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We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin\u2010stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer\u2010free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between <jats:italic>smuLymphNet<\/jats:italic>\u2010captured GC and sinus quantifications and distant metastasis\u2010free survival (DMFS). <jats:italic>smuLymphNet<\/jats:italic> achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). <jats:italic>smuLymphNet<\/jats:italic>\u2010captured sinuses were increased in LNs harbouring GCs (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). <jats:italic>smuLymphNet<\/jats:italic>\u2010captured GCs retained clinical relevance in LN\u2010positive TNBC patients whose cancer\u2010free LNs had on average \u22652 GCs, had longer DMFS (hazard ratio [HR]\u2009=\u20090.28, <jats:italic>p<\/jats:italic>\u2009=\u20090.02) and extended GCs' prognostic value to LN\u2010negative TNBC patients (HR\u2009=\u20090.14, <jats:italic>p<\/jats:italic>\u2009=\u20090.002). Enlarged <jats:italic>smuLymphNet<\/jats:italic>\u2010captured sinuses in involved LNs were associated with superior DMFS in LN\u2010positive TNBC patients in a cohort from Guy's Hospital (multivariate HR\u2009=\u20090.39, <jats:italic>p<\/jats:italic>\u2009=\u20090.039) and with distant recurrence\u2010free survival in 95 LN\u2010positive TNBC patients of the Dutch\u2010N4plus trial (HR\u2009=\u20090.44, <jats:italic>p<\/jats:italic>\u2009=\u20090.024). Heuristic scoring of subcapsular sinuses in LNs of LN\u2010positive Tianjin TNBC patients (<jats:italic>n<\/jats:italic>\u2009=\u200985) cross\u2010validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR\u2009=\u20090.33, <jats:italic>p<\/jats:italic>\u2009=\u20090.029 and cancer\u2010free LNs: HR\u2009=\u20090.21 <jats:italic>p<\/jats:italic>\u2009=\u20090.01). Morphological LN features reflective of cancer\u2010associated responses are robustly quantifiable by <jats:italic>smuLymphNet.<\/jats:italic> Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. \u00a9 2023 The Authors. <jats:italic>The Journal of Pathology<\/jats:italic> published by John Wiley &amp; Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.<\/jats:p>","DOI":"10.1002\/path.6088","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T19:01:25Z","timestamp":1685041285000},"page":"376-389","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large\u2010scale studies"],"prefix":"10.1002","volume":"260","author":[{"given":"Gregory","family":"Verghese","sequence":"first","affiliation":[{"name":"Cancer Bioinformatics, School of Cancer &amp; 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