Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors.

ABSTRACT:

Purpose

To create and investigate a novel, clinical decision-support system using machine learning (ML).

Methods and materials

The ML model was developed based on 79 radiotherapy plans of brain tumor patients that were prescribed a total dose of 60 Gy delivered with volumetric-modulated arc therapy (VMAT). Structures considered for analysis included planning target volume (PTV), brainstem, cochleae, and optic chiasm. The model aimed to classify the target variable that included class-0 corresponding to plans for which the PTV treatment planning objective was met and class-1 that was associated with plans for which the PTV objective was not met due to the priority trade-off to meet one or more organs-at-risk constraints. Several models were evaluated using double-nested cross

SUBMITTER: Siciarz P 

PROVIDER: S-EPMC8487981 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature