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bioRxiv · 10.1101/2024.08.21.608131

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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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 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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Rappaport, J., Chen, Q., McGuire, T., Daugherty-Lopes, A., Goldszmid, R. S.. 2024-08-23. Development of an optimized machine learning approach to enhance brain metastatic burden assessment in preclinical models.. https://doi.org/10.1101/2024.08.21.608131

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