Detection of malignancy in cytology specimens using spectral–spatial analysis

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.

[1]  P. Bartels,et al.  Computer-based diagnostic analysis of cells in the urinary sediment. , 1980, The Journal of urology.

[2]  L G Koss,et al.  Bladder cancer diagnosis by computer image analysis of cells in the sediment of voided urine using a video scanning system. , 1986, Analytical and quantitative cytology and histology.

[3]  W. Catalona,et al.  Urothelial Tumors of the Urinary Tract , 1992 .

[4]  D. Johnston,et al.  DNA image analysis of urinary cytology: prediction of recurrent transitional cell carcinoma. , 1996, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc.

[5]  F. Meyer,et al.  Diagnostic accuracy of urinary cytology, and deoxyribonucleic acid flow cytometry and cytology on bladder washings during followup for bladder tumors. , 1997, The Journal of urology.

[6]  V. Tut,et al.  Does voided urine cytology have biological significance? , 1998, British journal of urology.

[7]  H. G. van der Poel,et al.  Bladder wash cytology, quantitative cytology, and the qualitative BTA test in patients with superficial bladder cancer. , 1998, Urology.

[8]  D L Farkas,et al.  Non-invasive image acquisition and advanced processing in optical bioimaging. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[9]  Melanie Mitchell,et al.  Investigation of image feature extraction by a genetic algorithm , 1999, Optics + Photonics.

[10]  A. Renshaw Subclassifying atypical urinary cytology specimens , 2000, Cancer.

[11]  Neal R. Harvey,et al.  GENIE: a hybrid genetic algorithm for feature classification in multispectral images , 2000, SPIE Optics + Photonics.

[12]  P. Dey,et al.  DNA flow cytometry and bladder irrigation cytology in detection of bladder carcinoma , 2001, Diagnostic cytopathology.

[13]  Neal R. Harvey,et al.  Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[14]  Eva M. Wojcik,et al.  The effects of the current World Health Organization/International Society of Urologic Pathologists bladder neoplasm classification system on urine cytology results , 2002, Cancer.

[15]  Neal R. Harvey,et al.  Investigation of automated feature extraction using multiple data sources , 2003, SPIE Defense + Commercial Sensing.

[16]  David L Rimm,et al.  Diagnostic classification of urothelial cells in urine cytology specimens using exclusively spectral information , 2004, Cancer.

[17]  J. Driscoll,et al.  Trypan blue dye uptake and lactate dehydrogenase in adult rat hepatocytes—Freshly isolated cells, cell suspensions, and primary monolayer cultures , 1981, In Vitro.