung cancer during screening phase.

- Cancer-Negative: Participants not diagnosed with lung cancer throughout the study

The study will apply qXR to NLST CXRs, extract AI-predicted abnormalities, and compare them with radiologist interpretations and lung cancer confirmation data. Performance evaluation will include sensitivity, specificity, false positive/negative rates, ROC/AUC analysis, and agreement metrics.

- Evaluate the sensitivity and specificity of qXR for lung cancer detection, using confirmed cancer diagnoses as the ground truth.

- Compare the agreement between qXR and NLST radiologists in detecting lung nodules and other abnormalities.

- Estimate the proportion of missed cancers detected by qXR, focusing on cases where radiologists reported a negative screen result, but cancer was later diagnosed.

- Assess the clinical impact of AI-based screening by investigating if there are any reductions in false positives and false negatives screening results when using AI.