Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.

ABSTRACT:

Purpose/objectives

Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment.

Methods and materials

A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm. Fifty-eight patients with 629 BMs were assessed for treatment

SUBMITTER: Son S 

PROVIDER: S-EPMC10823345 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature