ArticleMethods in cell biology2024
Machine learning approach to assess brain metastatic burden in preclinical models.
Article in Methods in cell biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach.Brain sciences · 2025Article
- IMMUNE AND MOLECULAR CORRELATES OF RESPONSE TO IMMUNOTHERAPY REVEALED BY BRAIN-METASTATIC MELANOMA MODELS.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.