ReviewCancers2026
Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The clinical management of Pancreatic Neuroendocrine Neoplasms (Pan-NENs) is complicated by the disease's intrinsic variability, which creates significant hurdles for accurate risk profiling and the standardization of treatment protocols. Recently, Artificial Intelligence (AI) has offered a promising avenue to address these challenges. By integrating and processing high-dimensional multimodal datasets (encompassing clinical history, radiomics, and pathology), these computational tools can refine survival forecasts and support the development of personalized medicine. However, the transition from experimental success to routine clinical use is currently obstructed by reliance on limited, retrospective cohorts that lack external validation, alongside unresolved concerns regarding algorithmic transparency and ethical governance. This review evaluates the current landscape of AI-driven prognostic modeling for Pan-NENs and critically examines the pathway towards their reliable integration into clinical practice.
Indexed as
Identifiers
What Socratic 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 Socratic graph.