ReviewRadiology advances2025
Early detection of pancreatic cancer on computed tomography: advancements with deep learning.
Review in Radiology advances, 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.
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis.Radiology advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in artificial intelligence (AI) are transforming medical imaging diagnostics, offering new possibilities for automated pancreatic tumor detection in computed tomography scans. Pancreatic ductal adenocarcinoma continues to be one of the most lethal malignancies, with early detection being critical for improving survival rates. Deep learning models can learn hierarchical feature representations directly from imaging data, enhancing tumor detection accuracy. However, variations in model performance, impaired generalizability, and limited interpretability remain critical barriers to clinical adoption. This article provides a comprehensive overview of deep learning-based pancreatic tumor detection, discussing fundamental concepts, recent advancements, and challenges for clinical adoption. Implementation of deep learning tumor detection models into imaging workflows holds promise for improving early detection rates of pancreatic tumors. Addressing issues of standardization, external validation, and model transparency will be essential to enable the integration of AI into pancreatic cancer screening and diagnostics, ultimately improving early detection and patient outcomes.
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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.