Vision Transformer-Based Multilabel Survival Prediction for Oropharynx Cancer After Radiation Therapy.

ABSTRACT:

Purpose

A reliable and comprehensive cancer prognosis model for oropharyngeal cancer (OPC) could better assist in personalizing treatment. In this work, we developed a vision transformer-based (ViT-based) multilabel model with multimodal input to learn complementary information from available pretreatment data and predict multiple associated endpoints for radiation therapy for patients with OPC.

Methods and materials

A publicly available data set of 512 patients with OPC was used for both model training and evaluation. Planning computed tomography images, primary gross tumor volume masks, and 16 clinical variables representing patient demographics, diagnosis, and treatment were used as inputs. To extract deep image features with global attention, we used a ViT module. Clinic

SUBMITTER: Chen M 

PROVIDER: S-EPMC11161220 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature