基于探针电喷雾离子化质谱的肝肿瘤快速诊断。

Probe electrospray ionization mass spectrometry-based rapid diagnosis of liver tumors.

机构信息

Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Department of Anatomy and Cell Biology, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Yamanashi, Japan.

出版信息

J Gastroenterol Hepatol. 2022 Nov;37(11):2182-2188. doi: 10.1111/jgh.15976. Epub 2022 Aug 16.

DOI:10.1111/jgh.15976

PMID:35945170

Abstract

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.

摘要

背景与目的

快速鉴别肝脏肿瘤对临床具有重要意义，有时也颇具难度。一种新的诊断设备，结合探针电喷雾电离-质谱（PESI-MS）和机器学习，或有助于提供肝脏肿瘤的鉴别诊断。

方法

我们评估了该新型 PESI-MS 设备的诊断准确性，使用的是先前手术切除标本中获取和储存的组织。本研究纳入了以下癌症组织（收集日期）：肝细胞癌（HCC，2016-2019 年）、肝内胆管细胞癌（ICC，2014-2019 年）和结直肠癌肝转移（CRLM，2014-2019 年），来自接受肝切除术的患者。同时也纳入了取自 CRLM 病例的非癌性肝组织（NL）。使用支持向量机（一种机器学习类型）对 PESI-MS 提供的每个质谱进行测试，以评估该设备的鉴别能力。

结果

在这项研究中，我们使用了 91 例 HCC 中的 139 例、24 例 ICC 中的全部 24 例和 202 例 CRLM 中的 103 例样本；同时还使用了 80 例来自 CRLM 病例的 NL。每个质谱是通过 PESI-MS 在几分钟内获得的，并通过机器学习进行评估。PESI-MS 设备用于区分 HCC、ICC 和 CRLM 与三种肿瘤混合以及 NL 的灵敏度、特异性和诊断准确性分别为 98.9%、98.1%和 98.3%；87.5%、93.1%和 92.6%；99.0%、97.9%和 98.3%。