ReviewFrontiers in genetics2025
Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.
Review in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Imaging Strategies and Research Advances in Gastrointestinal Stromal Tumors.Current treatment options in oncology · 2026Review
- Targeting the Unmet Need in Gastrointestinal Stromal Tumors: A Contemporary Review of Investigational Clinical Trials and Therapeutic Landscape.Pharmaceuticals (Basel, Switzerland) · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal stromal tumors (GISTs) are uncommon tumors that arise from the gastrointestinal tract, making their early diagnosis and precise identification essential for the effective clinical management. Recently, the use of artificial intelligence (AI) technologies in medicine has grown significantly, showcasing remarkable potential, especially in analyzing the pathological images, radiological features, and genetic variations. This work compiles the most recent research on the application of AI in detecting and identifying GISTs, focusing on its role in pathological image analysis, the extraction of radiological characteristics, and the interpretation of genomic data. By offering a thorough overview of these advancements, this article aims to provide a valuable reference for future research and clinical practices related to the diagnosis and treatment of GISTs.
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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.