Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2.
Masatoshi MurakamiNao FujimoriKohei NakataMasafumi NakamuraShinichi HashimotoHiroshi KuraharaKazuyoshi NishiharaToshiya AbeShunpei HashigoNaotaka KugiyamaEisuke OzawaKazuhisa OkamotoYusuke IshidaKeiichi OkanoRyo TakakiYutaka ShimamatsuTetsuhide ItoMasami MikiNoriko OzaDaisuke YamaguchiHirofumi YamamotoHironobu TakedomiKen KawabeTetsuro AkashiKoichi MiyaharaJiro OhuchidaYasuhiro OguraYohei NakashimaToshiharu UekiKousei IshigamiHironobu UmakoshiKeijiro UedaTakamasa OonoYoshihiro OgawaPublished in: Journal of gastroenterology (2023)
Our study revealed the characteristics of resected PanNENs in real-world clinical practice. Machine learning techniques can be powerful analytical tools that provide new insights into the relationship between the Ki-67 index or tumor size and recurrence.