Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence
| Autor: | Luca Di Tommaso, Matteo Donadon, Roberta Pastorelli, Enrico Davoli, Hiroki Nakajima, Laura Brunelli, Matteo Cimino, Hidekazu Saiki, Guido Torzilli, Sen Takeda, Cristiana Soldani, Barbara Franceschini, Kentaro Yoshimura, Silvia Giordano, Ana Lleo |
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| Rok vydání: | 2020 |
| Předmět: |
Liver Cancer
Carcinoma Hepatocellular Tumor resection Diagnostic accuracy Negative margin Mass spectrometry Mass Spectrometry 03 medical and health sciences 0302 clinical medicine medicine Humans liver surgery resection margins liver tumours Hepatology business.industry Liver Neoplasms Liver tumours Hepatic tumour artificial intelligence medicine.disease Bile Ducts Intrahepatic Bile Duct Neoplasms 030220 oncology & carcinogenesis Hepatocellular carcinoma Original Article 030211 gastroenterology & hepatology Artificial intelligence business Liver cancer |
| Zdroj: | Liver International |
| ISSN: | 1478-3231 1478-3223 |
| DOI: | 10.1111/liv.14604 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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