Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

ABSTRACT:

Purpose

MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set.

Methods

A total of 109 daily MRI sets acquired on a 1.5T MR-Linac during MRgART for 22 pancreatic cancer patients each treated with five fractions were retrospectively analyzed. For each daily MRI set, OLAR and reposition (No-OLAR) plans were created and the sup

SUBMITTER: Parchur AK 

PROVIDER: S-EPMC9868055 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature