Usefulness of Discriminant Analysis Using Nuclear Three-Dimensional Analysis in Atypical Cells Prepared from Bronchial Brushing Cytology Cases That Are Cytologically Challenging

Abstract

Objective: Since well-differentiated adenocarcinoma cells of the lung (G1 cancer cells) show mild atypia, their differentiation from benign columnar epithelial cells (benign cells) is often difficult based on morphology. We performed discriminant analysis to distinguish benign from malignant cells by measuring 3-dimensional (3D) changes in nuclear luminance. Study Design: Discriminant analysis of 231 atypical cells prepared by bronchial brushing cytology, which were difficult to morphologically classify as benign or malignant, was performed using 100 G1 cancer cells. One hundred benign cells of bronchial brushing cytology specimens served as controls. The number of pixels of the nucleus, the number of focus layers and the level of change in the coefficient of variation (CV) of nuclear luminance between layers (3D-CV) were used as analytic parameters, and benign cells were discriminated from malignant cells based on the Mahalanobis distance. Results: As a result of discriminant analysis using a cutoff value determined in the control group, about 90% of the atypical cells difficult to classify as benign or malignant could be classified. Conclusion: For cells difficult to morphologically classify as benign or malignant, discriminant analysis based on 3D changes in nuclear luminance may be useful. This method can provide objective parameters for cancer diagnosis.

Image quantification technology of the heterochromatin and euchromatin region for differential diagnosis in the lobular endocervical glandular hyperplasia

“…Washiya et al sought to discriminate normal columnar cells from adenocarcinoma cells of the lung using the difference of signal intensity relative to the position of the cross section through the nucleus. 11 This study reported that calculation of signal intensity relative to cross sections of the nucleus was useful for distinguishing normal columnar cells from adenocarcinoma cells of the lung. This suggests that the direction of nuclear observation is important during signal intensity extraction for discriminant analysis.…”

“…We therefore calculated the signal intensity in heterochromatin and euchromatin regions of the nucleus from both the luminal and the lateral faces and determined whether there was a difference in signal intensity. Washiya et al sought to discriminate normal columnar cells from adenocarcinoma cells of the lung using the difference of signal intensity relative to the position of the cross section through the nucleus . This study reported that calculation of signal intensity relative to cross sections of the nucleus was useful for distinguishing normal columnar cells from adenocarcinoma cells of the lung.…”

Significance of Binucleated Cells with Compression in Atypical Squamous Cells of Undetermined Significance

“…We focused on the 3-dimensional structures of nuclei in cell smear specimens and reported objective discrimination using 3-dimensional and discriminant analyses of nuclei [4][5][6] . The nuclei of ASC-US cases in initial cytology were 3-dimensionally analyzed using liquid-based cytology (LBC) specimens prepared by the ThinPrep method.…”

Discriminant Analysis between Malignant Mesothelioma and Reactive Mesothelium Using Nuclear Three-Dimensional Analysis Is Useful for Morphologically Suspicious Cases

“…We focused on the 3-dimensional structures of nuclei in cell smear specimens to investigate the objective discrimination of malignancy using the 3-dimensional analysis of nuclear luminance [15,16,17,18]. Individual nuclei in cell specimens were 3-dimensionally photographed for retrospective discrimination based on Mahalanobis distance using various parameters including pixel counts (area), number of focus layers (number of images with the nuclei microscopically and morphologically focused) and 3-dimensional variation in the coefficient of variation of nuclear luminance between the focus layers (3D-CV), facilitating a discriminant diagnosis between MM and RM [18].…”