BACKGROUND: 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. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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. Copyright 2004 American Cancer Society.
BACKGROUND: 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. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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. Copyright 2004 American Cancer Society.