Probe electrospray ionization mass spectrometry‐based rapid diagnosis of liver tumors

• Published in: Journal of gastroenterology and hepatology Vol. 37; no. 11; pp. 2182 - 2188
• Main Authors: Hakoda, Hiroyuki, Kiritani, Sho, Kokudo, Takashi, Yoshimura, Kentaro, Iwano, Tomohiko, Tanimoto, Meguri, Ishizawa, Takeaki, Arita, Junichi, Akamatsu, Nobuhisa, Kaneko, Junichi, Takeda, Sen, Hasegawa, Kiyoshi
• Format: Journal Article
• Language: English
• Published: Richmond Wiley Subscription Services, Inc 01.11.2022
• Subjects: Differential diagnosis; Hepatocellular carcinoma; Ions; Learning algorithms; Liver cancer; Machine learning; Mass spectrometry; Mass spectroscopy; Metastases; Scientific imaging; Tumors
• ISSN: 0815-9319; 1440-1746
• DOI: 10.1111/jgh.15976

Background and Aim Prompt differential diagnosis of liver tumors is clinically important and sometimes difficult. A new diagnostic device that combines probe electrospray ionization‐mass spectrometry (PESI‐MS) and machine learning may help provide the differential diagnosis of liver tumors. Methods We evaluated the diagnostic accuracy of this new PESI‐MS device using tissues obtained and stored from previous surgically resected specimens. The following cancer tissues (with collection dates): hepatocellular carcinoma (HCC, 2016–2019), intrahepatic cholangiocellular carcinoma (ICC, 2014–2019), and colorectal liver metastasis (CRLM, 2014–2019) from patients who underwent hepatic resection were considered for use in this study. Non‐cancerous liver tissues (NL) taken from CRLM cases were also incorporated into the analysis. Each mass spectrum provided by PESI‐MS was tested using support vector machine, a type of machine learning, to evaluate the discriminatory ability of the device. Results In this study, we used samples from 91 of 139 patients with HCC, all 24 ICC samples, and 103 of 202 CRLM samples; 80 NL from CRLM cases were also used. Each mass spectrum was obtained by PESI‐MS in a few minutes and was evaluated by machine learning. The sensitivity, specificity, and diagnostic accuracy of the PESI‐MS device for discriminating HCC, ICC, and CRLM from among a mix of all three tumors and from NL were 98.9%, 98.1%, and 98.3%; 87.5%, 93.1%, and 92.6%; and 99.0%, 97.9%, and 98.3%, respectively. Conclusion This study demonstrated that PESI‐MS and machine learning could discriminate liver tumors accurately and rapidly.