ArticleScientific reports2019
Automated detection and quantification of breast cancer brain metastases in an animal model using democratized machine learning tools.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Brain metastasis detection using machine learning: a systematic review and meta-analysis.Neuro-oncology · 2021Pooled it
- Prophylactic and therapeutic CSC-based vaccination reduced tumor growth, metastasis and enhanced survival in mouse model of breast cancer.Breast cancer research : BCR · 2026Article
- MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.Pigment cell & melanoma research · 2025Article
- Article
- Grading of lung adenocarcinomas with simultaneous segmentation by artificial intelligence (GLASS-AI).NPJ precision oncology · 2023Article
- "Democratizing" artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term.Frontiers in genetics · 2022Article
- Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum.Journal of pathology informatics · 2022Article
- Echinacoside alleviates acetaminophen-induced liver injury by attenuating oxidative stress and inflammatory cytokines in mice.Journal of applied biomedicine · 2021Article
- A data-driven methodology to discover similarities between cocaine samples.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in digital whole-slide imaging and machine learning (ML) provide new opportunities for automated examination and quantification of histopathological slides to support pathologists and biologists. However, implementation of ML tools often requires advanced skills in computer science that may not be immediately available in the traditional wet-lab environment. Here, we propose a simple and accessible workflow to automate detection and quantification of brain epithelial metastases on digitized histological slides. We leverage 100 Hematoxylin & Eosin (H&E)-stained whole slide images (WSIs) from 25 Balb/c mice with various level of brain metastatic tumor burden. A supervised training of the Trainable Weka Segmentation (TWS) from Fiji was achieved from annotated WSIs. Upon comparison with manually drawn regions, it is apparent that the algorithm learned to identify and segment cancer cell-specific nuclei and normal brain tissue. Our approach resulted in a robust and highly concordant correlation between automated metastases quantification of brain metastases and manual human assessment (R
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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.