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Title :Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer
Authors :Abe, Norie
Authors alternative :阿部, 典恵
Issue Date :23-Dec-2019
Abstract :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.
URL :https://doi.org/10.1007/s00428-019-02730-6
Type Local :学位論文
ISSN :0945-6317
1432-2307
Publisher :University of the Ryukyus
URI :http://hdl.handle.net/20.500.12000/46671
Grant id :18001医研第507号
Date of granted :2020-03-24
Degree name :博士(医学)
Grantor :琉球大学
Citation :Virchows Archiv Vol.476 p.701 -709
Appears in Collections:Doctoral Dissertation (Graduate School of Medicine)

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