論文

2023年1月8日

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

Annals of Surgical Oncology
  • Sho Kiritani
  • Tomohiko Iwano
  • Kentaro Yoshimura
  • Ryo Saito
  • Takashi Nakayama
  • Daisuke Yamamoto
  • Hiroyuki Hakoda
  • Genki Watanabe
  • Nobuhisa Akamatsu
  • Junichi Arita
  • Junichi Kaneko
  • Sén Takeda
  • Daisuke Ichikawa
  • Kiyoshi Hasegawa
  • 全て表示

記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1245/s10434-022-13012-y
出版者・発行元
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.

リンク情報
DOI
https://doi.org/10.1245/s10434-022-13012-y
URL
https://link.springer.com/content/pdf/10.1245/s10434-022-13012-y.pdf
URL
https://link.springer.com/article/10.1245/s10434-022-13012-y/fulltext.html
ID情報
  • DOI : 10.1245/s10434-022-13012-y
  • ISSN : 1068-9265
  • eISSN : 1534-4681

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