Yan YK, Zhang YL, Lu B, He ZL, Cheng XR, Song K, Chen YQ, Han JJ, Chen HD, Dai M. Modelling the impact of colorectal cancer screening strategies: A systematic review and meta-analysis. World J Gastroenterol 2026; 32(43): 122311 [DOI: 10.3748/wjg.122311]
Corresponding Author of This Article
Hong-Da Chen, Professor, Center for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China. chenhongda@pumch.cn
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Yan YK, Zhang YL, Lu B, He ZL, Cheng XR, Song K, Chen YQ, Han JJ, Chen HD, Dai M. Modelling the impact of colorectal cancer screening strategies: A systematic review and meta-analysis. World J Gastroenterol 2026; 32(43): 122311 [DOI: 10.3748/wjg.122311]
Yi-Ke Yan, Yue-Lun Zhang, Bin Lu, Zhi-Liang He, Xin-Ran Cheng, Yu-Qing Chen, Hong-Da Chen, Center for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
Yi-Ke Yan, Bin Lu, Zhi-Liang He, Min Dai, Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China
Bin Lu, Center for Clinical and Epidemiologic Research, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing 101118, China
Kai Song, Department of Gastroenterology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100730, China
Jing-Jing Han, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
Co-corresponding authors: Hong-Da Chen and Min Dai.
Author contributions: Dai M and Chen HD contributed equally to this work as co-corresponding authors; they supervised the study, contributed to the interpretation of findings, critically revised the manuscript, and approved the final version. Yan YK, Zhang YL, Lu B and He ZL contributed equally to this work as co-first authors; they made substantial intellectual contributions to the conception and design of the study, data extraction, data analysis and interpretation, and manuscript preparation. Specifically, Yan YK, Lu B and He ZL conducted data extraction; Yan YK performed data analysis and drafted the initial manuscript; Zhang YL and Lu B provided methodological support and contributed to manuscript revision; Cheng XR, Song K, Chen YQ and Han JJ verified the data. All authors reviewed and approved the final version of the manuscript.
AI contribution statement: ASReview, an open-source machine learning tool, was used during title and abstract screening solely to prioritize records by predicted relevance; all eligibility decisions were made by the authors. During manuscript preparation, GPT-5 (OpenAI, San Francisco, CA, United States) was used solely for linguistic refinement, grammar correction, formatting assistance, and improvement of clarity and readability. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Supported by Capital’s Funds for Health Improvement and Research (CFH), No. 2026-2G-4026; PUMCH Talent Development and Support Program (Category B), No. UGG06641; National Key Research and Development Project of China, No. 2024YFA0918501; the National Natural Science Foundation of China, No. 82273726 and No. 82473705; Beijing Research Ward Excellence Program, No. BRWEP2024W034010101; the Fundamental Research Funds for the Central Universities, Peking Union Medical College, No. 3332025126; and the Science and Technology Planning Project of Tibet Autonomous Region, No. XZ202501JD0021.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Corresponding author: Hong-Da Chen, Professor, Center for Prevention and Early Intervention, National Infrastructures for Translational Medicine, Institute of Clinical Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China. chenhongda@pumch.cn
Received: April 16, 2026 Revised: May 13, 2026 Accepted: June 26, 2026 Published online: November 21, 2026 Processing time: 166 Days and 4.5 Hours
Abstract
BACKGROUND
Long-term trial evidence on the benefits of colorectal cancer (CRC) screening, particularly for novel or hybrid strategies, remains scarce. Simulation models have been developed to address questions unfeasible for trials.
AIM
To synthesize model-based evidence on the long-term impact of CRC screening strategies.
METHODS
We systematically searched PubMed, EMBASE, and Web of Science for studies published between January 1, 2014, and March 3, 2025. Eligible studies were modelling studies evaluating CRC screening strategies in average-risk populations, focusing on CRC incidence, CRC-specific mortality, and all-cause mortality; screening-related harms and colonoscopy resource requirements were beyond the scope of the quantitative synthesis. Pooled risk ratios (RRs) were estimated using random-effects models, with subgroup analyses by model structure, natural history, and country. Cost-effectiveness evidence was synthesized qualitatively.
RESULTS
We included 102 eligible studies, assessing seven common strategies, ten emerging test-based strategies, and four hybrid strategies. All common strategies significantly reduced CRC burden compared to no screening, with 10-yearly colonoscopy achieving the greatest reductions (RR, 0.23; 95%CI: 0.19-0.28 for incidence; RR, 0.17; 95%CI: 0.14-0.21 for mortality) under perfect adherence. Emerging strategies, such as artificial intelligence-assisted colonoscopy, showed promising reductions in CRC burden in limited model-based analyses. Four hybrid screening strategies also demonstrated high efficacy. Adherence significantly influenced outcomes and specific model contributed to heterogeneity. Most common screening strategies were reported as cost-effective compared to no screening, while certain novel screening strategies were not cost-effective at current prices.
CONCLUSION
Novel and hybrid strategies may have the potential to reduce CRC burden. Real-world participation and model choice influence projected outcomes and should be considered in policy and practice.
