Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

ABSTRACT:

Purpose

Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.

Methods

A total of 886 LA-NPC patients acquired from two medical centers were enrolled including clinical data, [¹⁸F]FDG PET/CT images, and follow-up of progression-free survival (PFS). We adopted a deep multi-task survival model (DeepMTS) to jointly perform prognostic prediction (DeepMTS-Score) and tumor segmentation from FDG-PET/CT images. The Dee

SUBMITTER: Gu B 

PROVIDER: S-EPMC10611876 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature