<p>Supplementary Table S2. Validation set sensitivity and specificity across confounders</p>
ARTICLE ABSTRACT
Colorectal cancer is the second leading cause of cancer-related deaths worldwide, and early detection significantly improves treatment outcomes, but existing blood-based tests often have limited sensitivity in early-stage disease. We developed a blood-based test combining orphan noncoding RNAs (oncRNA), a group of small cell-free RNAs, with generative artificial intelligence to detect colorectal cancer.
We leveraged a cohort of 613 colorectal cancer cases and controls to train a model that demonstrated both high clinical performance and minimal technical variability in robustness testing. We further validated our model in an independent, single-source cohort of 192 colorectal cancer cases and controls. Model performance was assessed by sensitivity, specificity, and area under the ROC curve, with attention to early-stage detection.
In our independent validation set, we achieved an overall sensitivity of 89% at 90% specificity, with an 80% sensitivity for stage I—an important milestone, as early-stage colorectal cancer detection remains a challenge for other blood-based technologies. Performance was consistent across demographic subgroups.
Our oncRNA–based blood test, powered by artificial intelligence, offers strong performance for early colorectal cancer detection, including in stage I disease for which existing blood-based assays are limited. These findings support further development toward a minimally invasive colorectal cancer screening tool.
