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Annals of Surgical Oncology
Published in: Annals of Surgical Oncology 5/2023

Open Access 08-01-2023 | Translational Research

New Diagnostic Modality Combining Mass Spectrometry and Machine Learning for the Discrimination of Malignant Intraductal Papillary Mucinous Neoplasms

Authors: Sho Kiritani, MD, PhD, Tomohiko Iwano, PhD, Kentaro Yoshimura, PhD, Ryo Saito, MD, PhD, Takashi Nakayama, MD, PhD, Daisuke Yamamoto, MD, Hiroyuki Hakoda, MD, Genki Watanabe, MD, PhD, Nobuhisa Akamatsu, MD, PhD, Junichi Arita, MD, PhD, Junichi Kaneko, MD, PhD, Sén Takeda, MD, PhD, Daisuke Ichikawa, MD, PhD, Kiyoshi Hasegawa, MD, PhD

Published in: Annals of Surgical Oncology | Issue 5/2023

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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.
Appendix
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Literature
1.
Werner J, Fritz S, Büchler MW. Intraductal papillary mucinous neoplasms of the pancreas—a surgical disease. Nat Rev Gastroenterol Hepatol. 2012;9(5):253–9.CrossRefPubMed
2.
Tanaka M. Clinical management and surgical decision-making of IPMN of the pancreas. Methods Mol Biol. 2019;1882:9–22.CrossRefPubMed
3.
Adsay V, Mino-Kenudson M, Furukawa T, et al. Pathologic evaluation and reporting of intraductal papillary mucinous neoplasms of the pancreas and other tumoral intraepithelial neoplasms of pancreatobiliary tract: recommendations of verona consensus meeting. Ann Surg. 2016;263(1):162–77.CrossRefPubMed
4.
Klöppel G, Solcia E, Longnecker DS. Histological typing of tumours of the exocrine pancreas. Cham: Springer; 1996.CrossRef
5.
Marchegiani G, Andrianello S, Pollini T, et al. “Trivial” cysts redefine the risk of cancer in presumed branch-duct intraductal papillary mucinous neoplasms of the pancreas: a potential target for follow-up discontinuation? Am J Gastroenterol. 2019;114(10):1678–84.CrossRefPubMed
6.
Facciorusso A, Crinò SF, Ramai D, et al. Association between pancreatic intraductal papillary mucinous neoplasms and extrapancreatic malignancies: a systematic review with meta-analysis. Eur J Surg Oncol J Eur Soc Surg Oncol Br Assoc Surg Oncol. 2022;48(3):632–9.
7.
Baiocchi GL, Molfino S, Frittoli B, et al. Increased risk of second malignancy in pancreatic intraductal papillary mucinous tumors: review of the literature. World J Gastroenterol. 2015;21(23):7313–9.CrossRefPubMedPubMedCentral
8.
Pugliese L, Keskin M, Maisonneuve P, et al. Increased incidence of extrapancreatic neoplasms in patients with IPMN: fact or fiction? A critical systematic review. Pancreatol Off J Int Assoc Pancreatol (IAP). 2015;15(3):209–16.CrossRef
9.
Basturk O, Esposito I, Fukushim N. Pancreatic intraductal papillary mucinous neoplasm. In: WHO classification of tumors. WHO classification of tumors. Digestive system tumors. Lyon: IARC Press; 2019. p. 310–8.
10.
Tanaka M, Fernández-Del Castillo C, Kamisawa T, et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatol Off J Int Assoc Pancreatol (IAP). 2017;17(5):738–53.CrossRef
11.
Shimizu Y, Hijioka S, Hirono S, et al. New model for predicting malignancy in patients with intraductal papillary mucinous neoplasm. Ann Surg. 2020;272(1):155–62.CrossRefPubMed
12.
Marchegiani G, Andrianello S, Borin A, et al. Systematic review, meta-analysis, and a high-volume center experience supporting the new role of mural nodules proposed by the updated 2017 international guidelines on IPMN of the pancreas. Surgery. 2018;163(6):1272–9.CrossRefPubMed
13.
Crippa S, Fogliati A, Valente R, et al. A tug-of-war in intraductal papillary mucinous neoplasms management: comparison between 2017 International and 2018 European guidelines. Dig Liver Dis. 2021;53(8):998–1003.CrossRefPubMed
14.
