Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases.

ABSTRACT:

Purpose

While deep learning has shown promise for automated radiotherapy planning, its application to the specific scenario of stereotactic radiosurgery (SRS) for brain metastases using fixed-field intensity modulated radiation therapy (IMRT) on a linear accelerator remains limited. This work aimed to develop and verify a deep learning-guided automated planning protocol tailored for this scenario.

Methods

We collected 70 SRS plans for solitary brain metastases, of which 36 cases were for training and 34 for testing. Test cases were derived from two distinct clinical institutions. The envisioned automated planning process comprised (1): clinical dose prediction facilitated by deep-learning algorithms (2); transformation of the forecasted dose into executable plans via voxel-c

SUBMITTER: Pan J 

PROVIDER: S-EPMC10701271 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature