Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

ABSTRACT:

Purpose

To develop a method of biologically guided deep learning for post-radiation ¹⁸FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information.

Methods

Based on the classic reaction-diffusion mechanism, a novel biological model was proposed using a partial differential equation that incorporates spatial radiation dose distribution as a patient-specific treatment information variable. A 7-layer encoder-decoder-based convolutional neural network (CNN) was designed and trained to learn the proposed biological model. As such, the model could generate post-radiation ¹⁸FDG-PET image outcome predictions with breakdown biological components for enhanced explainability. The proposed method was developed using 64 oropharyng

SUBMITTER: Ji H 

PROVIDER: S-EPMC9135979 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature