{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T18:33:11Z","timestamp":1793039591211,"version":"4.5.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Triple-negative breast cancer (TNBC) is an aggressive subtype with limited therapeutic options due to lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. While neoadjuvant immunotherapy shows promise, the impact of spatial organization of tertiary lymphoid structures (TLS) and tumor-infiltrating lymphocytes (TILs) on treatment efficacy remains incompletely characterized. This exploratory study aims to establish an AI-based quantitative framework for analyzing tumor-immune spatial interactions and develop a predictive model for chemo-immunotherapy response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed an integrated AI pipeline for automated analysis of hematoxylin-eosin (HE)-stained breast cancer samples. The framework incorporates: 1) TLS detection and classification, 2) TIL quantification, and 3) spatial relationship mapping between lymphocytes and tumor cells. Biomarkers associated with Miller-Payne (MP) response grades were identified using multivariate statistics, enabling construction of a random forest prognostic model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The TLS recognition model demonstrated substantial agreement with pathologists (\u03ba=0.73), while the TIL classifier achieved 0.92 accuracy. Analysis of 32 HE images from triple-negative breast cancer (TNBC) cases treated with neoadjuvant chemo-immunotherapy revealed significant associations: stromal lymphocyte density and percentage positively correlated with MP grades (p&amp;lt;0.05); average cell counts in 2-cell and 3-cell lymphocyte aggregates showed no significant correlations; shorter mean minimum distances between lymphocytes and tumor cells within 10-30 \u03bcm radius range were inversely associated with MP grades (p&amp;lt;0.05). The spatial-feature-based prediction model achieved an AUC of 0.81 (95% CI: 0.76\u20130.86).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study established an AI-driven HE analysis pipeline that precisely quantified spatial determinants of tumor-immune interactions. Lymphocyte spatial organization, particularly proximity to tumor cells and lymphocyte aggregation patterns, serves as a critical predictor of chemo-immunotherapy response beyond density metrics. The validated MP-grade prediction model demonstrates translational potential for clinical decision-making in TNBC management, although external validation in larger cohorts is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1797753","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T05:26:14Z","timestamp":1776317174000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["AI-driven spatial analysis of tumor-infiltrating lymphocytes predicts chemo-immunotherapy response in triple-negative breast cancer"],"prefix":"10.3389","volume":"17","author":[{"given":"Hai","family":"Liang","sequence":"first","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hejie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ru","family":"Feng","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoshuang","family":"Yan","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Qilu Pharmaceutical R&D Center Ltd.","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linfeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2016.66","article-title":"Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease","volume":"13","author":"Bianchini","year":"2016","journal-title":"Nat Rev Clin 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