Development and validation of a deep transfer learning-based multivariable survival model to predict overall survival in lung cancer.

ABSTRACT:

Background

Numerous deep learning-based survival models are being developed for various diseases, but those that incorporate both deep learning and transfer learning are scarce. Deep learning-based models may not perform optimally in real-world populations due to variations in variables and characteristics. Transfer learning, on the other hand, enables a model developed for one domain to be adapted for a related domain. Our objective was to integrate deep learning and transfer learning to create a multivariable survival model for lung cancer.

Methods

We collected data from 601,480 lung cancer patients in the Surveillance, Epidemiology, and End Results (SEER) database and 4,512 lung cancer patients in the First Affiliated Hospital of Guangzhou Medical University (GYFY) databa

SUBMITTER: Zhu F 

PROVIDER: S-EPMC10088002 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature