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).
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.