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培训病理学家评估乳腺癌中的间质肿瘤浸润淋巴细胞可协同临床护理和科学研究。

Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.

机构信息

Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.

Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, US Food and Drug Administration, Silver Spring, MD, USA.

出版信息

Histopathology. 2024 May;84(6):915-923. doi: 10.1111/his.15140. Epub 2024 Mar 3.

Abstract

A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.

摘要

越来越多的研究支持乳腺癌间质肿瘤浸润淋巴细胞(TIL)密度是一种强大的预后和预测生物标志物。乳腺癌间质 TIL 密度定量的金标准是病理学家使用苏木精和伊红染色切片进行的视觉评估。人工智能/机器学习算法正在开发中,以实现间质 TIL 评分过程的自动化,并且必须针对病理学家视觉评估等参考标准进行验证。视觉 TIL 评估可能存在显著的观察者间变异性。为了提高观察者间的一致性,美国食品和药物管理局的监管科学专家与国际学术病理学家合作,创建了一个免费的在线继续医学教育(CME)课程,该课程使用具有专家评论的互动格式培训病理学家评估乳腺癌间质 TIL。在这里,我们描述并提供了这个 CME 课程的用户指南,其内容旨在提高病理学家在评分乳腺癌 TIL 方面的准确性。我们还建议随后采取步骤,通过熟练测试将知识转化为临床实践。

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