Core Tip: This meta-analysis of modeling studies shows that colorectal cancer screening substantially reduces incidence and mortality, with 10-yearly colonoscopy being most effective under perfect adherence. Emerging and hybrid strategies appear promising but with uncertain cost-effectiveness. Outcomes are strongly influenced by adherence and modelling assumptions, highlighting the need to balance effectiveness, real-world implementation, and economic considerations in screening policy decisions.
Citation: Yan YK, Zhang YL, Lu B, He ZL, Cheng XR, Song K, Chen YQ, Han JJ, Chen HD, Dai M. Modelling the impact of colorectal cancer screening strategies: A systematic review and meta-analysis. World J Gastroenterol 2026; 32(43): 122311
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide, accounting for over 1.9 million new cases and 904000 deaths in 2022[1]. Population-based screening is central to CRC prevention, enabling early detection of precancerous lesions and localized cancers[2]. Over recent decades, available screening modalities have expanded from conventional methods such as colonoscopy and faecal immunochemical testing (FIT) to emerging technologies including cell-free DNA blood test (cf-bDNA)[3]. The growing diversity of screening options poses increasing challenges in comprehensively evaluating their long-term preventive effects, which are essential for informing population-based screening practices[4].
Although randomised controlled trials (RCTs) have demonstrated the benefits of CRC screening, their extended follow-up requirements, high costs, and limited scope across strategies restrict their ability to provide timely, comprehensive evidence on the population impact of evolving screening strategies[5]. Simulation models have become indispensable tools to address these gaps by synthesizing trial data, population statistics, and probability estimates to project long-term outcomes across diverse strategies[6]. Notably, evidence from Cancer Intervention and Surveillance Modelling Network simulation modelling has been systematically incorporated to inform the United States Preventive Services Task Force guidelines[7,8] and the American Cancer Society screening guidelines[9,10].
Currently, widely used models include Markov model, Monte Carlo simulation, and discrete event simulation[6]. Despite the proliferation of validated simulation models, systematic integration of model-based evidence on CRC screening remains limited. Prior reviews mainly focused on conventional strategies and mortality outcomes[11], overlooking novel tests, hybrid strategies, and incidence outcomes that are critical for long-term prevention.
To address these gaps, we conducted a systematic review and meta-analysis of modeling studies evaluating the long-term effectiveness of conventional, emerging, and hybrid CRC screening strategies on incidence and mortality, and, where available, contextual evidence on cost-effectiveness. Our findings aim to provide an integrated evidence base to support informed policymaking and guide future research.
To ensure the transparency and reproducibility of our research, especially regarding the application of advanced tools, we adhered to the TITAN[12] Guidelines for reporting artificial intelligence (AI).
MATERIALS AND METHODS
This systematic review and meta-analysis has been registered with PROSPERO and reported in accordance with the PRISMA[13], AMSTAR[14] guidelines.
Data sources and search strategy
We searched PubMed, EMBASE, and Web of science to identify all eligible studies published from 1 January 2014 to 3 March 2025. The main search terms combined both medical subject headings and free-text terms including “colorectal cancer”, “screening”, “incidence and mortality”, “cost effectiveness” and “modelling study” (the detailed search strategy is shown in Supplementary Tables 1-3). Additionally, to identify studies not captured by database searches, we manually reviewed reference lists of relevant reviews. This systematic review and meta-analysis was registered with the PROSPERO (registration number: CRD42024497660).
Study selection
This study included original simulation studies evaluating the effectiveness of various CRC screening strategies in average-risk populations, focusing on primary outcomes of CRC incidence, CRC-specific and all-cause mortality. Studies were excluded if they lacked detailed numerical data for primary outcomes of interest or if model-based cost-effectiveness analyses reported only the incremental cost-effectiveness ratio (ICER), quality-adjusted life-years (QALYs) gained, or life-years gained (LYG). Detailed inclusion and exclusion criteria are provided in Supplementary Table 4. During the title and abstract screening phase, all identified citations were imported into ASReview (https://asreview.nl/), an open-source machine learning tool that prioritizes records by predicted relevance[15]. The machine learning algorithm was used only to optimize the order of records presented, but did not replace human judgment. Each citation was independently screened by three reviewers (Yan YK, Lu B, He ZL) against the predefined inclusion and exclusion criteria. Full texts of potentially eligible studies were subsequently reviewed for final eligibility. Any discrepancy was resolved through discussion.
Data extraction and quality assessment
For each included study, one investigator independently extracted data and a second reviewer validated the data for accuracy. Extracted information included: (1) General study information (first author, year of publication and journal); (2) Population characteristics (sample size and country); (3) Intervention and comparator specifics (screening modality, interval, age, adherence and comparator); (4) Data for calculation of effect sizes of primary outcomes (e.g., number of new cases of CRC, CRC-specific mortality and all-cause mortality); (5) Cost-effectiveness analysis [discount rate, cost, willingness-to-pay (WTP), LYG, QALYs, and ICER]; and (6) Model parameters (model name, model type, natural history and time horizon). Study quality was assessed using a qualitative framework[11,16,17], which incorporated modelling approach (adenoma pathway, adenoma/tumor progression, sensitivity analyses, and calibration), input parameters, transparency of input data assumptions, and external validation. Input parameters included disease-related parameters (all-cause mortality, adenoma and CRC incidence, adenoma/CRC transition, and stage-specific survival), cohort characteristics (dynamic or static cohort), and intervention-related parameters (sensitivity/specificity of screening methods and adherence rates). Studies with two or more items unreported or unperformed were classified as high risk of bias; otherwise, they were considered low risk.
Data synthesis and primary analysis
We synthesized and analysed data by screening strategies including screening modality, screening age and screening interval. Based on screening modality, studies were categorized into three groups: Common, novel, and hybrid screening strategies. Primary meta-analyses were performed under perfect adherence scenarios and realistic adherence scenarios. Perfect adherence was defined as 100% compliance across all screening, diagnostic, and surveillance steps, representing the maximum achievable effectiveness of each screening strategy. Realistic adherence referred to adherence levels below 100% and was defined according to the scenarios reported in each included study, based on real-world data, published literature, or model assumptions informed by expert opinion or analogous screening tests. A random-effects model was conducted to estimate the pooled risk ratios (RRs) when data were available. Any data that could not be pooled were reported narratively. Heterogeneity was assessed using Cochran’s Q and I2 statistics, with I2 > 50% indicating significant heterogeneity[18]. Subsequently, subgroup analyses were performed to explore potential heterogeneity sources, stratified by specific model, model type, natural history assumptions, and country. Sensitivity analyses were conducted using an alternative assumed cohort size of 10000 participants per arm to assess the robustness of the primary pooled estimates.
For the secondary outcome, we performed a qualitative synthesis of cost-effectiveness analyses comparing screening strategies with no screening. ICERs were extracted from original studies or, when not reported, calculated as incremental cost divided by incremental LYG or QALYs. To reflect the context-specific nature of cost-effectiveness, ICERs were evaluated against the country- or region-specific WTP thresholds defined in each original study. Based on these thresholds, strategies were classified as: (1) Cost-saving (less costly and more effective); (2) Cost-effective (ICER ≤ WTP threshold); and (3) Not cost-effective (ICER > WTP threshold). Statistical analyses were conducted in R (version 4.3.1, R Foundation, Vienna, Austria, www.r-project.org) with a two-sided P value < 0.05 considered statistically significant.
RESULTS
Literature search
Figure 1 depicted the flowchart of the systematic search and selection process, with 102 eligible studies finally included in this analysis: Single screening strategies (n = 71), novel strategies (n = 16), and hybrid strategies (n = 17). For single screening strategies, we focused on seven common strategies (n = 36), guided by recent recommendations[7] and their widespread use: 10-yearly colonoscopy for ages 45-75, 5-yearly flexible sigmoidoscopy (FS) for ages 45-75, 5-yearly computed tomographic colonography (CTC) for ages 45-75, annual FIT for ages 45-75, biennial FIT for ages 45-75, annual mSEPT9 for ages 50-75, and triennial multitarget stool DNA (mt-sDNA) for ages 45-75.
Seventeen models were identified (Supplementary Table 5), with the most common being Model of Screening and Surveillance for Colorectal Cancer (MOSAIC, 10 studies), MIcrosimulation SCreening ANalysisColon (MISCAN-Colon, 10 studies), and CRC Simulated Population Model for Incidence and Natural History (CRC-SPIN, 6 studies). Most studies used Markov models (n = 49), comprising individual-level (n = 31) and cohort-based (n = 15) types. Exceptions included CAN-SCREEN and DECAS, which utilized discrete event simulation models (n = 3). Nearly all models employed a lifetime simulation framework. Quality assessment indicated that 17 studies were at high risk of bias (Supplementary Table 5). Most included studies reported model calibration (49/53) and external validation procedures (43/53).
Incidence and mortality outcomes of common screening strategies
Thirty-six studies evaluated common screening strategies: Colonoscopy[10,19-36] (n = 19), annual FIT[10,20-33,35-43] (n = 24), CTC[10,21,30,35,44,45] (n = 6), mSEPT9[44,46,47] (n = 3), mt-sDNA[10,21,24,26-28,30,33,35-39,41,48] (n = 15), FS[10,21,30,35,44,49-51] (n = 8) and biennial FIT[10,21,22,27,30,31,34,38,40,42,52-54] (n = 13). Under perfect adherence, various CRC screening strategies significantly reduced CRC incidence and CRC mortality compared to no screening (Figure 2, Supplementary Figures 1-17). Specifically, 10-yearly colonoscopy appeared most effective, reducing CRC incidence by 77% [RR: 0.23, 95% confidence interval (CI): 0.19-0.28] and mortality by 83% (RR: 0.17, 95%CI: 0.14-0.21). Annual FIT reduced CRC incidence by 64% (RR: 0.36, 95%CI: 0.32-0.41) and mortality by 77% (RR: 0.23, 95%CI: 0.20-0.27). Five-yearly CTC lowered CRC incidence by 65% (RR: 0.35, 95%CI: 0.30-0.41) and mortality by 76% (RR: 0.24, 95%CI: 0.20-0.28). Annual mSEPT9 reduced CRC incidence by 54% (RR: 0.46, 95%CI: 0.38-0.57) and mortality by 75% (RR: 0.25, 95%CI: 0.13-0.52). Triennial mt-sDNA reduced CRC incidence by 60% (RR: 0.40, 95%CI: 0.36-0.45) and mortality by 74% (RR: 0.26, 95%CI: 0.22-0.31). 5-yearly FS decreased CRC incidence by 61% (RR: 0.39, 95%CI: 0.36-0.42) and mortality by 72% (RR: 0.28, 95%CI: 0.25-0.33). Biennial FIT reduced CRC incidence by 49% (RR: 0.51, 95%CI: 0.46-0.57) and mortality by 69% (RR: 0.31, 95%CI: 0.26-0.36).
Figure 2 Summary of findings from pooled analysis of common screening strategies.
Screening strategies based on United States Preventive Services Task Force guidelines and the popularity. 1Screening strategies are presented as screening modality, age to begin-age to end screening, screening interval. 2Lifetime effects compared with no screening. CI: Confidence interval; CRC: Colorectal cancer; CTC: Computed tomography colonography; FIT: Fecal immunochemical test; FS: Flexible sigmoidoscopy; mSEPT9: Methylated Septin 9 DNA blood test; mt-sDNA: Multitarget stool-based DNA; RR: Risk ratio.
Under realistic adherence, the preventive effects of colonoscopy, FIT, CTC and FS on CRC incidence and mortality were significantly diminished compared to perfect adherence (P value of χ2 test for subgroup differences < 0.05; Supplementary Figures 1, 3-6, 8, and 14-17). In contrast, mt-sDNA and mSEPT9 exhibited relatively stable effects with minimal reduction (Figure 2). Sensitivity analyses using an alternative assumed cohort size of 10000 participants per arm yielded highly similar pooled estimates and did not materially alter the interpretation of the findings (Supplementary Table 6).
Subgroup analysis of the effectiveness of common screening strategies
Heterogeneity was observed across studies evaluating CRC incidence with colonoscopy (I2 = 58.1%, 100% adherence), CTC (I2 = 63.1%, 100% adherence), and mt-sDNA (I2 = 64.4%, realistic adherence) (Supplementary Figures 1-17). Subgroup analyses showed that heterogeneity persisted when stratified by model type, natural history assumptions, or country, with I2 remaining consistently high. In contrast, stratification by specific model (e.g., MISCAN, SimCRC, CRC-SPIN) substantially reduced heterogeneity, with I2 approaching zero.
Incidence and mortality outcomes of novel screening strategies
Sixteen studies[19,24,26-28,33,35,36,45,47,48,55-59] evaluated the effectiveness of 10 emerging potential screening tests (Table 1). Most studies were published after 2019 (n = 15). Thirteen studies were conducted in the United States[19,24,26-28,33,35,36,47,48,55,56,59], with one study each in Asia[58], Canada[57], and the Netherlands[45].
Table 1 Summary of screening effectiveness based on novel screening strategies1.
Screening strategies2 (modality, age range, interval)
Under perfect adherence, AI-assisted colonoscopy[19,55], next-generation MT-sDNA (ngMT-sDNA; Exact Sciences)[26,28,35], multitarget stool RNA (mt-sRNA)[35], FIT-RNA (Geneoscopy ColoSense)[28] and cf-bDNA (Guardant Shield)[28] demonstrated high effectiveness, reducing both CRC incidence and mortality by over 50%. AI-assisted colonoscopy showed superior efficacy, with a relative risk reduction of 82% for CRC incidence (RR = 0.18) and 87% for mortality (RR = 0.13)[55]. In contrast, triennial liquid biopsy exhibited lower efficacy, with a 2% reduction in CRC incidence (RR = 0.98) and a 6% reduction in mortality (RR = 0.94)[24].
Under realistic adherence conditions, invasive screening tests like AI-assisted colonoscopy exhibited reduced effectiveness, lowering CRC incidence by 49% (RR = 0.51) and mortality by 52% (RR = 0.48). In contrast, stool-based, blood-based, and urine metabolomic-based tests demonstrated relatively stable preventive effects.
Incidence and mortality outcomes of hybrid screening strategies
Seventeen studies assessed hybrid screening strategies integrating multiple modalities (Table 2, Supplementary Table 7). Most focused on concurrent use of two distinct modalities[21,24,35,49,50,60-62] (n = 8) or age-stratified sequential screening strategies[23,46,54,61,63-66] (n = 8). Additionally, one study[67] investigated a risk-stratified strategy, while two studies[51,54] explored gender-specific strategies.
Table 2 Summary of screening effectiveness based on hybrid strategies1.
When assuming perfect adherence, concurrent dual-modality screening, age-stratified sequential screening, gender-specific strategies and risk-stratified strategies yielded similar RR for CRC incidence relative to no screening, ranging from 0.17-0.48, 0.19-0.47, 0.36 and 0.36, respectively (Table 2). Analogous RRs of CRC mortality were presented in three hybrid screening paradigms, with mortality rates decreasing by 71% to 89% for concurrent dual-modality screening (RR = 0.11-0.29), 63% to 89% for age-stratified sequential screening (RR = 0.11-0.37) and 72% for gender-specific strategies (RR = 0.28).
Under realistic adherence, the effectiveness of hybrid screening strategies significantly decreased, with incidence reductions ranging from 7% to 42% for concurrent dual-modality screening (RR = 0.58-0.93), 12% to 61% for age-stratified sequential screening (RR = 0.39-0.88) and 15% to 50% for gender-specific strategies (RR = 0.50-0.85), and mortality reductions of 14% to 66% (RR = 0.34-0.86), 18% to 62% (RR = 0.38-0.82) and 26% (RR = 0.74), respectively (Table 2).
All-cause mortality for CRC screening strategies
Colonoscopy screening, as reported in two studies[68,69], reduced all-cause mortality by 1.4% to 6.4% over a 15- to 35-year period. Similarly, two studies[68,69] found that FIT decreased all-cause mortality by 1.1% to 6.7% over the same time frame. Additionally, two studies[68,70] indicated that FS lowered all-cause mortality by 0.6% to 1.1% over a 15-year period (Supplementary Table 8).
Cost-effectiveness analysis of CRC screening strategies
Thirty studies examined cost-effectiveness of common (n = 21), novel (n = 12) and hybrid screening strategies (n = 12) (Supplementary Tables 9 and 10). Under perfect adherence, most studies showed that common strategies, as well as selected novel approaches such as ngMT-sDNA, mt-sRNA, UMT, AI-assisted colonoscopy, and hybrid strategies, were cost-effective or cost-saving compared to no screening within their respective settings and WTP thresholds (Figure 3A, Supplementary Table 9). Five studies[27,28,33,35,56] simulating seven scenarios indicated that biennial and triennial cf-bDNA (Guardant Shield) were cost-effective. However, one study[28] demonstrated that annual cf-bDNA (Guardant Shield) was not cost-effective at its current price of $1450[28], and another[24] reported that triennial liquid biopsy was not cost-effective, with an ICER of $1062300-$1325429/LYG (WTP threshold: $100000/LYG).
Figure 3 Cost-effectiveness analysis of colorectal cancer screening strategies vs no screening.
Bar charts depict the number of modelled scenarios-varying in model, screening age and interval-in which each screening strategy was classified as cost-saving (blue), cost-effective (orange), or not cost-effective (gray) according to the original studies’ willingness-to-pay thresholds. A: Presents outcomes under perfect adherence; B: Presents outcomes under realistic adherence. AI: Artificial intelligence; cf-bDNA: Cell-free DNA blood test; CTC: Computed tomography colonography; FIT: Fecal immunochemical test; FOBT: Fecal occult blood testing; FS: Flexible sigmoidoscopy; MRC: MR colonography; mSEPT9: Methylated Septin 9 DNA blood test; mt-sDNA: Multitargeted stool-based DNA; mt-sRNA: Multitarget stool RNA; ngMT-sDNA: Next-generation MT-sDNA; UMT: Urine metabolomic-based test.
Under realistic adherence, common strategies, MRC, mt-sRNA, ngMT-sDNA, UMT and hybrid strategies were generally reported as cost-effective or cost-saving (Figure 3B, Supplementary Table 10). Findings for cf-bDNA were mixed: Two studies[35,59] suggested triennial cf-bDNA (Guardant Shield) could be cost-effective with adherence ≥ 90%, whereas another[56] reported it was not cost-effective with lower adherence (62.5%), yielding an ICER of $137217/QALY (WTP: $100000/QALY gained). Across studies, adherence level, test interval, and cost were key drivers of cost-effectiveness outcomes.
DISCUSSION
This review provides a comprehensive synthesis of model-based evidence on the effectiveness of CRC screening strategies. The findings indicate that common strategies reduce both CRC incidence and mortality, with colonoscopy appearing to be the most effective option under perfect adherence. Novel and hybrid strategies also demonstrate high efficacy. Adherence significantly attenuates screening effectiveness, while the choice of simulation model is a major source of heterogeneity. Colonoscopy, FIT and FS screening may reduce all-cause mortality rates. Most CRC screening strategies are cost-effective or cost-saving compared to no screening.
Our study demonstrates that colonoscopy, FIT, and FS significantly reduce CRC incidence and mortality, with colonoscopy emerging as the most effective modality[71], aligning with findings from RCTs[72-76] and cohort studies[4]. For guideline-recommended modalities lacking direct trial evidence like CTC and mt-sDNA, model-based analyses demonstrate potential reductions in CRC burden under both perfect and realistic adherence. Notably, simulated estimates of effectiveness appear more favourable than those reported in observational studies. For instance, observational data[4] show colonoscopy reduces CRC incidence by 57% and mortality by 64%, compared to model-based estimates of 77% and 83%, respectively. Similarly, although previous studies[77,78] reported no significant association between CRC screening and all-cause mortality, simulation results suggest colonoscopy, FIT, and FS may reduce all-cause mortality. These discrepancies may be attributed to uncertainties in model parameters[17], assumptions regarding long-term adherence, and the ability of simulation studies to incorporate lifetime screening intervals and extended follow-up horizons, which allow estimation of cumulative protective effects over longer periods[79]. In addition, CRC-related deaths account for only a small proportion of total mortality, making screening-related benefits difficult to detect in conventional trials with limited observation windows and statistical power[70,80]. Furthermore, differences in how competing mortality risks are incorporated across simulation models may also influence projected all-cause mortality benefits[81]. Therefore, although simulation models suggest reductions in all-cause mortality, the absolute benefits are likely modest and should be interpreted cautiously when considering their clinical significance.
The landscape of CRC screening is being reshaped by both the optimization of conventional modalities and the emergence of novel technological innovations[2]. Although RCTs comparing these rapidly expanding alternatives are often impractical, simulation models based on test characteristics offer valuable insights. Our findings indicate that several novel strategies may have the potential to reduce the CRC burden. For instance, AI-assisted colonoscopy may enhance polyp detection and lower CRC mortality by up to 87% under perfect adherence[55]. However, these findings likely represent best-case scenarios and may not generalize to all AI systems, as real-world effectiveness may vary according to AI system performance, clinical setting, and endoscopist experience. Meanwhile, advances in diagnostic technology have facilitated the development of novel non-invasive screening tests designed to improve participation among individuals reluctant to undergo colonoscopy[5]. Nevertheless, at current pricing, blood-based tests may not be cost-effective compared to no screening within the local WTP thresholds, especially with frequent testing or lower adherence[28,56]. Given the limited and highly context-dependent evidence, further studies are needed to confirm the effectiveness, cost-effectiveness, and implementation value of these emerging screening strategies.
Adherence is a critical determinant of CRC screening effectiveness. Our analysis reveals that the protective benefits of endoscopic strategies, FIT, and hybrid approaches are significantly attenuated under realistic adherence. This reduction likely reflects multiple barriers, such as embarrassment, discomfort from invasive nature, bowel preparation[82], and frequent FIT testing. In contrast, mSEPT9 and mt-sDNA tests may maintain more stable effectiveness across adherence scenarios, likely reflecting their higher real-world uptake. Accordingly, to maximize screening outcomes, multilevel interventions to boost adherence such as organized mailed FIT outreach, patient navigation, and reminders[83-85] should be prioritized. Additionally, digital health technologies, particularly AI-enhanced patient navigation systems[86], offer a promising approach. Moreover, current simulation models informing clinical guidelines assume 100% adherence to screening, follow-up, and surveillance, far exceeding real-world rates, which may inflate projected benefits. Future models should incorporate country-specific adherence rates and longitudinal participation patterns to bridge the evidence-practice gap.
This study identifies the specific model as a primary determinant of heterogeneity in screening effectiveness estimates, with the MISCAN-Colon model demonstrating a relatively conservative protective effect. This is consistent with previous comparative modelling evidence showing that MISCAN may generate lower screening effectiveness estimates than CRC-SPIN and SimCRC[87]. Kuntz et al[87] found that, although these models were calibrated to the same adenoma prevalence and CRC incidence data, their mean overall dwell times differed substantially, ranging from 10.6 years in MISCAN to approximately 25 years in CRC-SPIN and SimCRC. A larger proportion of CRC cases in MISCAN arose from adenomas developing within 10 years before diagnosis, implying fewer opportunities for prevention by long-interval screening. These differences suggest that unobserved structural parameters, including adenoma progression rates, progressive vs non-progressive adenoma assumptions, and dwell time, may influence screening effectiveness beyond broad model classifications, such as model type or natural history assumptions. Therefore, greater transparency in reporting model structures and progression assumptions[88], as well as coordinated cross-model comparisons, may be critical for strengthening the credibility and utility of simulation-based evidence. Beyond structural transparency, model calibration and external validation are also essential to ensure that simulation models reflect local CRC epidemiology and generate credible long-term projections. Although most studies reported calibration procedures (49/53) and external validation (43/53), the level of detail regarding calibration targets, data sources, and validation outcomes varied across studies, which may limit comparability and should be considered when interpreting heterogeneity.
Unlike recent reviews, this study incorporates novel and hybrid strategies and evaluates outcomes under both perfect and realistic adherence. Nevertheless, several limitations must be acknowledged. First, inclusion was restricted to English-language publications, which may have excluded relevant studies published in other languages. Second, the assumption of a fixed cohort size (n = 1000 per arm) for simulated studies may influence variance estimation in pooled analyses. Simulation modelling studies often report standardized outcomes (e.g., per 1000 or 10000 individuals) rather than actual simulated cohort sizes, limiting the feasibility of reconstructing study-specific variances. To standardize variance estimation, a fixed cohort size was applied. Nevertheless, sensitivity analyses using an alternative assumed cohort size (n = 10000 per arm) yielded highly similar pooled estimates, suggesting that the main findings were robust to reasonable alternative assumptions. To the best of our knowledge, there is currently no standardized methodological framework for synthesizing simulation modelling results, and further methodological research is warranted. Third, our findings suggest that heterogeneity in CRC screening studies largely stems from the choice of model. In this study, MOSAIC and MISCAN-Colon models were the most frequently utilized models, which may have substantially influenced the results. Fourth, real-world adherence was heterogeneously defined and implemented across included studies, with variation in fixed probabilities, independent per-test assumptions, longitudinal adherence patterns, and population-level behavioral patterns (e.g., never screenees, intermittent participants, and consistent screenees). Because adherence structures and calculation methods were often insufficiently reported, standardization was not feasible. In addition, limited evidence on realistic adherence for some strategies may have reduced statistical power; therefore, findings under realistic adherence scenarios should be interpreted cautiously. Fifth, this study primarily focuses on long-term effectiveness and does not account for potential harms or colonoscopy resource requirements, which are critical for the practical implementation of CRC screening. Several included modelling studies consider procedure-related harms, particularly bleeding and perforation associated with colonoscopy[68,89,90], as well as other gastrointestinal and cardiovascular adverse events[68], in addition to increased colonoscopy demand[34,60] under intensive screening or surveillance strategies. These factors should be considered when interpreting projected benefits and in future modelling and policy evaluations of screening strategies.
