An image-based deep learning framework for individualizing radiotherapy dose.

ABSTRACT:

Background

Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose.

Methods

We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We valid

SUBMITTER: Lou B 

PROVIDER: S-EPMC6708276 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature