Weakly supervised deep learning for determining the prognostic value of ¹⁸F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

ABSTRACT:

Purpose

To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment ¹⁸F-FDG PET/CT results.

Methods

One hundred and sixty-seven patients with ENKTL who underwent pretreatment ¹⁸F-FDG PET/CT were retrospectively collected. Eighty-four patients were followed up for at least 2 years (training set = 64, test set = 20). A WSDL method was developed to enable the integration of the remaining 83 patients with incomplete/missing follow-up information in the training set. To test generalization, these data were derived from three types of scanners. Prediction similarity index (PSI) was derived from deep

SUBMITTER: Guo R 

PROVIDER: S-EPMC7896833 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature