A Review on Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics
Date Issued
September 2021
DOI
10.1007/978-3-030-89128-2_24
Abstract
Breast cancer brain metastasis (BCBM) still remains a major clinical challenge. Current systemic treatments are often inadequate while diagnosis involves time-consuming series of neuro-imaging acquisitions and dangerous
invasive biopsies. Automated image analysis systems for the identification, prediction and follow up of BCBM are therefore required. This review discusses the
advancements in the automated MRI brain metastasis (BM) image analysis using
radiomic features based classification. Seven BM segmentation studies, and three
BCBM identification studies were considered eligible. The latter studies were
based on either manual or semi-automated segmentation methods. Almost every
fully automated BM segmentation method presented in the literature, reported a
maximum dice similarity score (DSC) of 84%, but they resulted in a poor BM
segmentation for brain areas less than 5 mm (0.06 ml). The multi-class prediction of BCBM approach, which is more representative for clinical applicability, is
based on imaging features and resulted in an area under the curve (AUC) of 60%.
Therefore, the need still exists for the development of automated image analysis
methods for the identification, follow up and prediction of BCBM. The potential
clinical usage of above methods entails further multi-center studies with comprehensive clinical data and multi-class modeling with vast and varying primary and
metastatic brain tumors.
invasive biopsies. Automated image analysis systems for the identification, prediction and follow up of BCBM are therefore required. This review discusses the
advancements in the automated MRI brain metastasis (BM) image analysis using
radiomic features based classification. Seven BM segmentation studies, and three
BCBM identification studies were considered eligible. The latter studies were
based on either manual or semi-automated segmentation methods. Almost every
fully automated BM segmentation method presented in the literature, reported a
maximum dice similarity score (DSC) of 84%, but they resulted in a poor BM
segmentation for brain areas less than 5 mm (0.06 ml). The multi-class prediction of BCBM approach, which is more representative for clinical applicability, is
based on imaging features and resulted in an area under the curve (AUC) of 60%.
Therefore, the need still exists for the development of automated image analysis
methods for the identification, follow up and prediction of BCBM. The potential
clinical usage of above methods entails further multi-center studies with comprehensive clinical data and multi-class modeling with vast and varying primary and
metastatic brain tumors.