AI Article Synopsis
- Brain metastases (BrM) are a serious complication for cancer patients, occurring when cancer cells spread to the brain from other body parts, and there are limited effective treatments available.
- Due to challenges in accessing patient samples, preclinical models are essential for studying how metastasis develops and responds to therapies, highlighting the need for reliable methods to measure metastatic burden.
- This text describes a new semi-automatic machine-learning method that uses QuPath software to quickly and accurately quantify metastatic burden in mouse brain images while maintaining tissue integrity, making it more efficient and unbiased compared to traditional techniques.
Article Abstract
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.
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| http://dx.doi.org/10.1016/bs.mcb.2024.10.001 | DOI Listing |
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