Ontology highlight
ABSTRACT:
SUBMITTER: Yi X
PROVIDER: S-EPMC6171020 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
Yi Xiaoping X Guan Xiao X Chen Chen C Zhang Youming Y Zhang Zhe Z Li Minghao M Liu Peihua P Yu Anze A Long Xueying X Liu Longfei L Chen Bihong T BT Zee Chishing C
Journal of Cancer 20180908 19
<b>Objective:</b> To evaluate the feasibility and accuracy of machine learning based texture analysis of unenhanced CT images in differentiating subclinical pheochromocytoma (sPHEO) from lipid-poor adenoma (LPA) in adrenal incidentaloma (AI). <b>Methods:</b> Seventy-nine patients with 80 LPA and 29 patients with 30 sPHEO were included in the study. Texture parameters were derived using imaging software (MaZda). Thirty texture features were selected and LPA was performed for the features selected ...[more]