Hiraoka K, Nishidate K, Mori K, Asakawa D, Suzuki S. Development of probe electrospray using a solid needle. Rapid Commun Mass Spectrom. 2007;21(18):3139–44.CrossRefPubMed
15.
Yoshimura K, Chen LC, Asakawa D, Hiraoka K, Takeda S. Physical properties of the probe electrospray ionization (PESI) needle applied to the biological samples. J Mass Spectrom. 2009;44(6):978–85.CrossRefPubMed
16.
Iwano T, Yoshimura K, Inoue S, et al. Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry. Br J Surg. 2020;107(6):632–5.CrossRefPubMedPubMedCentral
17.
Saito R, Yoshimura K, Shoda K, et al. Diagnostic significance of plasma lipid markers and machine learning-based algorithm for gastric cancer. Oncol Lett. 2021;21(5):405.CrossRefPubMedPubMedCentral
18.
Kiritani S, Yoshimura K, Arita J, et al. A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis. BMC Cancer. 2021;21(1):262.CrossRefPubMedPubMedCentral
19.
Ashizawa K, Yoshimura K, Johno H, et al. Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma. Oral Oncol. 2017;75:111–9.CrossRefPubMed
20.
Iwano T, Yoshimura K, Watanabe G, et al. High-performance collective biomarker from liquid biopsy for diagnosis of pancreatic cancer based on mass spectrometry and machine learning. J Cancer. 2021;12(24):7477–87.CrossRefPubMedPubMedCentral
21.
22.
23.
Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics. 2020;17:17.CrossRefPubMedPubMedCentral
24.
25.
de Figueiredo Junior AG, Serafim PVP, de Melo AA, et al. Analysis of the lipid profile in patients with colorectal cancer in advanced stages. Asian Pac J Cancer Prev. 2018;19(5):1287–93.PubMed
26.
Brown HM, Pirro V, Cooks RG. From DESI to the MasSpec Pen: ambient ionization mass spectrometry for tissue analysis and intrasurgical cancer diagnosis. Clin Chem. 2018;64(4):628–30.CrossRefPubMed
27.
28.
Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics. 2018;15(1):41–51.PubMed
29.
Moler EJ, Chow ML, Mian IS. Analysis of molecular profile data using generative and discriminative methods. Physiol Genomics. 2000;4(2):109–26.CrossRefPubMed
30.
Segal NH, Pavlidis P, Noble WS, et al. Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling. J Clin Oncol Off J Am Soc Clin Oncol. 2003;21(9):1775–81.CrossRef
31.
32.
Wishart DS, Guo A, Oler E, et al. HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res. 2022;50(D1):D622–31.CrossRefPubMed
33.
Tande AJ, Platz EA, Folsom AR. The metabolic syndrome is associated with reduced risk of prostate cancer. Am J Epidemiol. 2006;164(11):1094–102.CrossRefPubMed
34.
Furberg AS, Veierød MB, Wilsgaard T, Bernstein L, Thune I. Serum high-density lipoprotein cholesterol, metabolic profile, and breast cancer risk. J Nat Cancer Inst. 2004;96(15):1152–60.CrossRefPubMed
35.
Cust AE, Kaaks R, Friedenreich C, et al. Metabolic syndrome, plasma lipid, lipoprotein and glucose levels, and endometrial cancer risk in the European prospective investigation into cancer and nutrition (EPIC). Endocr Relat Cancer. 2007;14(3):755–67.CrossRefPubMed
Metadata
Title
New Diagnostic Modality Combining Mass Spectrometry and Machine Learning for the Discrimination of Malignant Intraductal Papillary Mucinous Neoplasms
Authors
Sho Kiritani, MD, PhD
Tomohiko Iwano, PhD
Kentaro Yoshimura, PhD
Ryo Saito, MD, PhD
Takashi Nakayama, MD, PhD
Daisuke Yamamoto, MD
Hiroyuki Hakoda, MD
Genki Watanabe, MD, PhD
Nobuhisa Akamatsu, MD, PhD
Junichi Arita, MD, PhD
Junichi Kaneko, MD, PhD
Sén Takeda, MD, PhD
Daisuke Ichikawa, MD, PhD
Kiyoshi Hasegawa, MD, PhD
Publication date
08-01-2023
Publisher
Springer International Publishing
Published in
Annals of Surgical Oncology / Issue 5/2023
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI
https://doi.org/10.1245/s10434-022-13012-y

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