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

BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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 %). CONCLUSION: 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.