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胸腺内异位生发中心准确预测胸腺瘤切除术后重症肌无力的预后。

Ectopic germinal centers in the thymus accurately predict prognosis of myasthenia gravis after thymectomy.

机构信息

Translational Immunology Research Program, University of Helsinki, Helsinki, Finland.

Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

出版信息

Mod Pathol. 2022 Sep;35(9):1168-1174. doi: 10.1038/s41379-022-01070-2. Epub 2022 Mar 25.

Abstract

The ability of thymic histopathology to predict the long-term impact of thymectomy in non-thymomatous myasthenia gravis (NTMG) is mainly uncharted. We applied digital pathology to quantitatively characterize differences of thymic histology between early-onset (EOMG) and late-onset MG (LOMG) and to investigate the role of thymic changes for thymectomy outcomes in MG. We analyzed 83 thymic H&E slides from thymectomized NTMG patients, of which 69 had EOMG and 14 LOMG, using digital pathology open-access software QuPath. We compared the results to the retrospectively assessed clinical outcome at two years after thymectomy and at the last follow-up visit where complete stable remission and minimal use of medication were primary outcomes. The automated annotation pipeline was an effective and reliable way to analyze thymic H&E samples compared to manual annotation with mean intraclass correlation of 0.80. The ratio of thymic tissue to stroma and fat was increased in EOMG compared to LOMG (p = 8.7e-07), whereas no difference was observed in the ratio of medulla to cortex between these subtypes. AChRAb seropositivity correlated with the number of ectopic germinal centers (eGC; p = 0.00067) but not with other histological areas. Patients with an increased number of eGCs had better post-thymectomy outcomes at two years after thymectomy (p = 0.0035) and at the last follow-up (p = 0.0267). ROC analysis showed that eGC area predicts thymectomy outcome in EOMG with an AUC of 0.79. Digital pathology can thus help in providing a predictive tool to the clinician, the eGC number, to guide the post-thymectomy treatment decisions in EOMG patients.

摘要

胸腺组织病理学预测非胸腺瘤型重症肌无力(NMGT)患者胸腺切除术长期疗效的能力尚不清楚。我们应用数字病理学方法定量分析早发型(EOMG)和晚发型 MG(LOMG)患者胸腺组织学差异,并探讨胸腺变化对 MG 患者胸腺切除术结果的作用。我们使用数字病理学开放获取软件 QuPath 分析了 83 例 NMGT 患者的胸腺苏木精-伊红(H&E)切片,其中 69 例为 EOMG,14 例为 LOMG。我们将结果与术后 2 年和最后一次随访的回顾性评估临床结果进行比较,完全稳定缓解和最小药物使用是主要结果。与手动注释相比,自动注释流水线是一种有效且可靠的分析胸腺 H&E 样本的方法,其平均组内相关系数为 0.80。与 LOMG 相比,EOMG 的胸腺组织与基质和脂肪的比例增加(p=8.7e-07),而这些亚型的皮质与髓质比例无差异。AChRAb 阳性与异位生发中心(eGC;p=0.00067)的数量相关,但与其他组织学区域无关。eGC 数量增加的患者在胸腺切除术后 2 年(p=0.0035)和最后一次随访时(p=0.0267)的术后效果更好。ROC 分析显示,eGC 面积对 EOMG 的胸腺切除术结果具有预测价值,AUC 为 0.79。因此,数字病理学可以帮助临床医生提供一种预测工具,即 eGC 数量,以指导 EOMG 患者的术后治疗决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cec9/9424113/8a7f6f1ac184/41379_2022_1070_Fig1_HTML.jpg

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