Artificial Intelligence

CADx’s Lung Nodule Impact

A new JACR study highlighted Computer-Aided Diagnosis (CADx) AI’s ability to improve lung nodule malignancy risk classifications, while stating a solid case for the technology’s potential clinical role.

The researchers applied RevealDx’s RevealAI-Lung CADx solution to chest CTs from 963 patients with 1,331 nodules (from 2 LC screening datasets, and one incidental nodule dataset), finding that RevealAI-Lung’s malignancy risk scores (mSI) combined with Lung-RADS would significantly improve…

  • Sensitivity versus Lung-RADS-only (3 cohorts: +25%, +68%, +117%)
  • Specificity versus Lung-RADS-only (3 cohorts: +17%, +18%, +33%)

Looking specifically at the study’s NLST cohort (704 nodules), mSI+Lung-RADS would have…

  • Reclassified 94 nodules to “high risk” (formerly false-negatives)
  • Potentially diagnosed 53 patients with malignant nodules at least one year earlier
  • Reclassified 36 benign nodules to “low-risk” (formerly false-positives)

The RevealDx-based malignancy scores also achieved comparable accuracy to existing clinical risk models when used independently (AUCs: 0.89 vs. 0.86 – 0.88).

The Takeaway

These results suggest that a CADx lung nodule solution like RevealAI-Lung could significantly improve lung nodule severity assessments. Considering the clinical importance of early and accurate diagnosis of high-risk nodules and the many negatives associated with improper diagnosis of low-risk nodules (costs, efficiency, procedures, patient burden), that could be a big deal.

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