ArticleScientific reports2025
Development and validation of a logistic regression model for the diagnosis of colorectal cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- Comparative Diagnostic Performance of Conventional and Novel Fatty Acid Indices in Blood Plasma as Biomarkers of Atherosclerosis Under Statin Therapy.Biomedicines · 2026Article
- Investigating radiotherapy's impact on intestinal perforation risk in gastrointestinal tumor patients treated with bevacizumab.World journal of gastrointestinal oncology · 2025Article
- A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
13 authors.
Funding
Abstract
Colorectal cancer (CRC) diagnosis is challenging due to generalized symptoms. Various biomarker models exist, but their clinical application is limited by low sensitivity and heterogeneous cutoff values. This study aimed to develop and validate a diagnostic model for CRC. Data from 489 patients-337 with CRC and 152 with benign disease-were included. Patients were randomly assigned to training (n = 342) and validation (n = 147) cohorts. Logistic regression identified age (OR 1.06), CA153 (OR 0.26), CEA (OR 4.49), CYFRA 21-1 (OR 5.88), ferritin (OR 0.15), and hs-CRP (OR 0.05) as independent risk factors. Sensitivity and specificity were 88.61% and 82.86% in the training cohort and 90.00% and 76.60% in the validation cohort. Cutoff values for the biomarkers were: CA199, 9.809 U/mL; CA125, 7.743 U/mL; CA153, 6.295 U/mL; CEA, 3.982 ng/mL; CYFRA 21-1, 1.769 ng/mL; ferritin, 163.361 mg/L; hs-CRP, 0.196 mg/L; and serum albumin, 55.966 g/L. The model showed higher sensitivity for early-stage CRC (95.45%, 95% CI 87.2-98.6%) than late-stage CRC (87.27%, 95% CI 76.4-93.5%; P = 0.08). AUCs were 0.907 (training) and 0.872 (validation). The model demonstrated higher sensitivity for early-stage CRC (95.45%) than late-stage CRC (87.27%), underscoring its utility in early detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.