Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition
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 c...
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Nature Portfolio
2025-07-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00967-8 |
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| author | Takanori Matsuura Masatoshi Abe Yoshiyuki Harada Masahiro Kido Hajime Nagahara Yuzo Kodama Yoshihide Ueda Eiji Hara Hirohiko Niioka Tomonori Matsumoto |
| author_facet | Takanori Matsuura Masatoshi Abe Yoshiyuki Harada Masahiro Kido Hajime Nagahara Yuzo Kodama Yoshihide Ueda Eiji Hara Hirohiko Niioka Tomonori Matsumoto |
| author_sort | Takanori Matsuura |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f3c01da699324aefbb0b6b39c9d4737c |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-f3c01da699324aefbb0b6b39c9d4737c2025-08-20T04:01:41ZengNature PortfolioCommunications Medicine2730-664X2025-07-015111310.1038/s43856-025-00967-8Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognitionTakanori Matsuura0Masatoshi Abe1Yoshiyuki Harada2Masahiro Kido3Hajime Nagahara4Yuzo Kodama5Yoshihide Ueda6Eiji Hara7Hirohiko Niioka8Tomonori Matsumoto9Department of Molecular Biology, Research Institute for Microbial Diseases, Osaka UniversityFaculty of Medicine, Osaka UniversityDepartment of Molecular Biology, Research Institute for Microbial Diseases, Osaka UniversityDivision of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of MedicineDepartment of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka UniversityDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of MedicineDivision of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of MedicineDepartment of Molecular Biology, Research Institute for Microbial Diseases, Osaka UniversityInstitute for Datability Science, Osaka UniversityDepartment of Molecular Biology, Research Institute for Microbial Diseases, Osaka UniversityAbstract 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.https://doi.org/10.1038/s43856-025-00967-8 |
| spellingShingle | Takanori Matsuura Masatoshi Abe Yoshiyuki Harada Masahiro Kido Hajime Nagahara Yuzo Kodama Yoshihide Ueda Eiji Hara Hirohiko Niioka Tomonori Matsumoto Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition Communications Medicine |
| title | Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition |
| title_full | Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition |
| title_fullStr | Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition |
| title_full_unstemmed | Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition |
| title_short | Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition |
| title_sort | selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence based pathological image recognition |
| url | https://doi.org/10.1038/s43856-025-00967-8 |
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