ArticleClinical and experimental medicine2025
Harnessing artificial intelligence for detection of pancreatic cancer: a machine learning approach.
Article in Clinical and experimental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the biliary microbiome in hepatopancreatobiliary disorders: a comprehensive systematic review of microbial signatures and diagnostic potential.BMC gastroenterology · 2025Pooled it
- Integrated periodontal pathogens and circulating miRNAs: a novel non-invasive panel of biomarkers for pancreatic cancer.Frontiers in cellular and infection microbiology · 2025Article
- Machine learning and microbiome analysis for early detection of pancreatic cancer.Gastroenterology and hepatology from bed to bench · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePancreatic cancer (PC) is one of the most lethal malignancies, often presenting with nonspecific symptoms and a dismal prognosis. Despite advancements in treatments, the 5-year survival rate remains low, highlighting the urgent need for effective early diagnostic approaches.
methodsThis study investigated the potential of an artificial intelligence (AI)-based approach to enhance diagnostic accuracy for PC by integrating diverse data, including clinical variables, laboratory results, and biomarkers such as two circulating microRNAs and two periodontal pathogens. A cohort of 123 PC patients and 120 matched non-cancer controls were used to train the machine learning (ML) models.
resultsFindings indicated that new-onset diabetes, levels of miR-21 and miR-155 in blood, and loads of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans in the oral cavity were the most important predictors of PC. Among predictive models, the ensemble learning exhibited superior accuracy with an area under the curve (AUC) of 0.87, a sensitivity of 0.89, and a specificity of 0.86.
conclusionThis research highlights the promise of integrating AI techniques to improve early detection of PC using non-invasive biomarkers. However, further external validation in diverse and multinational cohorts is needed to confirm generalizability before clinical implementation. If validated, this approach could pave the way for personalized screening strategies.
Indexed as
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.