Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology.

ABSTRACT:

Purpose

Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans.

Methods and materials

Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector mac

SUBMITTER: Pillai M 

PROVIDER: S-EPMC10185740 | biostudies-literature | 2023 Nov-Dec

REPOSITORIES: biostudies-literature