Silvia Giordano1, Sen Takeda2, Matteo Donadon3,4, Hidekazu Saiki5, Laura Brunelli1, Roberta Pastorelli1, Matteo Cimino3,4, Cristiana Soldani3, Barbara Franceschini3, Luca Di Tommaso6, Ana Lleo4,7, Kentaro Yoshimura2, Hiroki Nakajima5, Guido Torzilli3,4, Enrico Davoli1. 1. Mass Spectrometry Laboratory, Environmental Health Sciences Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy. 2. Department of Anatomy and Cell Biology, University of Yamanashi Faculty of Medicine, Chuo, Japan. 3. Department of Hepatobiliary and General Surgery, Humanitas University, Humanitas Clinical and Research Center - IRCCS, Milan, Italy. 4. Laboratory of Hepatobiliary Immunopathology, Humanitas Clinical and Research Center - IRCCS, Milan, Italy. 5. Shimadzu Corporation, Kyoto, Japan. 6. Department of Pathology, Humanitas University, Humanitas Clinical and Research Center - IRCCS, Milan, Italy. 7. Department of Internal Medicine, Humanitas University, Humanitas Clinical and Research Center - IRCCS, Milan, Italy.
Abstract
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.
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.
Authors: Karl-Christian Schäfer; Júlia Dénes; Katalin Albrecht; Tamás Szaniszló; Júlia Balog; Réka Skoumal; Mária Katona; Miklós Tóth; Lajos Balogh; Zoltán Takáts Journal: Angew Chem Int Ed Engl Date: 2009 Impact factor: 15.336
Authors: Valentina Pirro; Clint M Alfaro; Alan K Jarmusch; Eyas M Hattab; Aaron A Cohen-Gadol; R Graham Cooks Journal: Proc Natl Acad Sci U S A Date: 2017-06-12 Impact factor: 11.205
Authors: Manuel Schlageter; Luigi Maria Terracciano; Salvatore D'Angelo; Paolo Sorrentino Journal: World J Gastroenterol Date: 2014-11-21 Impact factor: 5.742
Authors: Júlia Balog; László Sasi-Szabó; James Kinross; Matthew R Lewis; Laura J Muirhead; Kirill Veselkov; Reza Mirnezami; Balázs Dezső; László Damjanovich; Ara Darzi; Jeremy K Nicholson; Zoltán Takáts Journal: Sci Transl Med Date: 2013-07-17 Impact factor: 17.956