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Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models.

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Title: Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models.
Authors: Nikkuni, Yutaka1 (AUTHOR) nikkuni@dent.niigata-u.ac.jp, Nishiyama, Hideyoshi1 (AUTHOR), Takamura, Masaki1 (AUTHOR), Kobayashi, Taichi1 (AUTHOR), Soga, Marie1 (AUTHOR), Ike, Makiko1 (AUTHOR), Katsura, Kouji1 (AUTHOR), Hayashi, Takafumi1 (AUTHOR)
Source: Cancers. Nov2025, Vol. 17 Issue 21, p3573. 16p.
Database: Academic Search Ultimate
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Header DbId: asn
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An: 189611825
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  Data: Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models.
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  Data: <searchLink fieldCode="JN" term="%22Cancers%22">Cancers</searchLink>. Nov2025, Vol. 17 Issue 21, p3573. 16p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=189611825
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/cancers17213573
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 3573
    Titles:
      – TitleFull: Evaluation of the Ability to Predict Subsequent Metastasis of Early Oral Squamous Cell Carcinoma Using PET Radiomics Machine Learning Models.
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            NameFull: Nishiyama, Hideyoshi
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            NameFull: Takamura, Masaki
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            NameFull: Kobayashi, Taichi
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            NameFull: Soga, Marie
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            NameFull: Ike, Makiko
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            NameFull: Katsura, Kouji
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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              Value: 17
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              Value: 21
          Titles:
            – TitleFull: Cancers
              Type: main
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