Early stage NSCLS patients' prognostic prediction with multi-information using transformer and graph neural network model.

ABSTRACT:

Background

We proposed a population graph with Transformer-generated and clinical features for the purpose of predicting overall survival (OS) and recurrence-free survival (RFS) for patients with early stage non-small cell lung carcinomas and to compare this model with traditional models.

Methods

The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient characteristics and nodes representing imaging tumour region characteristics generated by a pretrained Vision Transformer. The model was compared with a TNM model and a ResNet-Graph model. To evaluate the models' performance, the area under the receiver operator characteristic curve (ROC-AUC) was c

SUBMITTER: Lian J 

PROVIDER: S-EPMC9531948 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature