An algorithm-enhanced stool DNA system improves the differential diagnosis of colorectal cancer versus Crohn's disease in high-risk symptomatic patients

Crohn's disease (CD) and colorectal cancer (CRC) share many clinical symptoms, making non-invasive differential diagnosis difficult. FIT-sDNA is sensitive for CRC screening in average-risk populations but often gives false positives in CD patients due to inflammation-induced mucosal turnover. This study aimed to develop and validate an algorithm-enhanced system (FIT-sDNA-CA) to improve the specificity of CRC triage using current DNA tests.

The study enrolled 312 subjects, comprising a training cohort of 234 confirmed patients and a prospective validation cohort of 78 potential patients initially diagnosed by clinicians with either CD or CRC. Machine learning algorithms integrated gender, age, fecal KRAS mutation, BMP3/NDRG4/SDC2 methylation, fecal calprotectin (FC), and fecal immunochemical test (FIT) results. After comparing eight algorithms, polynomial regression (PR) was determined to be the optimal model.

The PR model demonstrated superior clinical applicability compared to long short-term memory (LSTM) networks (validation set AUC 0.906 vs 0.794). In CRC differential diagnosis, the FIT-sDNA-CA system achieved a positive predictive value of 69.65 % (95 % CI, 66.73-71.58), significantly higher than FIT (45.93 %) and FC (22.92 %).

By integrating genetic, epigenetic, and inflammatory biomarkers, the FIT-sDNA-CA system effectively filters out confounding signals from intestinal inflammation, overcoming the low specificity limitation of traditional fecal DNA testing. As a highly accurate non-invasive triage tool, this system facilitates early risk stratification for patients with high-risk colorectal cancer symptoms and significantly reduces unnecessary endoscopic referrals.