ArticleNPJ precision oncology2025
A large-scale, multi-centre validation study of an AI-empowered blood-based test for multi-cancer early detection.
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 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Value of Machine Learning Models for Cell-Free DNA-Based Multi-Cancer Early Detection: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Paired Capillary and Venous Serum Specimen Agreement for Protein Biomarker Measurements and Risk Category Outputs in a Multi-Cancer Early Detection Tool.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence-assisted photodynamic diagnosis and photodynamic therapy against cancer.Frontiers in oncology · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Cancer is a critical global health issue, especially in low- and middle-income countries (LMICs). In this study, we integrated four additional cohorts to assess the performance and robustness of an AI-empowered blood-based test (named OncoSeek) for multi-cancer early detection (MCED). It included a case-control cohort of symptomatic cancer patients, a prospective blinded study, and two retrospective cohorts conducted on two distinct platforms. Combining these with previously published one training and two validation cohorts, we evaluated OncoSeek's performance in 15,122 participants (3029 cancer patients and 12,093 non-cancer individuals) from seven centres in three countries, using four platforms and two sample types. OncoSeek showed adequate performance for MCED with an area under the curve (AUC) of 0.829, 58.4% sensitivity, 92.0% specificity, and overall accuracy of 70.6% in tissue of origin (TOO) prediction for the true positives. The test could detect 14 common cancer types, accounting for 72% of global cancer deaths, with sensitivities ranging from 38.9 to 83.3%. Additionally, the symptomatic cohort exhibited a high sensitivity of 73.1% at 90.6% specificity, indicating OncoSeek's potential for cancer early diagnosis. These findings underscore OncoSeek's consistent performances across diverse populations, platforms, and sample types, offering affordable and accessible multi-cancer early detection, especially for LMICs.
Identifiers
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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.