ArticleInternational journal of computer assisted radiology and surgery2021
Metabolomics patterns of breast cancer tumors using mass spectrometry imaging.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 14 citations in OpenAlex.
- From morphology to single-cell molecules: high-resolution 3D histology in biomedicine.Molecular cancer · 2025Review
- Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.BMC medical informatics and decision making · 2024Article
- Metabolic heterogeneity in cancer.Nature metabolism · 2024Review
- Review
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
purposeIntraoperative assessment of surgical margins is important for reducing the rate of revisions in breast conserving surgery for palpable malignant tumors. The hypothesis was that metabolomics methods, based on mass spectrometry, could find patterns of relative abundances of molecules that distinguish clusters of benign tissue and cancer in surgical resections.
methodsExcisions from 8 patients were used to acquire 112,317 mass spectrometry signals by desorption electrospray ionization. A process of nonnegative matrix factorization and graph decomposition produced clusters that were approximated as affine spaces. Each signal's distance to the affine space of a cluster was used to visualize the clustering.
resultsThe distance maps were superior to binary clustering in identifying cancer regions. They were particularly effective at finding cancer regions that were discontinuously distributed within benign tissue.
conclusionsDesorption electrospray ionization mass spectrometry, which has been shown to be useful intraoperatively, can acquire signals that distinguish malignant from benign breast tissue in surgically excised tumors. The method may be suitable for real-time surgical decisions based on cancer margins.
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