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一种用于评估TMEM(一种转移预后生物标志物)的自动化定量数字病理学方法的验证。

Validation of an Automated Quantitative Digital Pathology Approach for Scoring TMEM, a Prognostic Biomarker for Metastasis.

作者信息

Entenberg David, Oktay Maja H, D'Alfonso Timothy, Ginter Paula S, Robinson Brian D, Xue Xiaonan, Rohan Thomas E, Sparano Joseph A, Jones Joan G, Condeelis John S

机构信息

Department of Anatomy and Structural Biology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA.

Gruss-Lipper Biophotonics Center, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA.

出版信息

Cancers (Basel). 2020 Mar 31;12(4):846. doi: 10.3390/cancers12040846.

Abstract

Metastasis causes ~90% of breast cancer mortality. However, standard prognostic tests based mostly on proliferation genes do not measure metastatic potential. Tumor MicroEnvironment of Metastasis (TMEM), an immunohistochemical biomarker for doorways on blood vessels that support tumor cell dissemination is prognostic for metastatic outcome in breast cancer patients. Studies quantifying TMEM doorways have involved manual scoring by pathologists utilizing static digital microscopy: a labor-intensive process unsuitable for use in clinical practice. We report here a validation study evaluating a new quantitative digital pathology (QDP) tool (TMEM-DP) for identification and quantification of TMEM doorways that closely mimics pathologists' workflow and reduces pathologists' variability to levels suitable for use in a clinical setting. Blinded to outcome, QDP was applied to a nested case-control study consisting of 259 matched case-control pairs. Sixty subjects of these were manually scored by five pathologists, digitally recorded using whole slide imaging (WSI), and then used for algorithm development and optimization. Validation was performed on the remainder of the cohort. TMEM-DP shows excellent reproducibility and concordance and reduces pathologist time from ~60 min to ~5 min per case. Concordance between manual scoring and TMEM-DP was found to be >0.79. These results show that TMEM-DP is capable of accurately identifying and scoring TMEM doorways (also known as MetaSite score) equivalent to pathologists.

摘要

转移导致约90%的乳腺癌患者死亡。然而,主要基于增殖基因的标准预后检测并不能衡量转移潜能。转移瘤微环境(TMEM)是一种免疫组化生物标志物,用于标记支持肿瘤细胞播散的血管通道,对乳腺癌患者的转移结局具有预后价值。以往对TMEM通道进行量化的研究需要病理学家利用静态数字显微镜进行人工评分:这是一个劳动密集型过程,不适用于临床实践。我们在此报告一项验证研究,评估一种新的定量数字病理学(QDP)工具(TMEM-DP),用于识别和量化TMEM通道,该工具紧密模拟病理学家的工作流程,并将病理学家评分的变异性降低到适合临床应用的水平。在对结果不知情的情况下,将QDP应用于一项巢式病例对照研究,该研究由259对匹配的病例对照组成。其中60名受试者由五名病理学家进行人工评分,使用全切片成像(WSI)进行数字记录,然后用于算法开发和优化。对队列中的其余受试者进行验证。TMEM-DP显示出极佳的可重复性和一致性,将病理学家对每个病例的评分时间从约60分钟减少到约5分钟。发现人工评分与TMEM-DP之间的一致性>0.79。这些结果表明,TMEM-DP能够准确识别和评分TMEM通道(也称为MetaSite评分),与病理学家的评分相当。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ed9/7226227/290215ef3a49/cancers-12-00846-g001.jpg

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