Polpun Boonmak, Suwannee Suraseranivongse, Ngamjit Pattaravit, Suhattaya Boonmak, Tachawan Jirativanont, Tripop Lertbunnaphong, Rajin Arora, Jittiya Watcharotayangul, Intanon Imsuwan, Panithan Kwangwaropas, Borwon Wittayachamnankul
Simulation-based medical education in Thailand: a cross-sectional online national survey
BMC medical education
pancreatic neuroendocrine tumors pnets exhibit wide range behavior localized disease aggressive metastasis comprehensive transcriptomic profile capable differentiating phenotypes remains elusive use machine learning develop predictive models pnet metastatic potential dependent upon transcriptomic signature rnasequencing data analyzed 95 surgicallyresected primary pnets international cohort two cohorts generated equally balanced metastatic pnet composition machine learning used create predictive models distinguishing localized metastatic tumors models validated independent cohort 29 formalinfixed paraffinembedded samples using nanostring ncounter clinicallyavailable mrna quantification platform gene expression analysis identified concordant differentially expressed genes two cohorts gene set enrichment analysis identified additional genes contributed enriched biologic pathways metastatic pnets expression values genes combined additional 7 genes known contribute pnet oncogenesis prognosis including arx pdx1 eight specific genes aurka cdca8 cpb2 myt1l ndc80 pappa2 sfmbt1 zpld1 identified sufficient classify metastatic status high sensitivity 875 938 specificity 781 969 models remained predictive metastatic phenotype using nanostring ncounter independent validation cohort achieving median auroc 0886 identified validated eightgene panel predictive metastatic phenotype pnets detected using clinicallyavailable nanostring ncounter system panel studied prospectively determine utility guiding operative versus nonoperative management