A convolutional neural network for total tumor segmentation in [⁶⁴Cu]Cu-DOTATATE PET/CT of patients with neuroendocrine neoplasms.

ABSTRACT:

Background

Segmentation of neuroendocrine neoplasms (NENs) in [⁶⁴Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [⁶⁴Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a

SUBMITTER: Carlsen EA 

PROVIDER: S-EPMC9148347 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature