Jaganath Rajesh, Angeletti Cesar, Levenson Richard, Rimm David L
Department of Pathology, Yale University School of Medicine, New Haven, Connecticut, USA.
Cancer. 2004 Jun 25;102(3):186-91. doi: 10.1002/cncr.20302.
Although cytologic evaluation of urine specimens is a standard procedure in the diagnosis and follow-up of bladder carcinoma, its sensitivity and specificity are low. Cytopathologic diagnoses are driven primarily by spatial relations or morphology. Although color enhances the pathologist's perception of the specimen, spectral information plays a minimal role in diagnostic processes. Recently, methods have been developed to capture and analyze spectral information from clinical specimens. In the current study, the authors determined the classification value of spectral information by testing its ability to discriminate between malignant and benign urothelial cells in cytology specimens.
Multiple images of benign urothelial cells (n = 39) and urothelial carcinoma cells (n = 35) were collected at serial wavelengths using a liquid crystal tunable optical filter and composited into a mosaic using ENVI (Environment for Visualizing Images) software. Through minimum noise fractionation and principal component analysis, the spectral information in the mosaic was compressed into a 29-dimensional scatter plot. The data generated were analyzed using visual and spectral end member extraction on both the original data set and a second independent data set (test set).
One area of spectral clustering in the scatter plot segmented with carcinoma cells exclusively (100% specific), but was not present in every cell (approximately 50%), which may indicate that these spectral profiles are present in a subpopulation of malignant cells or at specific points of their cell cycle. Using ENVI algorithms, the authors found that a particular classification spectrum (end member 9) and its closest relatives identified malignant cell clusters, with a sensitivity and specificity that reached 82% and 81%, respectively. To validate this mechanism in a test set, a second mosaic comprised of 15 benign and 15 malignant clusters was analyzed using end member 9, resulting in a combined sensitivity and specificity of 73%.
The results of the current study demonstrate that spectral information, in the complete absence of morphologic or spatial information, allows discrimination of benign and malignant urothelial cells in routine urine cytology specimens. The authors believe that this novel technology, combined with spatial analysis, has the potential to serve as an ancillary test for improved detection of bladder carcinoma.
尽管尿液标本的细胞学评估是膀胱癌诊断和随访的标准程序，但其敏感性和特异性较低。细胞病理学诊断主要由空间关系或形态学驱动。虽然颜色可增强病理学家对标本的感知，但光谱信息在诊断过程中所起的作用极小。最近，已开发出从临床标本中捕获和分析光谱信息的方法。在本研究中，作者通过测试光谱信息区分细胞学标本中恶性和良性尿路上皮细胞的能力，确定了其分类价值。
使用液晶可调谐光学滤波器在一系列波长下收集良性尿路上皮细胞（n = 39）和尿路上皮癌细胞（n = 35）的多张图像，并使用ENVI（图像可视化环境）软件将其合成一幅镶嵌图。通过最小噪声分离和主成分分析，将镶嵌图中的光谱信息压缩成一个29维散点图。对生成的数据在原始数据集和第二个独立数据集（测试集）上进行视觉和光谱端元提取分析。
散点图中的一个光谱聚类区域仅由癌细胞分割（特异性为100%），但并非每个细胞中都存在（约50%），这可能表明这些光谱特征存在于恶性细胞亚群中或其细胞周期的特定阶段。使用ENVI算法，作者发现特定的分类光谱（端元9）及其最接近的相关光谱可识别恶性细胞簇，敏感性和特异性分别达到82%和81%。为在测试集中验证这一机制，使用端元9分析了由15个良性和15个恶性簇组成的第二幅镶嵌图，综合敏感性和特异性为73%。
本研究结果表明，在完全没有形态学或空间信息的情况下，光谱信息能够区分常规尿液细胞学标本中的良性和恶性尿路上皮细胞。作者认为，这项新技术与空间分析相结合，有可能作为一种辅助检测手段，用于改善膀胱癌的检测。
