Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort
| Title: | Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort |
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| Authors: | Silvia Giordano, Angela Marika Siciliano, Matteo Donadon, Cristiana Soldani, Barbara Franceschini, Ana Lleo, Luca Di Tommaso, Matteo Cimino, Guido Torzilli, Hidekazu Saiki, Hiroki Nakajima, Sen Takeda, Enrico Davoli |
| Source: | Applied Sciences, Vol 12, Iss 9, p 4244 (2022) |
| Publisher Information: | MDPI AG, 2022. |
| Publication Year: | 2022 |
| Collection: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| Subject Terms: | clinical diagnosis, tumor diagnosis, hepatocellular carcinoma (HCC), Probe Electrospray Ionization Mass Spectrometry (PESI-MS), Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| More Details: | Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible to obtain detailed molecular information from tissue specimens in real-time, with minimal sample pre-treatment. Probe Electro Spray Ionization MS (PESI-MS), combined with artificial intelligence (AI), has demonstrated its effectiveness in distinguishing liver cancer tissues from healthy tissues in a large Italian population group. As the MS profile can reflect the patient’s ethnicity, dietary habits, or particular operating room procedures, the AI algorithm must be well trained to distinguish different groups. We used a large dataset composed of liver tumor and healthy specimens, from the Italian and Japanese populations, to develop a versatile algorithm free from ethnic bias. The system can classify tissues with discrepancies |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/12/9/4244; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app12094244 |
| Access URL: | https://doaj.org/article/07451e8885144565830d82948c3bc860 |
| Accession Number: | edsdoj.07451e8885144565830d82948c3bc860 |
| Database: | Directory of Open Access Journals |
| ISSN: | 20763417 |
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| DOI: | 10.3390/app12094244 |
| Published in: | Applied Sciences |
| Language: | English |