ArticlePeerJ2026
Development and validation of machine learning-based models integrating Septin9 methylation and serum biomarkers for early detection and differentiation of colorectal cancer.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
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 ( Results: Gender, age, hemoglobin (Hb), C-reactive protein (CRP), carcinoembryonic antigen (CEA), and Septin9 methylation were independent predictors of high-risk colorectal diseases, with the latter five also specific for CRC ( Conclusions: We developed and validated two ML-based models integrating Septin9 methylation with routine serum biomarkers for early detection and differentiation of CRC. These models show potential as non-invasive clinical decision-support tools to facilitate individualized risk assessment and support clinical management in patients undergoing evaluation for colorectal neoplasia.
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