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  1. 学位論文
  2. 博士論文
  3. 医学研究科
  1. 部局別インデックス
  2. 医学研究科

Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer

http://hdl.handle.net/20.500.12000/46671
http://hdl.handle.net/20.500.12000/46671
6749fcaa-3e8e-470d-b47b-ac5bf01c4d61
名前 / ファイル ライセンス アクション
iken507digest.pdf iken507digest.pdf
iken507review.pdf iken507review.pdf
iken507abstract.pdf iken507abstract.pdf
Item type デフォルトアイテムタイプ(フル)(1)
公開日 2020-09-14
タイトル
タイトル Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer
言語 en
作成者 Abe, Norie

× Abe, Norie

en Abe, Norie

阿部, 典恵

× 阿部, 典恵

ja 阿部, 典恵

アクセス権
アクセス権 embargoed access
アクセス権URI http://purl.org/coar/access_right/c_f1cf
主題
言語 en
主題Scheme Other
主題 Tumor-infiltrating lymphocyte (TIL)
言語 en
主題Scheme Other
主題 Image analysis
言語 en
主題Scheme Other
主題 HER2
言語 en
主題Scheme Other
主題 Pathological complete response (pCR)
内容記述
内容記述タイプ Other
内容記述 As visual quantification of the density of tumor-infiltrating lymphocytes (TILs) lacks in precision, digital image analysis (DIA) approach has been applied in order to improve. In several studies, TIL density has been examined on hematoxylin and eosin (HE)-stained sections using DIA. The aim of the present study was to quantify TIL density on HE sections of core needle biopsies using DIA and investigate its association with clinicopathological parameters and pathological response to neoadjuvant chemotherapy in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. The study cohort comprised of patients with HER2-positive breast cancer, all treated with neoadjuvant anti-HER2 therapy. DIA software applying machine learning-based classification of epithelial and stromal elements was used to count TILs. TIL density was determined as the number of TILs per square millimeter of stromal tissue. Median TIL density was 1287/mm^2 (range, 123–8101/mm^2). A high TIL density was associated with higher histological grade (P = 0.02), estrogen receptor negativity (P = 0.036), and pathological complete response (pCR) (P < 0.0001). In analyses using receiver operating characteristic curves, a threshold TIL density of 2420/mm^2 best discriminated pCR from non-pCR. In multivariate analysis, high TIL density (> 2420/mm^2) was significantly associated with pCR (P < 0.0001). Our results indicate that DIA can assess TIL density quantitatively, machine learning-based classification algorithm allowing determination of TIL density as the number of TILs per unit area, and TIL density established by this method appears to be an independent predictor of pCR in HER2-positive breast cancer.
内容記述タイプ Other
内容記述 学位論文
出版者
言語 en
出版者 University of the Ryukyus
言語
言語 eng
資源タイプ
資源タイプ doctoral thesis
資源タイプ識別子 http://purl.org/coar/resource_type/c_db06
識別子
識別子 http://hdl.handle.net/20.500.12000/46671
識別子タイプ HDL
関連情報
関連識別子
識別子タイプ DOI
関連識別子 https://doi.org/10.1007/s00428-019-02730-6
関連識別子
識別子タイプ DOI
関連識別子 https://doi.org/10.1007/s00428-019-02730-6
収録物識別子
収録物識別子タイプ ISSN
収録物識別子 0945-6317
収録物識別子タイプ ISSN
収録物識別子 1432-2307
収録物名
言語 en
収録物名 Virchows Archiv
書誌情報
巻 476, p. 701-709
学位授与番号
学位授与番号 甲第507号
学位名
学位名 博士(医学)
言語 ja
学位授与年月日
学位授与年月日 2020-03-24
学位授与機関
学位授与機関識別子
学位授与機関識別子 18001
学位授与機関識別子Scheme kakenhi
学位授与機関名
学位授与機関名 琉球大学
言語 ja
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