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<p>Cancer development is influenced by a complex interplay of diverse risk factors, but synthesizing this fragmented research remains challenging. To address this, we developed CanRisk-DB, an Artificial intelligence (AI)-driven database that systematically aggregates and standardizes published evidence on cancer-associated risk factors. Using a multi-stage AI pipeline based on the PICOS framework, we analyzed 435,975 publications from PubMed, Embase, and Cochrane (2000-2024), employing a Graph-based Retrieval-Augmented Generation framework to extract cancer types, risk factors, and quantitative estimates (e.g., relative risk [RR], hazard ratio [HR], standardized incidence ratio [SIR]). In the literature screening stage, the system demonstrated high accuracy and efficiency. From 9550 relevant articles, CanRisk-DB compiled 445,646 standardized records covering 76 risk factor groups and 42 cancer types across 80 countries over 50 years. Validated against benchmark datasets, this publicly accessible resource constitutes a comprehensive knowledge base of cancer risk factors, supporting etiological research, risk analyses, and the development of evidence-informed prevention strategies. The CanRisk-DB is available at <a href="http://www.canrisk-ai.com">http://www.canrisk-ai.com</a>.</p>

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CanRisk-DB: an artificial intelligence-driven comprehensive database of cancer risk factors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

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  • Yongjie Xu,
  • Changfa Xia,
  • Shiyuan Tong,
  • Hui Yu,
  • Fang Liu,
  • Shiqing Chen,
  • Jing Liu,
  • Yujie Wu,
  • Jiachen Wang,
  • Qianru Li,
  • Sibo Zhu,
  • Wanqing Chen

摘要

Cancer development is influenced by a complex interplay of diverse risk factors, but synthesizing this fragmented research remains challenging. To address this, we developed CanRisk-DB, an Artificial intelligence (AI)-driven database that systematically aggregates and standardizes published evidence on cancer-associated risk factors. Using a multi-stage AI pipeline based on the PICOS framework, we analyzed 435,975 publications from PubMed, Embase, and Cochrane (2000-2024), employing a Graph-based Retrieval-Augmented Generation framework to extract cancer types, risk factors, and quantitative estimates (e.g., relative risk [RR], hazard ratio [HR], standardized incidence ratio [SIR]). In the literature screening stage, the system demonstrated high accuracy and efficiency. From 9550 relevant articles, CanRisk-DB compiled 445,646 standardized records covering 76 risk factor groups and 42 cancer types across 80 countries over 50 years. Validated against benchmark datasets, this publicly accessible resource constitutes a comprehensive knowledge base of cancer risk factors, supporting etiological research, risk analyses, and the development of evidence-informed prevention strategies. The CanRisk-DB is available at http://www.canrisk-ai.com.