ArticleWorld journal of gastrointestinal oncology2025
Early cancer diagnosis
Article in World journal of gastrointestinal 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.
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe early diagnosis rate of pancreatic ductal adenocarcinoma (PDAC) is low and the prognosis is poor. It is important to develop an interpretable noninvasive early diagnostic model in clinical practice.
aimTo develop an interpretable noninvasive early diagnostic model for PDAC using plasma extracellular vesicle long RNA (EvlRNA).
methodsThe diagnostic model was constructed based on plasma EvlRNA data. During the process of establishing the model, EvlRNA-index was introduced, and four algorithms were adopted to calculate EvlRNA-index. After the model was successfully constructed, performance evaluation was conducted. A series of bioinformatics methods were adopted to explore the potential mechanism of EvlRNA-index as the input feature of the model. And the relationship between key characteristics and PDAC were explored at the single-cell level.
resultsA novel interpretable machine learning framework was developed based on plasma EvlRNA. In this framework, a two-layer classifier was established. A new concept was proposed: EvlRNA-index. Based on EvlRNA-index, a cancer diagnostic model was established, and a good diagnostic effect was achieved. The accuracy of PDACandCPvsHealth-Probabilistic PCA Index-SVM (PDAC and chronic pancreatitis
conclusionAn interpretable two-layer machine learning framework was proposed for early diagnosis and prediction of PDAC based on plasma EvlRNA, providing new insights into the clinical value of EvlRNA.
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.