A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information

TL;DR

This paper introduces a large, diverse dataset of brain metastasis MRI images with detailed 3D segmentations and clinical data, aiming to enhance AI model training and improve generalizability in clinical settings.

Contribution

The creation of a comprehensive, annotated brain metastasis dataset with 200 patients, including multiple MRI modalities and detailed segmentations, facilitating better AI model development.

Findings

Dataset contains 975 lesions, many sub-centimeter in size.

Includes multiple MRI sequences and clinical features.

Supports improved AI model training and validation.

Abstract

Resection and whole brain radiotherapy (WBRT) are the standards of care for the treatment of patients with brain metastases (BM) but are often associated with cognitive side effects. Stereotactic radiosurgery (SRS) involves a more targeted treatment approach and has been shown to avoid the side effects associated with WBRT. However, SRS requires precise identification and delineation of BM. While many AI algorithms have been developed for this purpose, their clinical adoption has been limited due to poor model performance in the clinical setting. Major reasons for non-generalizable algorithms are the limitations in the datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models to improve generalizability. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and whole tumor (including peritumoral edema) 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging feature information. We used a streamlined approach to database-building leveraging a PACS-integrated segmentation workflow.