Brain metastases (BrM) occur when malignant cells spread from a primary tumor located in other parts of the body to the brain. BrM is a deadly complication for cancer patients and severely lacks effective therapies. Due to the limited access to patient samples, preclinical models remain a very valuable tool for studying metastasis development, progression, and response to therapy. Thus, reliable methods to assess metastatic burden in these models are crucial. Here we describe step by step a new semi-automatic machine-learning approach to quantify metastatic burden on mouse whole-brain stereomicroscope images while preserving tissue integrity. This protocol uses the open-source and user-friendly image analysis software QuPath. The method is fast, reproducible, unbiased, and gives access to data points not always accessible with other existing strategies.
Keywords: Brain metastasis; Digital image analysis; Machine learning; Melanoma; Metastasis detection; Metastatic burden.
Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.