Background/Objectives:This study presents and explores the potential of Updated Bayesian Deduction (UBD) using colorectal cancer (CRC) detection and prioritisation as a case example. Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, and its prognosis strongly depends on early detection and timely treatment. In Chile, colonoscopy waiting lists forsymptomaticpatients in public hospitals can exceed one year, limiting access to early diagnosis and reducing survival rates. Traditional single-test screening strategies, such as a single faecal immunochemical test (FIT), often yield uncertain results, contributing to inefficiencies in resource allocation.Methods:We propose a deductive approach that integrates evidence from multiple sequential and independent FITs to dynamically update the posterior probability of CRC. A case study is analysed with this Updated Bayesian Deduction over a four-round FIT protocol to assess how this could improve risk stratification compared to standard symptoms-based screening.Results:Our mathematical model shows that over 85% of colonoscopies for symptomatic patients were not urgent. We then demonstrate that, if 4-FIT UBD were used to screen Chile’s Metropolitan Region population, only 96 out of 100,000 people would require an urgent colonoscopy to detect the 19.6 out of 100,000 individuals with CRC in this region. Many countries cannot afford a colonoscopy-based population screening, such as what is performed in Germany. Performing 4x FITs + a very small number of colonoscopies would be much more affordable and would get more countries to adopt general CRC screening.Conclusions:In countries with limited colonoscopy availability, such as Chile, where symptomatic patients can wait over a year for treatment in public hospitals, implementing a UBD-based strategy could drastically reduce costs and optimise the use of resources. This would improve access to colonoscopies for critical cases and ultimately enhance five-year survival rates. These findings highlight UBD as a promising approach for evidence-based precision medicine in CRC screening and prioritisation that is both explainable and adaptable.
背景/目的：本研究以结直肠癌（CRC）检测与优先级排序为例，介绍并探讨更新贝叶斯演绎法的应用潜力。结直肠癌是全球癌症相关死亡的主要原因，其预后高度依赖于早期发现和及时治疗。在智利，公立医院有症状患者的结肠镜检查等待时间可能超过一年，这限制了早期诊断的机会并降低了生存率。传统的单一检测筛查策略（如单次粪便免疫化学检测）常产生不确定结果，导致资源配置效率低下。 方法：我们提出一种演绎推理方法，通过整合多次连续独立FIT检测的证据，动态更新CRC的后验概率。本研究通过四轮FIT检测方案，运用更新贝叶斯演绎法进行案例分析，评估该方法相较于标准症状筛查在风险分层方面的改进效果。 结果：数学模型显示，超过85%的有症状患者结肠镜检查并非紧急需求。研究表明，若采用四轮FIT更新贝叶斯演绎法筛查智利首都大区人口，每10万人中仅需96例紧急结肠镜检查即可发现该地区每10万人中19.6例CRC患者。许多国家无法承担基于结肠镜的全民筛查（如德国模式），而四次FIT检测配合极少量结肠镜检查的方案更具经济可行性，有助于推动更多国家实施全民CRC筛查。 结论：在结肠镜检查资源有限的国家（如智利），有症状患者在公立医院可能需等待超过一年才能获得治疗，实施基于更新贝叶斯演绎法的策略可大幅降低医疗成本并优化资源配置。这将提高危重病例获得结肠镜检查的机会，最终提升五年生存率。研究结果表明，更新贝叶斯演绎法作为一种可解释、可调整的循证精准医疗方法，在CRC筛查与优先级排序领域具有广阔应用前景。
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