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首页> 外文期刊>Diagnostic cytopathology >Morphometric analysis of atypical glandular cells correctly classifies normal, reactive, and atypical cells in cervical smears
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Morphometric analysis of atypical glandular cells correctly classifies normal, reactive, and atypical cells in cervical smears

机译:非典型腺体细胞的形态学分析正确对宫颈涂片中的正常,反应性和非典型细胞进行了分类

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Abstract The 2014 Bethesda System diagnostic criteria for atypical glandular cells (AGC) aid in the classification of atypical cells in cervical cytology. Anyway, AGC diagnosis remains challenging, due to low frequencies of this finding (approximately 0.5%‐1% of Pap test results), abundance of AGC mimics, and significant interobserver variability. We developed an algorithm based on nuclear areas parameter that can help to differentiate AGC from Normal and Reactive glandular cells. Nuclear areas and perimeters were measured on 16 Pap smears with AGC and 18 with Reactive glandular cells of women aged between 30 and 77. Glandular cells from nonpathological Pap smears were used as controls. For each case, the means, medians, standard deviations, and the minimum and maximum values of both nuclear areas and perimeters of the cells of interest were calculated. The nuclear area analysis showed a 100% specificity in discriminating Normal from Altered cells (either Reactive or AGC), whereas the nuclear perimeter analysis showed a lower specificity (87.5%). Both nuclear area and perimeter variability analysis resulted in high specificity values in distinguishing Reactive cells from AGC. Therefore, a stepwise two‐step algorithm using nuclear areas to discriminate Normal from Altered cells, and nuclear area variability to distinguish Reactive from AGC, allowed us to reliably classify the cells into these three categories. The morphometric analysis of nuclear area is a valuable and reliable aid in AGC diagnosis and standardization, easily integrable into common automatic algorithms.
机译:摘要2014年贝塞斯达系统诊断标准(AGC)辅助宫颈细胞学中非典型细胞分类的辅助标准。无论如何,由于该发现的低频(约0.5%-1%的PAP测试结果的0.5%-1%),AGC模仿的丰度和显着的Interobserver变异性,AGC诊断仍然具有挑战性。我们开发了一种基于核领域参数的算法,可以帮助区分AGC从正常和反应性腺体细胞。核区域和周长以16个PAP涂片测量,AGC和18款,其中18岁的女性的反应腺细胞在30到77岁之间。来自非嗜可能的PAP涂片的腺细胞被用作对照。对于每种情况,计算核区域和感兴趣细胞细胞周期的手段,中位数,标准偏差和最小值和最大值。核面积分析表明,在从改变的细胞(反应性或AGC)中鉴别正常的核区域分析表现出100%的特异性,而核周边分析显示出较低的特异性(87.5%)。核面积和周边可变性分析导致从AGC区分反应性细胞的高特异性值。因此,使用核区域的逐步两步算法以区分从改变的细胞区分,以及从AGC区分反应性的核区域可变性,使我们能够可靠地将细胞分类为这三个类别。核面积的形态学分析是AGC诊断和标准化的有价值且可靠的辅助,易于集成为常见的自动算法。

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