Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

ABSTRACT:

Purpose

To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS).

Methods and materials

A total of 114 brain metastases (BMs) from 82 patients were evaluated, including 26 BMs that developed biopsy-confirmed local failure post-SRS. The SRS spatial dose distribution (D_map) of each BM was registered to the planning contrast-enhanced T1 (T1-CE) magnetic resonance imaging (MRI). Axial slices of the D_map, T1-CE, and planning target volume (PTV) segmentation (PTV_seg) intersecting the BM center were extracted within a fixed field of view determined by the 60% isodose volume in D_map. A spherical projection was implemented to transform plana

SUBMITTER: Zhao J 

PROVIDER: S-EPMC12282658 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature