Literature DB >> 16200074

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

Cesar Angeletti1, Neal R Harvey, Vitali Khomitch, Andrew H Fischer, Richard M Levenson, David L Rimm.   

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.

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Year:  2005        PMID: 16200074     DOI: 10.1038/labinvest.3700357

Source DB:  PubMed          Journal:  Lab Invest        ISSN: 0023-6837            Impact factor:   5.662


  9 in total

Review 1.  Medical hyperspectral imaging: a review.

Authors:  Guolan Lu; Baowei Fei
Journal:  J Biomed Opt       Date:  2014-01       Impact factor: 3.170

2.  Intraoperative multispectral and hyperspectral label-free imaging: A systematic review of in vivo clinical studies.

Authors:  Jonathan Shapey; Yijing Xie; Eli Nabavi; Robert Bradford; Shakeel R Saeed; Sebastien Ourselin; Tom Vercauteren
Journal:  J Biophotonics       Date:  2019-04-29       Impact factor: 3.207

3.  Quantifying histological features of cancer biospecimens for biobanking quality assurance using automated morphometric pattern recognition image analysis algorithms.

Authors:  Joshua D Webster; Eleanor R Simpson; Aleksandra M Michalowski; Shelley B Hoover; R Mark Simpson
Journal:  J Biomol Tech       Date:  2011-09

4.  A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.

Authors:  Robert Pike; Guolan Lu; Dongsheng Wang; Zhuo Georgia Chen; Baowei Fei
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-14       Impact factor: 4.538

5.  Digital pathology and image analysis augment biospecimen annotation and biobank quality assurance harmonization.

Authors:  Bih-Rong Wei; R Mark Simpson
Journal:  Clin Biochem       Date:  2013-12-18       Impact factor: 3.281

6.  A novel precision-engineered microfiltration device for capture and characterisation of bladder cancer cells in urine.

Authors:  Marc Birkhahn; Anirban P Mitra; Anthony J Williams; Nancy J Barr; Eila C Skinner; John P Stein; Donald G Skinner; Yu-Chong Tai; Ram H Datar; Richard J Cote
Journal:  Eur J Cancer       Date:  2013-07-09       Impact factor: 9.162

7.  Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image analysis.

Authors:  Joshua D Webster; Aleksandra M Michalowski; Jennifer E Dwyer; Kara N Corps; Bih-Rong Wei; Tarja Juopperi; Shelley B Hoover; R Mark Simpson
Journal:  J Pathol Inform       Date:  2012-04-18

8.  Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery.

Authors:  Laura E Boucheron; Zhiqiang Bi; Neal R Harvey; Bs Manjunath; David L Rimm
Journal:  BMC Cell Biol       Date:  2007-07-10       Impact factor: 4.241

9.  A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms.

Authors:  Cesar Angeletti
Journal:  J Pathol Inform       Date:  2018-04-20
  9 in total

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