Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test.
Simple Summary: Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help...
| Publicado en: | Cancers Vol. 13; no. 13; pp. 3109 - 3110 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | algorithm pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151318498&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151318498 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jul2021 vid: 13 iid: 13 pid: 97109 pub: MDPI artinfo: ui: 151318498 151318498 151318498 10.3390/cancers13133109 151318498 ppf: 3109 ppct: 1 formats: tig: atl: Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test. aug: au: Mahalingam, Devalingam Chelis, Leonidas Nizamuddin, Imran Lee, Sunyoung S. Kakolyris, Stylianos Halff, Glenn Washburn, Ken Attwood, Kristopher Fahad, Ibnshamsah Grigorieva, Julia Asmellash, Senait Meyer, Krista Oliveira, Carlos Roder, Heinrich Roder, Joanna Iyer, Renuka affil: Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Northwestern University, Chicago, IL 60611, USA sug: subj: Carcinoma, Hepatocellular Diagnosis Cancer Screening Methods Mass Spectrometry Methods Hematologic Tests Human Machine Learning Algorithms alpha Fetoproteins Blood Adult Tumor Markers, Biological Sensitivity and Specificity Proteomics Adult: 19-44 years ab: Simple Summary: Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests. Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as "Cancer" or "No Cancer". Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted. pubtype: Academic Journal doctype: algorithm pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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