CONCLUSION
This systematic review and meta-analysis offers a comprehensive and up-to-date synthesis of model-based evidence on the long-term effectiveness of various CRC screening strategies. Guideline-recommended strategies markedly reduce CRC incidence and mortality, with colonoscopy yielding the greatest benefit. Novel and hybrid strategies show promise in reducing CRC burden, but vary in cost-effectiveness depending on context, adherence and cost. Multilevel interventions to boost participation are crucial for optimizing screening effectiveness, and integrating multiple rigorously validated models is essential for robust evidence synthesis.
US Preventive Services Task Force, Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, Donahue KE, Doubeni CA, Krist AH, Kubik M, Li L, Ogedegbe G, Owens DK, Pbert L, Silverstein M, Stevermer J, Tseng CW, Wong JB. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement.JAMA. 2021;325:1965-1977.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1663][Cited by in RCA: 1590][Article Influence: 318.0][Reference Citation Analysis (6)]
Wolf AMD, Fontham ETH, Church TR, Flowers CR, Guerra CE, LaMonte SJ, Etzioni R, McKenna MT, Oeffinger KC, Shih YT, Walter LC, Andrews KS, Brawley OW, Brooks D, Fedewa SA, Manassaram-Baptiste D, Siegel RL, Wender RC, Smith RA. Colorectal cancer screening for average-risk adults: 2018 guideline update from the American Cancer Society.CA Cancer J Clin. 2018;68:250-281.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 1293][Cited by in RCA: 1371][Article Influence: 171.4][Reference Citation Analysis (7)]
Meester RGS, Peterse EFP, Knudsen AB, de Weerdt AC, Chen JC, Lietz AP, Dwyer A, Ahnen DJ, Siegel RL, Smith RA, Zauber AG, Lansdorp-Vogelaar I. Optimizing colorectal cancer screening by race and sex: Microsimulation analysis II to inform the American Cancer Society colorectal cancer screening guideline.Cancer. 2018;124:2974-2985.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 67][Cited by in RCA: 75][Article Influence: 9.4][Reference Citation Analysis (0)]
Agha RA, Mathew G, Rashid R, Kerwan A, Al-Jabir A, Sohrabi C, Franchi T, Nicola M, Agha M; TITAN Group. Transparency In The reporting of Artificial Intelligence – the TITAN guideline.PJS. 2025;10:100082.
[PubMed] [DOI] [Full Text]
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews.Int J Surg. 2021;88:105906.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 7951][Cited by in RCA: 5653][Article Influence: 1130.6][Reference Citation Analysis (8)]
van de Schoot R, de Bruin J, Schram R, Zahedi P, de Boer J, Weijdema F, Kramer B, Huijts M, Hoogerwerf M, Ferdinands G, Harkema A, Willemsen J, Ma Y, Fang Q, Hindriks S, Tummers L, Oberski DL. An open source machine learning framework for efficient and transparent systematic reviews.Nat Mach Intell. 2021;3:125-133.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 49][Cited by in RCA: 468][Article Influence: 93.6][Reference Citation Analysis (0)]
Thiruvengadam NR, Coté GA, Gupta S, Rodrigues M, Schneider Y, Arain MA, Solaimani P, Serrao S, Kochman ML, Saumoy M. An Evaluation of Critical Factors for the Cost-Effectiveness of Real-Time Computer-Aided Detection: Sensitivity and Threshold Analyses Using a Microsimulation Model.Gastroenterology. 2023;164:906-920.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 20][Cited by in RCA: 21][Article Influence: 7.0][Reference Citation Analysis (1)]
Half EE, Levi Z, Mannalithara A, Leshno M, Ben-Aharon I, Abu-Freha N, Silverman B, Ladabaum U. Colorectal cancer screening at age 45 years in Israel: Cost-effectiveness and global implications.Cancer. 2024;130:901-912.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 7][Reference Citation Analysis (0)]
Peterse EFP, Meester RGS, Siegel RL, Chen JC, Dwyer A, Ahnen DJ, Smith RA, Zauber AG, Lansdorp-Vogelaar I. The impact of the rising colorectal cancer incidence in young adults on the optimal age to start screening: Microsimulation analysis I to inform the American Cancer Society colorectal cancer screening guideline.Cancer. 2018;124:2964-2973.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 179][Cited by in RCA: 177][Article Influence: 22.1][Reference Citation Analysis (4)]
van den Puttelaar R, Nascimento de Lima P, Knudsen AB, Rutter CM, Kuntz KM, de Jonge L, Escudero FA, Lieberman D, Zauber AG, Hahn AI, Inadomi JM, Lansdorp-Vogelaar I. Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With a Blood Test That Meets the Centers for Medicare & Medicaid Services Coverage Decision.Gastroenterology. 2024;167:368-377.
