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Deep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer

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dc.contributor.authorChoi, S-
dc.contributor.authorCho, SI-
dc.contributor.authorJung, W-
dc.contributor.authorLee, T-
dc.contributor.authorChoi, SJ-
dc.contributor.authorSong, S-
dc.contributor.authorPark, G-
dc.contributor.authorPark, S-
dc.contributor.authorMa, M-
dc.contributor.authorPereira, S-
dc.contributor.authorYoo, D-
dc.contributor.authorShin, S-
dc.contributor.authorOck, CY-
dc.contributor.authorKim, S-
dc.description.abstractTumor-infiltrating lymphocytes (TILs) have been recognized as key players in the tumor microenvironment of breast cancer, but substantial interobserver variability among pathologists has impeded its utility as a biomarker. We developed a deep learning (DL)-based TIL analyzer to evaluate stromal TILs (sTILs) in breast cancer. Three pathologists evaluated 402 whole slide images of breast cancer and interpreted the sTIL scores. A standalone performance of the DL model was evaluated in the 210 cases (52.2%) exhibiting sTIL score differences of less than 10 percentage points, yielding a concordance correlation coefficient of 0.755 (95% confidence interval [CI], 0.693–0.805) in comparison to the pathologists’ scores. For the 226 slides (56.2%) showing a 10 percentage points or greater variance between pathologists and the DL model, revisions were made. The number of discordant cases was reduced to 116 (28.9%) with the DL assistance (p < 0.001). The DL assistance also increased the concordance correlation coefficient of the sTIL score among every two pathologists. In triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who underwent the neoadjuvant chemotherapy, the DL-assisted revision notably accentuated higher sTIL scores in responders (26.8 ± 19.6 vs. 19.0 ± 16.4, p = 0.003). Furthermore, the DL-assistant revision disclosed the correlation of sTIL-high tumors (sTIL ≥ 50) with the chemotherapeutic response (odd ratio 1.28 [95% confidence interval, 1.01–1.63], p = 0.039). Through enhancing inter-pathologist concordance in sTIL interpretation and predicting neoadjuvant chemotherapy response, here we report the utility of the DL-based tool as a reference for sTIL scoring in breast cancer assessment.-
dc.titleDeep learning model improves tumor-infiltrating lymphocyte evaluation and therapeutic response prediction in breast cancer-
dc.contributor.affiliatedAuthorKim, S-
dc.type.localJournal Papers-
dc.citation.titleNPJ breast cancer-
dc.identifier.bibliographicCitationNPJ breast cancer, 9(1). : 71-71, 2023-
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Journal Papers > School of Medicine / Graduate School of Medicine > Pathology
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