ArticleLiver international : official journal of the International Association for the Study of the Liver2020
Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence.
Article in Liver international : official journal of the International Association for the Study of the Liver, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 37 citations in OpenAlex.
- Intraoperative Mass Spectrometry in Oncology: Technologies, Clinical Applications, and Challenges.Molecules (Basel, Switzerland) · 2026Review
- Serum mass spectral fingerprints with machine learning for early discrimination of infectious pneumonia in the emergency department.BMC infectious diseases · 2026Observational
- Beyond the convergence of metabolic reprogramming in primary liver cancer: a comprehensive review on energy and lipids metabolism.Frontiers in cell and developmental biology · 2026Review
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- A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.Scientific reports · 2024Article
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy.Cancers · 2023Review
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- Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis.Frontiers in oncology · 2023Article
- Artificial intelligence: Emerging player in the diagnosis and treatment of digestive disease.World journal of gastroenterology · 2022Review
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research.BioMed research international · 2022Article
- Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor.Thoracic cancer · 2022Article
- Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns.Metabolites · 2021Review
- 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
- Applications of ambient ionization mass spectrometry in 2020: An annual review.Analytical science advances · 2021Review
- Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine.Biomedicines · 2021Review
- Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence.Liver international : official journal of the International Association for the Study of the Liver · 2020Article
- Rapid, Sensitive, and Versatile: A 5-Year Perspective on Applications of Ambient Ionization Mass Spectrometry in Bioanalytical Science.Mass spectrometry reviewsReview
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsComplete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra-operative assessment of tumour resection margins are time-consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS-based system for rapid and objective liver cancer identification and classification.
methodsA large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non-tumours) and 96 patients with mass-forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non-tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated.
resultsThe overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non-tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non-tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity.
conclusionsThe herein reported MS-based system, combined with AI, permits liver cancer identification with high accuracy. Its bench-top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision-making process in real-time with the ultimate aim of improving cancer patient cure.
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