Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.

ABSTRACT:

Objective

To identify delivery error type and predict associated error magnitude by image-based features using machine learning (ML).

Methods

In this study, a total of 40 thoracic plans (including 208 beams) were selected, and four error types with different magnitudes were introduced into the original plans, including 1) collimator misalignment (COLL), 2) monitor unit (MU) variation, 3) systematic multileaf collimator misalignment (MLCS), and 4) random MLC misalignment (MLCR). These dose distributions of portal dose predictions for the original plans were defined as the reference dose distributions (RDD), while those for the error-introduced plans were defined as the error-introduced dose distributions (EDD). Both distributions were calculated for all beams with portal dose

SUBMITTER: Huang Y 

PROVIDER: S-EPMC10133379 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature