Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features.

ABSTRACT:

Background

A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in terms of tumor local failure is crucial for these patients and can facilitate treatment adjustments or allow for early salvage therapies.

Purpose

In this work, a novel deep learning architecture is introduced to predict the outcome of local control/failure in brain metastasis treated with stereotactic radiation therapy using treatment-planning magnetic resonance imaging (MRI) and standard clinical attributes.

Methods

At the core of the proposed architecture is an InceptionResentV2 network to extract distinct features from eac

SUBMITTER: Jalalifar SA 

PROVIDER: S-EPMC10083982 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature