ArticleThe Journal of clinical endocrinology and metabolism2024
Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.
Article in The Journal of clinical endocrinology and metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Predicting the Unpredictable: AI-Driven Prognosis in Pancreatic Neuroendocrine Neoplasms.Cancers · 2026Review
- NDC80 promotes epithelial to mesenchymal transition of esophageal squamous cell carcinoma through macrophages polarization and PI3K/AKT pathway activation.European journal of medical research · 2025Article
- The Landmark Series: Surgical Management of Functioning and Non-Functioning Pancreatic Neuroendocrine Tumors.Annals of surgical oncology · 2025Review
- Artificial Intelligence for Prognosis of Gastro-Entero-Pancreatic Neuroendocrine Neoplasms.Cancers · 2025Review
- Developing a Predictive Model for Metastatic Potential in Pancreatic Neuroendocrine Tumor.The Journal of clinical endocrinology and metabolism · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
contextPancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive.
objectiveUse machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature.
methodsRNA-sequencing data were analyzed from 95 surgically resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically available mRNA quantification platform.
resultsGene expression analysis identified concordant differentially expressed genes between the 2 cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5-93.8%) and specificity (78.1-96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median area under the receiving operating characteristic curve of 0.886.
conclusionWe identified and validated an 8-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative vs nonoperative management.
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.