Suppr超能文献

利用光谱空间分析检测细胞学标本中的恶性肿瘤。

Detection of malignancy in cytology specimens using spectral-spatial analysis.

作者信息

Angeletti Cesar, Harvey Neal R, Khomitch Vitali, Fischer Andrew H, Levenson Richard M, Rimm David L

机构信息

Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.

出版信息

Lab Invest. 2005 Dec;85(12):1555-64. doi: 10.1038/labinvest.3700357.

Abstract

Despite low sensitivity (around 60%), cytomorphologic examination of urine specimens represents the standard procedure in the diagnosis and follow-up of bladder cancer. Although color is information-rich, morphologic diagnoses are rendered almost exclusively on the basis of spatial information. We hypothesized that quantitative assessment of color (more precisely, of spectral properties) using liquid crystal-based spectral fractionation, combined with genetic algorithm-based spatial analysis, can improve the accuracy of traditional cytologic examination. Images of various cytological specimens were collected every 10 nm from 400 to 700 nm to create an image stack. The resulting data sets were analyzed using the Los Alamos-developed GENetic Imagery Exploitation (GENIE) package, a hybrid genetic algorithm that segments (classifies) images using automatically 'learned' spatio-spectral features. In an evolutionary fashion, GENIE generates a series of algorithms or 'chromosomes', keeping the one with best fitness with respect to a user-defined training set. First, we tested the system to determine if it could recognize malignant cells using artificial cytology specimens constructed to completely avoid the requirement for human interpretation. GENIE was able to differentiate malignant from benign cells and to estimate their relative proportions in controlled mixtures. We then tested the system on routine cytology specimens. When targeted to detect malignant urothelial cells in cytology specimens, GENIE showed a combined sensitivity and specificity of 85 and 95%, in samples drawn from two separate institutions over a span of 4 years. When trained on cases initially diagnosed as 'atypical' but with unequivocal follow-up by biopsy, surgical specimen or cytology, GENIE showed efficiency superior to the cytopathologist with respect to predicting the follow-up result in a cohort of 85 cases. We believe that, in future, this type of methodology could be used as an ancillary test in cytopathology, in a manner analogous to immunostaining, in those situations when a definitive diagnosis cannot be rendered based solely on the morphology.

摘要

尽管灵敏度较低(约60%),但尿液标本的细胞形态学检查仍是膀胱癌诊断和随访的标准程序。虽然颜色包含丰富信息,但形态学诊断几乎完全基于空间信息。我们推测,使用基于液晶的光谱分离技术对颜色(更准确地说是光谱特性)进行定量评估,并结合基于遗传算法的空间分析,可以提高传统细胞学检查的准确性。从400至700纳米每隔10纳米收集各种细胞学标本的图像,以创建一个图像堆栈。使用洛斯阿拉莫斯国家实验室开发的遗传图像利用(GENIE)软件包对所得数据集进行分析,这是一种混合遗传算法,利用自动“学习”的空间光谱特征对图像进行分割(分类)。GENIE以进化方式生成一系列算法或“染色体”,保留与用户定义训练集适应性最佳的算法。首先,我们测试该系统,以确定它能否使用完全无需人工解读的人工细胞学标本识别恶性细胞。GENIE能够区分恶性细胞和良性细胞,并估计它们在受控混合物中的相对比例。然后,我们在常规细胞学标本上测试该系统。当针对细胞学标本中恶性尿路上皮细胞进行检测时,在来自两个不同机构、跨越4年的样本中,GENIE的综合灵敏度和特异性分别为85%和95%。当在最初诊断为“非典型”但经活检、手术标本或细胞学明确随访的病例上进行训练时,在一组85例病例中,GENIE在预测随访结果方面表现出优于细胞病理学家的效率。我们相信,未来在仅根据形态无法做出明确诊断的情况下,这种方法可以作为细胞病理学中的辅助检测方法,类似于免疫染色。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验