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


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/mm2 (range, 123–8101/mm2). 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/mm2 best discriminated pCR from non-pCR. In multivariate analysis, high TIL density (> 2420/mm2) 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.

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Authors and Affiliations



NA designed and write the study, analyzed the data, and collected clinical data and sample. HM and NY designed the study and assisted with data analysis. RT analyzed HER2 fluorescence in situ hybridization. All authors have substantial contributions to the conception, design, and drafting the work.

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Correspondence to Hirofumi Matsumoto.

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The authors declared that they have no conflict of interest.

Ethical approval

This study was part of a research project approved by the ethics committees at Nakagami Hospital (2016029-2) and Nahanishi Clinic (NNCEC2018005). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from each individual participant included in the study.

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Supplementary Fig. 1

The area of TIL assessment on CNB slide. TILs were evaluated on the stromal compartment within the border of the invasive foci (encircled in yellow). TILs around ductal carcinoma in situ elements and area of tertiary lymphoid structures were excluded from TIL assessment (encircled in black) (PNG 8163 kb)

High Resolution Image (TIF 8071 kb)

Supplementary Fig. 2

Concordance in stromal area derived from machine learning-based classification and manual assessment (PNG 149 kb)

High Resolution Image (TIF 502 kb)

Supplementary Fig. 3

Concordance in the number of stromal TILs derived from DIA and manual assessment (PNG 147 kb)

High Resolution Image (TIF 491 kb)

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Abe, N., Matsumoto, H., Takamatsu, R. et al. Quantitative digital image analysis of tumor-infiltrating lymphocytes in HER2-positive breast cancer. Virchows Arch 476, 701–709 (2020).

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