Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies

Verghese, G; Li, M; Liu, F; Lohan, A; Kurian, NC; Meena, S; Gazinska, P; Shah, A; Oozeer, A; Chan, T; Opdam, M; Linn, S; Gillett, C; Alberts, E; Hardiman, T; Jones, S; Thavaraj, S; Jones, JL; Salgado, R; Pinder, SE; Rane, S; Sethi, A; Grigoriadis, A

Author(s): Verghese, G; Li, M; Liu, F; Lohan, A; Kurian, NC; Meena, S; Gazinska, P; Shah, A; Oozeer, A; Chan, T; Opdam, M; Linn, S; Gillett, C; Alberts, E; Hardiman, T; Jones, S; Thavaraj, S; Jones, JL; Salgado, R; Pinder, SE; Rane, S; Sethi, A; Grigoriadis, A;
Details: Publication Year 2023,Volume 260,Issue #4,Page 376-389
Journal Title: Journal of Pathology
Publication Type: Research article
Abstract: The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free 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 smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet 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). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average >/=2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. (c) 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Keywords: Humans; *Deep Learning; Lymph Nodes/pathology; Lymphatic Metastasis/pathology; Neoplasm Staging; Prognosis; Retrospective Studies; *Triple Negative Breast Neoplasms/therapy/pathology; Female; Clinical Trials as Topic
Department(s): Laboratory Research
PubMed ID: 37230111
Publisher's Version: https://doi.org/10.1002/path.6088
Open Access at Publisher's Site: https://doi.org/10.1002/path.6088
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2024-08-20 06:18:02

Last Modified: 2024-08-20 06:56:39