Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Background

An intraductal papillary mucinous neoplasm (IPMN) is a pancreatic tumor with malignant potential. Although we anticipate a sensitive method to diagnose the malignant conversion of IPMN, an effective strategy has not yet been established. The combination of probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning provides a promising solution for this purpose.

Methods

We prospectively analyzed 42 serum samples obtained from IPMN patients who underwent pancreatic resection between 2020 and 2021. Based on the postoperative pathological diagnosis, patients were classified into two groups: IPMN-low grade dysplasia (n = 17) and advanced-IPMN (n = 25). Serum samples were analyzed by PESI-MS, and the obtained mass spectral data were converted into continuous variables. These variables were used to discriminate advanced-IPMN from IPMN-low grade dysplasia by partial least square regression or support vector machine analysis. The areas under receiver operating characteristics curves were obtained to visualize the difference between the two groups.

Results

Partial least square regression successfully discriminated the two disease classes. From another standpoint, we selected 130 parameters from the entire dataset by PESI-MS, which were fed into the support vector machine. The diagnostic accuracy was 88.1%, and the area under the receiver operating characteristics curve was 0.924 by this method. Approximately 10 min were required to perform each method.

Conclusion

PESI-MS combined with machine learning is an easy-to-use tool with the advantage of rapid on-site analysis. Here, we show the great potential of our system to diagnose the malignant conversion of IPMN, which would be a promising diagnostic tool in clinical settings.

DOI： 10.1245/s10434-022-13012-y

PubMed

Other Link： https://link.springer.com/article/10.1245/s10434-022-13012-y/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1111/jgh.15976.

PubMed

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：IVYSPRING  

Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed. Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm. Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control. Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.

DOI： 10.7150/jca.63244

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Background and Aim: Preoperative histological evaluation of pancreatic neoplasms is important for guiding the resection strategy and preventing postoperative adverse events. However, conventional endoscopic methods have technical limitations that reduce the accuracy of the histopathological examination. Probe electrospray ionization mass spectrometry (PESI-MS) may be a useful technique for rapidly evaluating small specimens. Methods: This single-center prospective study included patients with pancreatic neoplasms between October 2018 and December 2019. Pancreatic ductal adenocarcinoma (PDAC) specimens were obtained via endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) and non-neoplastic tissue was obtained via surgery. Specimens were subjected to PESI-MS and the mass spectra were analyzed using partial least squares regression-discriminant analysis. Results: The study included 40 patients with 20 nonneoplastic specimens and 19 PDAC specimens (1 case of neuroendocrine carcinoma was omitted). All nonneoplastic specimens were sufficient for PESI-MS analysis, although only 7 of 19 PDAC specimens were sufficient for PESI-MS analysis because of poor sample quality or insufficient quantity (<1 mg). Among the 27 analyzed cases, the mass spectra clearly differentiated between the PDAC and nonneoplastic specimens. Conclusions: This study revealed that PESI-MS could differentiate between PDAC and nonneoplastic specimens, even in cases where EUS-FNA produced very small specimens.

DOI： 10.1002/jgh3.12617

PubMed

Language：English   Publishing type：Research paper (scientific journal)  

Biomarkers may be of value for the early detection of gastric cancer (GC) and the preoperative identification of tumor characteristics to guide treatment strategies. The present study analyzed the expression levels of phospholipids in plasma from patients with GC using liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) to detect reliable biomarkers for GC. Furthermore, combining the results with a machine learning strategy, the present study attempted to establish a diagnostic system for GC. A total of 20 plasma samples from preoperative patients with GC and 16 plasma samples from tumor-free patients (controls) were selected from our biobank named 'SHINGEN (Yamanashi Biobank of Gastroenterological Cancers)', which includes a total of 1,592 plasma samples, and were analyzed by LC/ESI-MS. The obtained data were discriminated using a machine learning-based diagnostic algorithm, whose discriminant ability was confirmed through leave-one-out cross-validation. Using LC/ESI-MS, the levels of 236 lipid molecules were determined. Biomarker analysis revealed that a few lipids that were downregulated in the GC group could discriminate between the GC and control groups. Whole lipid composition analysis using partial least squares regression revealed good discrimination ability between the GC and control groups. Integrative analysis of all molecules using the aforementioned machine learning method exhibited a diagnostic accuracy of 94.4% (specificity, 93.8%; sensitivity, 95.0%). In conclusion, the outcomes of the present study suggested the potential future application of the aforementioned system in clinical settings. By accumulating more reliable data, the present system will be able to detect early-stage cancer and will be capable of predicting the efficacy of each therapeutic strategy.

DOI： 10.3892/ol.2021.12666

PubMed

Total pages：14   Responsible for pages：63-76   Language：Japanese   Book type：Textbook, survey, introduction

Event date： 2022.11

Language：Japanese   Presentation type：Poster presentation  

Event date： 2021.3

Language：English   Presentation type：Poster presentation  

Event date： 2020.5

Language：Japanese   Presentation type：Poster presentation  

Event date： 2019.3

Language：Japanese   Presentation type：Poster presentation  

Event date： 2018.3

Language：Japanese   Presentation type：Oral presentation(general)  

Venue：富山大学和漢医薬学総合研究所  

Applicant：国立大学法人山梨大学

Application no：特願2023-062125  Date applied：2023.4