ArticleThe British journal of surgery2020
Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry.
Article in The British journal of surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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.
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Who cites it
16 citing papers in PubMed, 37 citations in OpenAlex.
- Serum mass spectral fingerprints with machine learning for early discrimination of infectious pneumonia in the emergency department.BMC infectious diseases · 2026Observational
- Machine learning approach in canine mammary tumour classification using rapid evaporative ionization mass spectrometry.Analytical and bioanalytical chemistry · 2025Article
- Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls.International journal of molecular sciences · 2024Article
- Ambient Mass Spectrometry and Machine Learning-Based Diagnosis System for Acute Coronary Syndrome.Mass spectrometry (Tokyo, Japan) · 2024Article
- New Diagnostic Modality Combining Mass Spectrometry and Machine Learning for the Discrimination of Malignant Intraductal Papillary Mucinous Neoplasms.Annals of surgical oncology · 2023Article
- Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology.International journal of molecular sciences · 2022Article
- Article
- Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling.Molecules (Basel, Switzerland) · 2022Article
- Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor.Thoracic cancer · 2022Article
- New strategy for evaluating pancreatic tissue specimens from endoscopic ultrasound-guided fine needle aspiration and surgery.JGH open : an open access journal of gastroenterology and hepatology · 2021Article
- Diagnostic significance of plasma lipid markers and machine learning-based algorithm for gastric cancer.Oncology letters · 2021Article
- Applications of ambient ionization mass spectrometry in 2020: An annual review.Analytical science advances · 2021Review
- A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.BMC cancer · 2021Article
- High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning.Journal of Cancer · 2021Article
- The Function and Mechanism of Lipid Molecules and Their Roles in The Diagnosis and Prognosis of Breast Cancer.Molecules (Basel, Switzerland) · 2020Review
- Rapid, Sensitive, and Versatile: A 5-Year Perspective on Applications of Ambient Ionization Mass Spectrometry in Bioanalytical Science.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Probe electrospray ionization mass spectrometry (PESI-MS) is an ambient ionization-based mass spectrometry method that surpasses the original electrospray ionization technique in features such as the rapidity of analysis, simplicity of the equipment and procedure, and lower cost. This study found that the PESI-MS system with machine learning has the potential to establish a lipid-based diagnosis of breast cancer with higher accuracy, using a simpler approach. Rapid mass spectrometry for breast cancer.
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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.