Development of an optimized machine learning approach to enhance brain metastatic burden assessment in preclinical models.
Rappaport, J.; Chen, Q.; McGuire, T.; Daugherty-Lopes, A.; Goldszmid, R. S.
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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 currently lacks effective therapies. Due to the limited access to patient samples, preclinical models remain a valuable tool for studying metastasis development, progression, and response to therapy. Thus, reliable methods for quantifying 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 utilizes the open-source, user-friendly image analysis software QuPath. The method is fast, reproducible, unbiased, and provides access to data points not always obtainable with other existing strategies.
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