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多尺度深度学习框架捕捉淋巴结中的系统性免疫特征,可预测大规模研究中三阴性乳腺癌的结局。

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

机构信息

Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

出版信息

J Pathol. 2023 Aug;260(4):376-389. doi: 10.1002/path.6088. Epub 2023 May 25.


DOI:10.1002/path.6088
PMID:37230111
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10720675/
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. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

摘要

淋巴结(LNs)中系统免疫反应对三阴性乳腺癌(TNBC)患者具有预后价值的观点,此前尚未在大型队列中进行研究。我们使用深度学习(DL)框架来量化数字化全切片图像中苏木精和伊红染色的 LNs 的形态特征。从 345 名乳腺癌患者中,评估了 5228 个腋窝 LNs,包括癌旁和受累的 LNs。开发了通用多尺度 DL 框架来捕获和量化生发中心(GCs)和窦。Cox 回归比例风险模型测试了 smuLymphNet 捕获的 GC 和窦定量与远处无转移生存(DMFS)之间的关联。smuLymphNet 分别捕获 GC 和窦的 Dice 系数为 0.86 和 0.74,与病理学家之间的 Dice 系数 0.66(GC)和 0.60(窦)相当。在含有 GCs 的 LNs 中,smuLymphNet 捕获的窦增多(p<0.001)。在 LN 阳性的 TNBC 患者中,smuLymphNet 捕获的 GC 保留了临床相关性,这些患者的无癌 LNs 平均有≥2 个 GC,DMFS 更长(危险比[HR]=0.28,p=0.02),并将 GC 的预后价值扩展到 LN 阴性的 TNBC 患者(HR=0.14,p=0.002)。在 Guy's 医院的队列中,受累 LNs 中 smuLymphNet 捕获的扩大窦与 LN 阳性的 TNBC 患者的 DMFS 更好相关(多变量 HR=0.39,p=0.039),在 95 例 LN 阳性的荷兰 N4plus 试验中的 TNBC 患者中与远处无复发生存相关(HR=0.44,p=0.024)。对 LN 阳性天津 TNBC 患者(n=85)的 LNs 中包膜下窦的启发式评分交叉验证了扩大的窦与较短的 DMFS 之间的关联(受累 LNs:HR=0.33,p=0.029 和无癌 LNs:HR=0.21,p=0.01)。smuLymphNet 可以可靠地定量反映与癌症相关反应的 LN 形态特征。我们的发现进一步加强了评估 LN 特性对 TNBC 患者预后的价值,超出了对转移灶检测的评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/7296dc557807/PATH-260-376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/9b3125182bd4/PATH-260-376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/998297d4dc58/PATH-260-376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/762f41cd9d63/PATH-260-376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/10f85efb7702/PATH-260-376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/7296dc557807/PATH-260-376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/9b3125182bd4/PATH-260-376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/998297d4dc58/PATH-260-376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/762f41cd9d63/PATH-260-376-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/10f85efb7702/PATH-260-376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1665/10720675/7296dc557807/PATH-260-376-g003.jpg

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[7]
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[9]
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[10]
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本文引用的文献

[1]
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology.

Nat Cancer. 2022-9

[2]
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EBioMedicine. 2021-8

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EBioMedicine. 2021-8

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Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication.

NPJ Breast Cancer. 2021-7-5

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