A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

Kato, Minoru; Hayashi, Yoshito; Uema, Ryotaro; Kanesaka, Takashi; Yamaguchi, Shinjiro; Maekawa, Akira; Yamada, Takuya; Yamamoto, Masashi; Kitamura, Shinji; Inoue, Takuya; Yamamoto, Shunsuke; Kizu, Takashi; Takeda, Risato; Ogiyama, Hideharu; Yamamoto, Katsumi; Aoi, Kenji; Nagaike, Koji; Sasai, Yasutaka; Egawa, Satoshi; Akamatsu, Haruki; Ogawa, Hiroyuki; Komori, Masato; Akihiro, Nishihara; Yoshihara, Takeo; Tsujii, Yoshiki; Takehara, Tetsuo

doi:https://doi.org/10.1007/s10120-024-01511-8

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

Kato, Minoru; Hayashi, Yoshito; Uema, Ryotaro et al.

Gastric Cancer, 2024, 27(5), 1069-1077

Number of Access:3,854（2025-09-06 08:20 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/97124

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Title	A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
Creator	NRID 1000080939249 1000080939249 CreatorName Kato, Minoru
	NRID 1000080647123 1000080647123 researchmap permalink y-h y-h Scopus Author ID 35219993200 35219993200 CreatorName Hayashi, Yoshito
	NRID 1000030939161 1000030939161 researchmap permalink ryotarouema ryotarouema CreatorName Uema, Ryotaro
	CreatorName Kanesaka, Takashi
	CreatorName Yamaguchi, Shinjiro
	CreatorName Maekawa, Akira
	CreatorName Yamada, Takuya
	CreatorName Yamamoto, Masashi
	CreatorName Kitamura, Shinji
	CreatorName Inoue, Takuya
	researchmap permalink shunsuke_xs shunsuke_xs Scopus Author ID 57204908251 57204908251 CreatorName Yamamoto, Shunsuke
	NRID 1000030748299 1000030748299 CreatorName Kizu, Takashi
	CreatorName Takeda, Risato
	CreatorName Ogiyama, Hideharu
	CreatorName Yamamoto, Katsumi
	CreatorName Aoi, Kenji
	CreatorName Nagaike, Koji
	CreatorName Sasai, Yasutaka
	CreatorName Egawa, Satoshi
	CreatorName Akamatsu, Haruki
	CreatorName Ogawa, Hiroyuki
	CreatorName Komori, Masato
	CreatorName Akihiro, Nishihara
	NRID 1000020814601 1000020814601 researchmap permalink ty_gh ty_gh CreatorName Yoshihara, Takeo
	NRID 1000080795170 1000080795170 researchmap permalink yhcks yhcks Scopus Author ID 57192176408 57192176408 CreatorName Tsujii, Yoshiki
	NRID 1000070335355 1000070335355 researchmap permalink takeharatetsuo takeharatetsuo Scopus Author ID 7103332258 7103332258 CreatorName Takehara, Tetsuo
Subject	Artificial intelligence
	Early gastric cancer
	Endoscopic submucosal dissection
	Lymph node metastasis
	Machine learning
Description	The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.
Abstract	Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.
Publisher	Springer
Source Title	Gastric Cancer
Volume (Issue)	27(5)
Page	1069-1077
Date of Issued	2024-05-25
Language	English
Handle URL	https://hdl.handle.net/11094/97124
PISSN	14363291
eISSN	14363305
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1007/s10120-024-01511-8
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Gastric Cancer. 2024, 27(5), p. 1069-1077
DC.title	A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
DC.creator	Kato, Minoru
	Hayashi, Yoshito
	Uema, Ryotaro
	Kanesaka, Takashi
	Yamaguchi, Shinjiro
	Maekawa, Akira
	Yamada, Takuya
	Yamamoto, Masashi
	Kitamura, Shinji
	Inoue, Takuya
	Yamamoto, Shunsuke
	Kizu, Takashi
	Takeda, Risato
	Ogiyama, Hideharu
	Yamamoto, Katsumi
	Aoi, Kenji
	Nagaike, Koji
	Sasai, Yasutaka
	Egawa, Satoshi
	Akamatsu, Haruki
	Ogawa, Hiroyuki
	Komori, Masato
	Akihiro, Nishihara
	Yoshihara, Takeo
	Tsujii, Yoshiki
	Takehara, Tetsuo
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-05-25
DC.identifier	https://doi.org/10.1007/s10120-024-01511-8
DC.subject	Artificial intelligence
	Early gastric cancer
	Endoscopic submucosal dissection
	Lymph node metastasis
	Machine learning
DC.description	The version of record of this article, first published in Gastric Cancer, is available online at Publisher’s website: https://doi.org/10.1007/s10120-024-01511-8.
DCTERMS.abstract	Background: We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system. Methods: We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort. Results: LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test). Conclusions: Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria. Mini-abstract: We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	97124
citation_title	A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
citation_author	Kato, Minoru
	Hayashi, Yoshito
	Uema, Ryotaro
	Kanesaka, Takashi
	Yamaguchi, Shinjiro
	Maekawa, Akira
	Yamada, Takuya
	Yamamoto, Masashi
	Kitamura, Shinji
	Inoue, Takuya
	Yamamoto, Shunsuke
	Kizu, Takashi
	Takeda, Risato
	Ogiyama, Hideharu
	Yamamoto, Katsumi
	Aoi, Kenji
	Nagaike, Koji
	Sasai, Yasutaka
	Egawa, Satoshi
	Akamatsu, Haruki
	Ogawa, Hiroyuki
	Komori, Masato
	Akihiro, Nishihara
	Yoshihara, Takeo
	Tsujii, Yoshiki
	Takehara, Tetsuo
citation_publisher	Springer
citation_language	English
citation_date	2024-05-25
citation_journal_title	Gastric Cancer
citation_volume	27
citation_issue	5
citation_firstpage	1069
citation_lastpage	1077
citation_doi	https://doi.org/10.1007/s10120-024-01511-8
citation_public_url	https://hdl.handle.net/11094/97124

A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

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