論文

査読有り 国際誌
2020年12月

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
  • Silvia Giordano
  • Sen Takeda
  • Matteo Donadon
  • Hidekazu Saiki
  • Laura Brunelli
  • Roberta Pastorelli
  • Matteo Cimino
  • Cristiana Soldani
  • Barbara Franceschini
  • Luca Di Tommaso
  • Ana Lleo
  • Kentaro Yoshimura
  • Hiroki Nakajima
  • Guido Torzilli
  • Enrico Davoli
  • 全て表示

40
12
開始ページ
3117
終了ページ
3124
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1111/liv.14604
出版者・発行元
Wiley

BACKGROUND AND AIMS: Complete 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: The 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.

リンク情報
DOI
https://doi.org/10.1111/liv.14604
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/32662575
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754124
URL
https://onlinelibrary.wiley.com/doi/pdf/10.1111/liv.14604
URL
https://onlinelibrary.wiley.com/doi/full-xml/10.1111/liv.14604
ID情報
  • DOI : 10.1111/liv.14604
  • ISSN : 1478-3223
  • eISSN : 1478-3231
  • PubMed ID : 32662575
  • PubMed Central 記事ID : PMC7754124

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