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Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence
Author(s) -
Giordano Silvia,
Takeda Sen,
Donadon Matteo,
Saiki Hidekazu,
Brunelli Laura,
Pastorelli Roberta,
Cimino Matteo,
Soldani Cristiana,
Franceschini Barbara,
Di Tommaso Luca,
Lleo Ana,
Yoshimura Kentaro,
Nakajima Hiroki,
Torzilli Guido,
Davoli Enrico
Publication year - 2020
Publication title -
liver international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.873
H-Index - 110
eISSN - 1478-3231
pISSN - 1478-3223
DOI - 10.1111/liv.14604
Subject(s) - hepatocellular carcinoma , medicine , support vector machine , artificial intelligence , diagnostic accuracy , random forest , classifier (uml) , liver cancer , machine learning , radiology , computer science
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.