ArticleNPJ precision oncology2025
CanRisk-DB: an artificial intelligence-driven comprehensive database of cancer risk factors.
Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi-cancer early detection: from promise to practice and the next frontier.Cancer biology & medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
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 .
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.