[RCA] [PubMed] [DOI] [Full Text] [Full Text (PDF)][Cited by in Crossref: 61][Cited by in RCA: 60][Article Influence: 30.0][Reference Citation Analysis (0)]
Forbes SP, Yay Donderici E, Zhang N, Sharif B, Tremblay G, Schafer G, Raymond VM, Talasaz A, Eagle C, Das AK, Grady WM. Population health outcomes of blood-based screening for colorectal cancer in comparison to current screening modalities: insights from a discrete-event simulation model incorporating longitudinal adherence.J Med Econ. 2024;27:991-1002.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 7][Reference Citation Analysis (0)]
Ebner D, Kisiel J, Barnieh L, Sharma R, Smith NJ, Estes C, Vahdat V, Ozbay AB, Limburg P, Fendrick AM. The cost-effectiveness of non-invasive stool-based colorectal cancer screening offerings from age 45 for a commercial and medicare population.J Med Econ. 2023;26:1219-1226.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 9][Reference Citation Analysis (0)]
Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, Taveira F, Spadaccini M, Antonelli G, Ebigbo A, Kudo SE, Arribas J, Barua I, Kaminski MF, Messmann H, Rex DK, Dinis-Ribeiro M, Hassan C. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study.Lancet Digit Health. 2022;4:e436-e444.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 201][Cited by in RCA: 174][Article Influence: 43.5][Reference Citation Analysis (9)]
Lew JB, St John DJB, Macrae FA, Emery JD, Ee HC, Jenkins MA, He E, Grogan P, Caruana M, Sarfati D, Greuter MJE, Coupé VMH, Canfell K. Evaluation of the benefits, harms and cost-effectiveness of potential alternatives to iFOBT testing for colorectal cancer screening in Australia.Int J Cancer. 2018;143:269-282.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 20][Cited by in RCA: 39][Article Influence: 4.9][Reference Citation Analysis (0)]
Sekiguchi M, Igarashi A, Matsuda T, Matsumoto M, Sakamoto T, Nakajima T, Kakugawa Y, Yamamoto S, Saito H, Saito Y. Optimal use of colonoscopy and fecal immunochemical test for population-based colorectal cancer screening: a cost-effectiveness analysis using Japanese data.Jpn J Clin Oncol. 2016;46:116-125.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 3][Cited by in RCA: 18][Article Influence: 1.6][Reference Citation Analysis (0)]
Bretthauer M, Løberg M, Wieszczy P, Kalager M, Emilsson L, Garborg K, Rupinski M, Dekker E, Spaander M, Bugajski M, Holme Ø, Zauber AG, Pilonis ND, Mroz A, Kuipers EJ, Shi J, Hernán MA, Adami HO, Regula J, Hoff G, Kaminski MF; NordICC Study Group. Effect of Colonoscopy Screening on Risks of Colorectal Cancer and Related Death.N Engl J Med. 2022;387:1547-1556.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 669][Cited by in RCA: 595][Article Influence: 148.8][Reference Citation Analysis (7)]
Atkin W, Wooldrage K, Parkin DM, Kralj-Hans I, MacRae E, Shah U, Duffy S, Cross AJ. Long term effects of once-only flexible sigmoidoscopy screening after 17 years of follow-up: the UK Flexible Sigmoidoscopy Screening randomised controlled trial.Lancet. 2017;389:1299-1311.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 316][Cited by in RCA: 285][Article Influence: 31.7][Reference Citation Analysis (4)]
Segnan N, Armaroli P, Bonelli L, Risio M, Sciallero S, Zappa M, Andreoni B, Arrigoni A, Bisanti L, Casella C, Crosta C, Falcini F, Ferrero F, Giacomin A, Giuliani O, Santarelli A, Visioli CB, Zanetti R, Atkin WS, Senore C; SCORE Working Group. Once-only sigmoidoscopy in colorectal cancer screening: follow-up findings of the Italian Randomized Controlled Trial--SCORE.J Natl Cancer Inst. 2011;103:1310-1322.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 513][Cited by in RCA: 445][Article Influence: 29.7][Reference Citation Analysis (7)]
Holme Ø, Løberg M, Kalager M, Bretthauer M, Hernán MA, Aas E, Eide TJ, Skovlund E, Lekven J, Schneede J, Tveit KM, Vatn M, Ursin G, Hoff G; NORCCAP Study Group†. Long-Term Effectiveness of Sigmoidoscopy Screening on Colorectal Cancer Incidence and Mortality in Women and Men: A Randomized Trial.Ann Intern Med. 2018;168:775-782.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 138][Cited by in RCA: 132][Article Influence: 16.5][Reference Citation Analysis (1)]
Moadel AB, Galeano D, Bakalar J, Garrett C, Greenstone S, Segev A, Baruchi I, Baruchi RP, Kalnicki S. AI virtual patient navigation to promote re-engagement of U.S. inner city patients nonadherent with colonoscopy appointments: A quality improvement initiative.J Clin Oncol. 2024;42:100-100.
[PubMed] [DOI] [Full Text]
