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Computer-Aided Detection of Quantitative Signatures for Breast Fibroepithelial Tumors Using Label-Free Multi-Photon Imaging.
Kobayashi-Taguchi, Kana; Saitou, Takashi; Kamei, Yoshiaki; Murakami, Akari; Nishiyama, Kanako; Aoki, Reina; Kusakabe, Erina; Noda, Haruna; Yamashita, Michiko; Kitazawa, Riko; Imamura, Takeshi; Takada, Yasutsugu.
Afiliación
  • Kobayashi-Taguchi K; Department of Breast Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Saitou T; Department of Hepato-Biliary-Pancreatic Surgery and Breast Surgery, Ehime University, Ehime 791-0204, Japan.
  • Kamei Y; Department of Molecular Medicine for Pathogenesis, Graduate School of Medicine, Ehime University, Ehime 791-0204, Japan.
  • Murakami A; Translational Research Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Nishiyama K; Department of Breast Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Aoki R; Department of Hepato-Biliary-Pancreatic Surgery and Breast Surgery, Ehime University, Ehime 791-0204, Japan.
  • Kusakabe E; Department of Breast Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Noda H; Department of Hepato-Biliary-Pancreatic Surgery and Breast Surgery, Ehime University, Ehime 791-0204, Japan.
  • Yamashita M; Department of Breast Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Kitazawa R; Department of Hepato-Biliary-Pancreatic Surgery and Breast Surgery, Ehime University, Ehime 791-0204, Japan.
  • Imamura T; Department of Breast Center, Ehime University Hospital, Ehime 791-0204, Japan.
  • Takada Y; Department of Hepato-Biliary-Pancreatic Surgery and Breast Surgery, Ehime University, Ehime 791-0204, Japan.
Molecules ; 27(10)2022 May 23.
Article en En | MEDLINE | ID: mdl-35630817
Fibroadenomas (FAs) and phyllodes tumors (PTs) are major benign breast tumors, pathologically classified as fibroepithelial tumors. Although the clinical management of PTs differs from FAs, distinction by core needle biopsy diagnoses is still challenging. Here, a combined technique of label-free imaging with multi-photon microscopy and artificial intelligence was applied to detect quantitative signatures that differentiate fibroepithelial lesions. Multi-photon excited autofluorescence and second harmonic generation (SHG) signals were detected in tissue sections. A pixel-wise semantic segmentation method using a deep learning framework was used to separate epithelial and stromal regions automatically. The epithelial to stromal area ratio and the collagen SHG signal strength were investigated for their ability to distinguish fibroepithelial lesions. An image segmentation analysis with a pixel-wise semantic segmentation framework using a deep convolutional neural network showed the accurate separation of epithelial and stromal regions. A further investigation, to determine if scoring the epithelial to stromal area ratio and the SHG signal strength within the stromal area could be a marker for differentiating fibroepithelial tumors, showed accurate classification. Therefore, molecular and morphological changes, detected through the assistance of computational and label-free multi-photon imaging techniques, enable us to propose quantitative signatures for epithelial and stromal alterations in breast tissues.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Neoplasias Fibroepiteliales / Fibroadenoma Tipo de estudio: Diagnostic_studies Límite: Female / Humans Idioma: En Revista: Molecules Asunto de la revista: BIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Suiza
Texto completo
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Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Neoplasias Fibroepiteliales / Fibroadenoma Tipo de estudio: Diagnostic_studies Límite: Female / Humans Idioma: En Revista: Molecules Asunto de la revista: BIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Suiza