A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.
A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.
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DOI：
10.1186/s12885-021-08001-5
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发表时间：
2021-03-10
期刊：
影响因子：
3.8
通讯作者：
Hasegawa K
中科院分区：
文献类型：
--
作者：
Kiritani S;Yoshimura K;Arita J;Kokudo T;Hakoda H;Tanimoto M;Ishizawa T;Akamatsu N;Kaneko J;Takeda S;Hasegawa K
探针电喷雾电离质谱 (PESI-MS) 可以快速可视化通过手术获得的小型组织样本的质谱，与机器学习相结合可区分恶性谱图模式，是一种很有前途的新型诊断工具。本研究旨在评估该装置在结直肠肝转移（CRLM）快速诊断中的实用性。使用回顾性获得的组织进行了一项前瞻性计划的研究。使用 PESI-MS 分析了总共 103 个 CRLM 样本和 80 个从手术提取的标本中切下的非癌肝组织。通过使用逻辑回归（一种机器学习）将 PESI-MS 获得的质谱分为癌症组或非癌症组。接下来，为了确定导致 CRLM 和非癌组织之间差异的确切分子，我们进行了液相色谱-电喷雾电离-MS (LC-ESI-MS)，它可以更详细地可视化样品分子组成。该诊断系统将CRLM与非癌肝实质区分开来，准确率高达99.5%。受试者工作特征曲线下面积达到0.9999。 LC-ESI-MS分析显示CRLM中磷脂酰胆碱和磷脂酰乙醇胺的离子强度高于非癌肝实质中的离子强度（分别P<0.01）。 CRLM 中单不饱和脂肪酸的磷脂比例 (37.2%) 高于非癌肝实质中的 (10.7%; P < 0.01)。 PESI-MS 和机器学习的结合可以高精度区分 CRLM 和非癌组织。属于单不饱和脂肪酸的磷脂导致了 CRLM 和正常实质之间的差异，也可能是 CRLM 的有用诊断生物标志物和治疗靶点。在线版本包含可在 10.1186/s12885-021-08001-5 获取的补充材料。
Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM). A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail. This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P < 0.01, respectively). The proportion of phospholipids categorized as monounsaturated fatty acids was higher in CRLM (37.2%) than in non-cancer liver parenchyma (10.7%; P < 0.01). The combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM. The online version contains supplementary material available at 10.1186/s12885-021-08001-5.
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影响因子：
5.6
作者：
Perrotti F;Rosa C;Cicalini I;Sacchetta P;Del Boccio P;Genovesi D;Pieragostino D
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作者：
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发表时间：
2007-08-01
影响因子：
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影响因子：
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作者：
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