Colorectal cancer risk in ulcerative colitis: an updated population-based systematic review and meta-analysis
Bibliographic record
Abstract
Background Patients with ulcerative colitis (UC) face a heightened risk of colorectal cancer (CRC), though the estimated risk levels differ across UC populations. This study aims to provide updated, population-based estimates of CRC incidence, standardised incidence ratios (SIR), and prevalence in patients with UC. Methods We searched PubMed, Embase, and Cochrane Library to April 12, 2025, for population-based studies on patients with UC reporting CRC risk. Study quality was assessed using the Newcastle–Ottawa Scale. The primary outcome was CRC risk in UC, evaluated through incidence, SIR, and prevalence. A random-effects model was used for meta-analysis, and meta-regression evaluated the impact of study characteristics. Publication bias was assessed using funnel plots and statistical tests. PROSPERO: CRD42025634800. Findings From 7991 records, 13 population-based studies involving 161,157 patients with UC were included. Most studies were conducted in Europe, with others from North America and Asia. All studies were of good quality, with scores greater than 5 on the NOS quality assessment scale. The pooled CRC incidence was 1.47 per 1000 person-years (95% CI 1.30–1.67; I 2 = 66.2%), the SIR was 2.48 (95% CI 1.64–3.76; I 2 = 91.7%), and the prevalence was 1.54% (95% CI 1.14–1.99; I 2 = 96.1%). Subgroup analyses revealed similar CRC risk in male (SIR 2.14, 95% CI 0.85–5.38) and female (SIR 2.20, 95% CI 1.52–3.19) patients and an increased risk with extensive colitis, with an SIR of 3.95 (95% CI 2.56–6.09). Interpretation This systematic review and meta-analysis provides population-based estimates of CRC risk in patients with UC, based on high-quality studies with rigorous methodology. The results offer reliable reference values for incidence, SIR, and prevalence, which are applicable to the broader UC population and relevant to clinical decision-making and public health planning. Nonetheless, substantial heterogeneity across studies and limited geographic representation—particularly from Asia, South America, Africa, and Oceania—highlight the need for additional population-based research in underrepresented regions to improve the global applicability of CRC risk estimates in UC. Funding This study was supported by State key Laboratory of Digestive Health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".