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:ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations.
ONEST (Observers Needed to Evaluate Subjective Tests) Analysis of Stromal Tumour-Infiltrating Lymphocytes (sTILs) in Breast Cancer and Its Limitations.
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TIL(肿瘤浸润淋巴细胞)反映抗肿瘤免疫。对组织病理标本的 TIL 评估会受到多种因素影响,并存在重复性问题。ONEST(评估主观检测所需观察者人数)可用于确定可靠估算 TIL 分类观察者间一致性所需的观察者数量;此前尚未将其用于 TIL 评估。研究者使用第一作者开发的开源软件,对两项既往研究中的乳腺癌 TIL 定量数据进行 ONEST 分析。一项为重复性研究,涉及 49 例乳腺癌;第一轮有 23 名、第二轮有 14 名病理学家参与。另一项研究包括 100 例病例和 9 名病理学家。除估算所需观察者人数外,研究还考察了影响 ONEST 结果的其他因素。分析显示,通常需要 6–9 名观察者(范围 2–11 名)才能稳健估算重复性。此外,观察者人数及经验、数值分布是否集中在两端附近,以及分类中的离群值均会影响结果。鉴于 ONEST 简便且可能提供有用信息,我们建议将其纳入新的重复性分析。
Tumour-infiltrating lymphocytes (TILs) reflect antitumour immunity. Their evaluation of histopathology specimens is influenced by several factors and is subject to issues of reproducibility. ONEST (Observers Needed to Evaluate Subjective Tests) helps in determining the number of observers that would be sufficient for the reliable estimation of inter-observer agreement of TIL categorisation.
This has not been explored previously in relation to TILs. ONEST analyses, using an open-source software developed by the first author, were performed on TIL quantification in breast cancers taken from two previous studies. These were one reproducibility study involving 49 breast cancers, 23 in the first circulation and 14 pathologists in the second circulation, and one study involving 100 cases and 9 pathologists.
In addition to the estimates of the number of observers required, other factors influencing the results of ONEST were examined. The analyses reveal that between six and nine observers (range 2-11) are most commonly needed to give a robust estimate of reproducibility.
In addition, the number and experience of observers, the distribution of values around or away from the extremes, and outliers in the classification also influence the results. Due to the simplicity and the potentially relevant information it may give, we propose ONEST to be a part of new reproducibility analyses.
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