基于 DNA 超分子水凝胶的保护性 NK 细胞储库用于增强三阴性乳腺癌治疗
Protective NK Cell Reservoir Based on DNA Supramolecular Hydrogel for Enhanced Triple-Negative Breast Cancer Therapy.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.
Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research.
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越来越多的研究支持乳腺肿瘤间质TIL密度是一个稳健的预后和预测生物标志物。乳腺肿瘤间质TIL密度定量的金标准是病理学家使用苏木精-伊红染色切片进行视觉评估。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,并且必须针对参考标准(如病理学家视觉评估)进行验证。视觉TIL评估可能存在显著的观察者间变异。为了提高观察者间一致性,美国食品药品监督管理局的监管科学专家与国际学术病理学家合作,创建了一个免费开放的在线继续医学教育课程,以互动形式并附专家评论来培训病理学家评估乳腺肿瘤间质TIL。在此,我们描述并提供该继续医学教育课程的用户指南,其内容旨在提高病理学家对乳腺肿瘤TIL评分的准确性。我们还建议后续步骤,通过能力验证测试将知识转化为临床实践。
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.
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