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Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.

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Title: Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.
Authors: Zhang, Yanping1, Han, Jiaying1, Chen, Huang2, Hu, Fengyuan1, Huang, Yaping1, Tian, Geng3, Zhong, Dingrong2, Yang, Jialiang4
Source: Briefings in Bioinformatics; Nov2025, Vol. 26 Issue 6, p1-12, 12p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 190784202
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=190784202
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1093/bib/bbaf580
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      – Code: eng
        Text: English
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        PageCount: 12
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      – TitleFull: Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.
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            NameFull: Zhang, Yanping
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            NameFull: Han, Jiaying
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            NameFull: Chen, Huang
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            NameFull: Hu, Fengyuan
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            NameFull: Huang, Yaping
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            NameFull: Tian, Geng
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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            – TitleFull: Briefings in Bioinformatics
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