ArticleThe American journal of gastroenterology2019
Head-to-Head Comparison of the Performance of 17 Risk Models for Predicting Presence of Advanced Neoplasms in Colorectal Cancer Screening.
Article in The American journal of gastroenterology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cross-cohort analysis identifies shared gut microbial signatures and validates microbial risk scores for colorectal cancer.Journal of translational medicine · 2025Pooled it
- Can the Parallel or Tandem Combination of Questionnaire-Based Risk Assessment and Fecal Immunochemical Test Improve the Efficiency of Colorectal Cancer Screening?International journal of cancer · 2026Article
- Multiple polygenic score approach in colorectal cancer risk prediction.Scientific reports · 2025Article
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Application of machine-learning model to optimize colonic adenoma detection in India.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Observational
- Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer.Combinatorial chemistry & high throughput screening · 2023Article
- Shape-specific characterization of colorectal adenoma growth and transition to cancer with stochastic cell-based models.PLoS computational biology · 2023Article
- Improved risk scoring systems for colorectal cancer screening in Shanghai, China.Cancer medicine · 2022Article
- Colorectal Cancer Screening in China: Status, Challenges, and Prospects - China, 2022.China CDC weekly · 2022Article
- Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records.PloS one · 2022Article
- Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma.Journal of personalized medicine · 2021Article
- Comparative yield and efficiency of strategies based on risk assessment and fecal immunochemical test in colorectal cancer screening: A cross-sectional population-based analysis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2021Article
- A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction.Nature communications · 2021Article
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- A New Comprehensive Colorectal Cancer Risk Prediction Model Incorporating Family History, Personal Characteristics, and Environmental Factors.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesMany risk scores have been proposed to predict presence of advanced colorectal neoplasms, but a comprehensive comparison conducted in the same population is sparse. The aim of this study was to evaluate and directly compare the diagnostic performance of published risk prediction models for advanced colorectal neoplasms.
methodsData were drawn from 2 cohorts of subjects undergoing screening colonoscopy in Germany, i.e., KolosSal (n = 16,195) and BliTz (n = 7,444). Absolute risks and relative risks were generated for the presence of at least 1 advanced neoplasm, taking the lowest risk group as the reference group. Performance of risk models was assessed by the area under the receiver operating characteristic curve (AUC) and compared by the net reclassification improvement.
resultsThe 2 cohorts included 1,917 (11.8%) and 848 (11.4%) participants with advanced neoplasm, respectively. Absolute risks were mostly between 5% and 10% among participants in the lowest risk group and between 15% and 20% among participants in the highest risk group, and relative risks mostly ranged from 2.0 to 4.0 across the risk models in both cohorts. The AUCs ranged from 0.58 to 0.65 in KolosSal and from 0.57 to 0.61 in BliTz for all risk scores. Compared to models with lower AUC, classification was significantly improved in most models with higher AUC. DISCUSSION: Risk models for advanced colorectal neoplasms generally yielded modest discriminatory power, despite some variation in performance between models. Future studies should evaluate the performance of these risk models in racially diverse populations and investigate possible extensions, such as combination with polygenic risk scores.
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