ReviewCancers2025
Artificial Intelligence for Prognosis of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) represent a challenging disease. Their large heterogeneity limits the possibility of providing accurate risk assessments or standardizing the most effective therapies for these patients. In recent years, artificial intelligence (AI), and in particular machine learning approaches, have shown real promise in addressing these complexities. By analyzing large volumes of clinical, imaging, and pathological data, AI-based tools can significantly improve the accuracy of survival predictions and guide more tailored treatment strategies. In this narrative review, we examine the potential applications of AI to develop effective prognostic models in GEP-NENs, and how these models may help clinicians in predicting survival and optimizing patient management. While early results are encouraging, important limitations remain, since available data stem from small, retrospective datasets, sometimes lacking external validation, and concerns around transparency and ethics still represent an open issue. Addressing these gaps will be key to moving from research applications to practical tools that can support everyday clinical decision-making.
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.