Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition

Matsuura, Takanori; Abe, Masatoshi; Harada, Yoshiyuki; Kido, Masahiro; Nagahara, Hajime; Kodama, Yuzo; Ueda, Yoshihide; Hara, Eiji; Niioka, Hirohiko; Matsumoto, Tomonori

doi:https://doi.org/10.1038/s43856-025-00967-8

Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition

Matsuura, Takanori; Abe, Masatoshi; Harada, Yoshiyuki et al.

Communications Medicine, 2025, 5, 270

アクセス数:342件（2026-02-15 23:51 集計）

固定URL: https://hdl.handle.net/11094/102904

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タイトル	Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition
著者	NRID 1000081000495 1000081000495 著者名 Matsuura, Takanori
	著者名 Abe, Masatoshi
	著者名 Harada, Yoshiyuki
	NRID 1000000403246 1000000403246 著者名 Kido, Masahiro
	NRID 1000080362648 1000080362648 ORCID 0000-0003-1579-8767 0000-0003-1579-8767 researchmap permalink read0054705 read0054705 Scopus Author ID 7006675539 7006675539 著者名 Nagahara, Hajime
	NRID 1000080378687 1000080378687 著者名 Kodama, Yuzo
	NRID 1000090378662 1000090378662 著者名 Ueda, Yoshihide
	NRID 1000080263268 1000080263268 ORCID 0000-0001-7821-3960 0000-0001-7821-3960 researchmap permalink read0108962 read0108962 Scopus Author ID 7004944434 7004944434 著者名 Hara, Eiji
	NRID 1000070552074 1000070552074 著者名 Niioka, Hirohiko
	NRID 1000040886610 1000040886610 ORCID 0000-0003-4483-2178 0000-0003-4483-2178 researchmap permalink tomo_matsumoto tomo_matsumoto Scopus Author ID 55628587431 55628587431 著者名 Matsumoto, Tomonori
内容	Matsuura T., Abe M., Harada Y., et al. Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition. Communications Medicine 5, 270 (2025); https://doi.org/10.1038/s43856-025-00967-8.
抄録	Background: Polyploidy is frequently observed in cancer cells and is closely associated with chromosomal instability, which can lead to cancer progression. Polyploid cancers are more aggressive than diploid cancers, and polyploidy has been shown to be a prognostic marker for hepatocellular carcinoma (HCC). However, polyploidy is challenging to diagnose. Currently, no clinically implementable methods are available for diagnosing polyploidy in cancer. Methods: We established a method for assessing polyploidization in HCC using deep-learning-based artificial intelligence image recognition models to assess hematoxylin and eosin-stained pathological images. Using 44 HCCs whose ploidy status had been determined by chromosome fluorescence in situ hybridization, we evaluated the ability of our constructed deep learning models to detect HCC ploidy. We then tested the models on an independent group of 169 liver cancers and applied them to a publicly available dataset. Results: Here we show that our constructed models effectively assess HCC ploidy in a separate cohort and identify a subset with poor prognosis based on the ploidy determinations for 169 HCCs. Our pipeline also identifies HCCs with poor prognosis in the external dataset, with a more significant difference than that for ploidy inferences by genomic analysis. By exploiting the high processing capacity of artificial intelligence, new aspects of polyploid HCC, such as the high prevalence of scirrhous structures, are identified. Conclusions: Our findings suggest that ploidy assessment using artificial intelligence-based pathological image recognition can serve as a novel diagnostic tool for personalized medicine.
出版者	Springer Nature
収録物名	Communications Medicine
巻(号)	5
ページ	270
刊行年月	2025-07-03
言語	英語
ハンドルURL	https://hdl.handle.net/11094/102904
eISSN	2730664X
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1038/s43856-025-00967-8
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Communications Medicine. 2025, 5, p. 270
DC.title	Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition
DC.creator	Matsuura, Takanori
	Abe, Masatoshi
	Harada, Yoshiyuki
	Kido, Masahiro
	Nagahara, Hajime
	Kodama, Yuzo
	Ueda, Yoshihide
	Hara, Eiji
	Niioka, Hirohiko
	Matsumoto, Tomonori
DC.publisher	Springer Nature
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-07-03
DC.identifier	https://doi.org/10.1038/s43856-025-00967-8
DC.description	Matsuura T., Abe M., Harada Y., et al. Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition. Communications Medicine 5, 270 (2025); https://doi.org/10.1038/s43856-025-00967-8.
DCTERMS.abstract	Background: Polyploidy is frequently observed in cancer cells and is closely associated with chromosomal instability, which can lead to cancer progression. Polyploid cancers are more aggressive than diploid cancers, and polyploidy has been shown to be a prognostic marker for hepatocellular carcinoma (HCC). However, polyploidy is challenging to diagnose. Currently, no clinically implementable methods are available for diagnosing polyploidy in cancer. Methods: We established a method for assessing polyploidization in HCC using deep-learning-based artificial intelligence image recognition models to assess hematoxylin and eosin-stained pathological images. Using 44 HCCs whose ploidy status had been determined by chromosome fluorescence in situ hybridization, we evaluated the ability of our constructed deep learning models to detect HCC ploidy. We then tested the models on an independent group of 169 liver cancers and applied them to a publicly available dataset. Results: Here we show that our constructed models effectively assess HCC ploidy in a separate cohort and identify a subset with poor prognosis based on the ploidy determinations for 169 HCCs. Our pipeline also identifies HCCs with poor prognosis in the external dataset, with a more significant difference than that for ploidy inferences by genomic analysis. By exploiting the high processing capacity of artificial intelligence, new aspects of polyploid HCC, such as the high prevalence of scirrhous structures, are identified. Conclusions: Our findings suggest that ploidy assessment using artificial intelligence-based pathological image recognition can serve as a novel diagnostic tool for personalized medicine.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	102904
citation_title	Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition
citation_author	Matsuura, Takanori
	Abe, Masatoshi
	Harada, Yoshiyuki
	Kido, Masahiro
	Nagahara, Hajime
	Kodama, Yuzo
	Ueda, Yoshihide
	Hara, Eiji
	Niioka, Hirohiko
	Matsumoto, Tomonori
citation_publisher	Springer Nature
citation_language	英語
citation_date	2025-07-03
citation_journal_title	Communications Medicine
citation_volume	5
citation_firstpage	270
citation_doi	https://doi.org/10.1038/s43856-025-00967-8
citation_public_url	https://hdl.handle.net/11094/102904

Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition

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