文章：

利用机器学习分析溃疡性结肠炎相关结直肠癌的风险因素：基于日本国家数据库的回顾性纵向研究

Analysis of Risk Factors for Colorectal Cancer Associated with Ulcerative Colitis Using Machine Learning: A Retrospective Longitudinal Study Using a National Database in Japan

原文发布日期：24 November 2025

DOI: 10.3390/cancers17233752

类型: Article

开放获取: 是

英文摘要：

Background/Objectives:Ulcerative colitis (UC) is a chronic inflammatory bowel disease that significantly increases the colorectal cancer (CRC) risk. This study used nationwide data on intractable diseases to clarify the clinical epidemiology of UC-related CRC in Japan.Methods: Patients diagnosed with UC between FY 2003 and 2011 were included. The relative incidence ratio (RR) was calculated using the standardized incidence ratio from the National Cancer Registry. To compare prognostic factors, outcomes were evaluated using the Cox proportional hazards model analysis for cancer occurrence, and a prognostic prediction model was developed using machine learning.Results: Among 78,556 patients with UC, CRC was identified in 141 patients. The RR of CRC peaked in both males and females in the 25–39 age group. Univariate analysis revealed several risk factors, including pseudo-polyps observed during endoscopy (hazard ratio 2.92,p= 0.001), abnormal crypt architecture (hazard ratio 3.14,p< 0.001), and dysplasia (hazard ratio 11.31,p< 0.001) in biopsy. Conversely, 5-ASA was associated with reduced CRC risk (hazard ratio 0.36,p= 0.003). The machine learning model categorized patients into three groups, demonstrating that the group with the highest number of patients with pancolitis had a significantly higher risk of CRC than did the other groups.Conclusions: Pseudo-polyps and dysplasia represent CRC risk factors in patients with UC. Additionally, machine learning analysis indicates that pancolitis in individuals in their 50s increases the risk of colon cancer, while proctitis in those in their 30s raises rectal cancer risk. These findings aim to enhance early detection and improve prevention efforts for UC-related CRC.

摘要翻译： 

背景/目的：溃疡性结肠炎是一种慢性炎症性肠病，会显著增加结直肠癌风险。本研究利用日本全国难治性疾病数据，旨在阐明日本UC相关CRC的临床流行病学特征。 方法：纳入2003至2011财年确诊的UC患者。采用国家癌症登记标准化发病率计算相对发病率。通过Cox比例风险模型分析癌症发生的预后因素，并运用机器学习构建预后预测模型。 结果：在78,556例UC患者中，141例确诊CRC。25-39岁年龄组男女患者的CRC相对发病率均达峰值。单因素分析显示内镜下假性息肉（风险比2.92，p=0.001）、活检异常隐窝结构（风险比3.14，p<0.001）及异型增生（风险比11.31，p<0.001）为危险因素，而5-ASA可降低CRC风险（风险比0.36，p=0.003）。机器学习模型将患者分为三组，证实全结肠炎患者数量最多的组别CRC风险显著高于其他组。 结论：假性息肉和异型增生是UC患者发生CRC的危险因素。机器学习分析进一步表明，50岁人群的全结肠炎会增加结肠癌风险，而30岁人群的直肠炎会提高直肠癌风险。这些发现有助于加强UC相关CRC的早期检测与预防工作。

原文链接：

Analysis of Risk Factors for Colorectal Cancer Associated with Ulcerative Colitis Using Machine Learning: A Retrospective Longitudinal Study Using a National Database in Japan