ArticleNPJ digital medicine2025
Deep learning CT model for stratified diagnosis of pancreatic cystic neoplasms: multicenter development, validation, and real-world clinical impact.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses 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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence versus conventional imaging for differentiating G2/G3 from G1 pancreatic neuroendocrine tumors: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- Deep learning models for pancreatic cancer detection on CT: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Radiomics and artificial intelligence in pancreatic cyst characterization: future or fiction?Abdominal radiology (New York) · 2026Review
- An extrinsic enzyme-activatable fluorescent probe for high-contrast tumor imaging.Chemical science · 2026Article
- From Bench to Bedside: The Path Toward Real-World Translation for Artificial Intelligence in Pancreatic Cancer Detection.Korean journal of radiology · 2026Review
- Factors Associated with Stage at Diagnosis in Pancreatic Cancer: Implications for Precision Screening and Early Detection.Biomedicines · 2026Review
- Pancreatic Cystic Lesions: From Basic Knowledge to Recent Guidelines.Journal of clinical medicine · 2026Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Pancreatic cystic neoplasms (PCN) are critical precursors for early pancreatic cancer detection, yet current diagnostic methods lack accuracy and consistency. This multicenter study developed and validated an artificial intelligence (AI)-powered CT model (PCN-AI) for improved assessment. Using contrast-enhanced CT images from 1835 patients, PCN-AI extracted 63 quantitative features to classify PCN subtypes through four hierarchical tasks. A multi-reader, multi-case (MRMC) study demonstrated that AI assistance significantly improved radiologists' diagnostic accuracy (AUC: 0.786 to 0.845; p < 0.05) and reduced interpretation time by 23.7% (5.28 vs. 4.03 minutes/case). Radiologists accepted AI recommendations in 87.14% of cases. In a prospective real-world cohort, PCN-AI outperformed radiologist double-reading, providing actionable diagnostic benefits to 45.45% of patients (5/11) by correctly identifying missed malignant PCN cases, enabling timely intervention, and simultaneously reducing clinical workload by 39.3%. PCN-AI achieved robust performance across tasks (AUCs: 0.845-0.988), demonstrating its potential to enhance early detection, precision management, and diagnostic efficiency in clinical practice.
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.