Development and validation of machine learning-based models integrating Septin9 methylation and serum biomarkers for early detection and differentiation of colorectal cancer

作者：C H Jiang, Yiyi Lu, Beiying Wu, Yunzhe Wu, Lilan Jin, Gang Cai, Zirui He, Lu Lin · 发表于：PeerJ · 年份：2026 · DOI：10.7717/peerj.21053 · 研究领域：Colorectal Cancer Screening and Detection、AI in cancer detection、Colorectal Cancer Surgical Treatments

Background Accurate risk stratification and early detection of colorectal cancer (CRC) are critical for improving patient outcomes and optimizing the use of colonoscopy; however, the diagnostic performance of existing biomarkers remains suboptimal. This study aimed to develop and evaluate machine learning (ML)-based models to facilitate individualized risk assessment and clinical decision-making for colorectal lesions. Methods A total of 1,714 participants who underwent colonoscopy at Department of Gastrointestinal Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine were included. Participants were categorized into normal colonoscopy controls ( n = 677) and high-risk colorectal diseases group ( n = 1,037), with the latter further subdivided into adenomas ( n = 376) and CRC ( n = 661) subsets. Demographic characteristics and relevant laboratory data were collected. Variables significantly associated with high-risk colorectal conditions or CRC were identified using univariable and multivariable logistic regression analyses and incorporated into two independent nomogram-based ML models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) analysis was performed to determine each feature’s contribution. Results Gender, age, hemoglobin (Hb), C-reactive protein (CRP), carcinoembryonic antigen (CEA), and Septin9 methylati...