ArticleProceedings of the National Academy of Sciences of the United States of America2015
MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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.
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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.
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Who cites it
33 citing papers in PubMed, 81 citations in OpenAlex.
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- The evolution and application of multi-omic analysis for pituitary neuroendocrine tumors.Frontiers in medicine · 2025Review
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- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies.International journal of molecular sciences · 2022Article
- Applications of spatially resolved omics in the field of endocrine tumors.Frontiers in endocrinology · 2022Review
- Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial.Journal of mass spectrometry and advances in the clinical lab · 2022Article
- Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis.Metabolites · 2021Review
- Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics.Clinics in laboratory medicine · 2021Review
- Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues.Frontiers in chemistry · 2021Review
- Biological Applications for LC-MS-Based Proteomics.Advances in experimental medicine and biology · 2021Article
- Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy.PLoS computational biology · 2020Article
- Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.Bioinformatics (Oxford, England) · 2020Article
- Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.Pituitary · 2020Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making.
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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.