A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-03-10

AUTHORS

Sho Kiritani, Kentaro Yoshimura, Junichi Arita, Takashi Kokudo, Hiroyuki Hakoda, Meguri Tanimoto, Takeaki Ishizawa, Nobuhisa Akamatsu, Junichi Kaneko, Sen Takeda, Kiyoshi Hasegawa

ABSTRACT

BackgroundProbe 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).MethodsA 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.ResultsThis 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).ConclusionThe 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. More... »

PAGES

262

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12885-021-08001-5

DOI

http://dx.doi.org/10.1186/s12885-021-08001-5

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PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33691644


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26 schema:description BackgroundProbe 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).MethodsA 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.ResultsThis 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).ConclusionThe 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.
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32 schema:keywords LC-ESI
33 MS analysis
34 PESI-MS
35 accuracy
36 accuracy rate
37 acid
38 ambient mass spectrometry
39 analysis
40 area
41 biomarkers
42 cancer
43 characteristic curve
44 colorectal liver metastases
45 combination
46 composition
47 curves
48 detail
49 devices
50 diagnosis
51 diagnostic biomarkers
52 diagnostic system
53 diagnostic tool
54 differences
55 exact molecules
56 fatty acids
57 group
58 high accuracy
59 higher ion intensities
60 intensity
61 ion intensities
62 ionization MS
63 ionization mass spectrometry
64 kind
65 kind of machine
66 learning
67 liquid chromatography-electrospray ionization-MS
68 liver metastases
69 liver parenchyma
70 liver tissue
71 logistic regression
72 machine
73 machine learning
74 mass spectra
75 mass spectrometry
76 metastasis
77 molecular composition
78 molecules
79 more detail
80 non-cancer group
81 non-cancer liver tissues
82 non-cancer tissues
83 non-cancerous tissues
84 normal parenchyma
85 novel diagnostic tool
86 parenchyma
87 patterns
88 phosphatidylcholine
89 phosphatidylethanolamine
90 phospholipids
91 present study
92 proportion
93 proportion of phospholipids
94 rapid diagnosis
95 rapid diagnostic system
96 rate
97 receiver
98 regression
99 samples
100 specimens
101 spectra
102 spectrometry
103 spectrum pattern
104 study
105 system
106 target
107 therapeutic target
108 tissue
109 tissue samples
110 tool
111 total
112 useful diagnostic biomarker
113 utility
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