ArticleAmerican journal of translational research2020
Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.
Article in American journal of translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- Feature Down-Selection to Improve Supervised Classification by Machine Learning on Mass Spectrometry Imaging Data.Molecules (Basel, Switzerland) · 2026Article
- Serum mass spectral fingerprints with machine learning for early discrimination of infectious pneumonia in the emergency department.BMC infectious diseases · 2026Observational
- PanMETAI - a high performance tabular foundation model for accurate pancreatic cancer diagnosis via NMR metabolomics.Nature communications · 2026Article
- Automated sample preparation for electrospray ionization mass spectrometry based on CLOCK-controlled autonomous centrifugal microfluidics.Biomedical microdevices · 2024Article
- 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
- Non-Invasive Biomarkers for Earlier Detection of Pancreatic Cancer-A Comprehensive Review.Cancers · 2021Review
- Applications of ambient ionization mass spectrometry in 2020: An annual review.Analytical science advances · 2021Review
- Endoscopic Ultrasound-Guided Sampling for Personalized Pancreatic Cancer Treatment.Diagnostics (Basel, Switzerland) · 2021Review
- High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning.Journal of Cancer · 2021Article
- Application of Mass Spectrometry in Pancreatic Cancer Translational Research.Frontiers in oncology · 2021Review
- 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
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations.
Indexed as
Identifiers
32051746PMC7013221W3008769549What OpenQuestion holds
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.