Significance of nuclear morphometry on fine needle aspirates of breast lesions

Abstract

Background: FNAC of the breast, although effective for the diagnosis of breast lesions is largely subjective and a minority of cases cannot be classified as benign or malignant due to the morphological overlap. This hinders a definite diagnosis which may sometimes lead to unnecessary surgical biopsy. Morphometry in combination with FNAC is one such method of improving the diagnosis. Objective: To study the nuclear morphology with regard to nuclear diameter; nuclear area; coefficient of variation of nuclear area; nuclear/cytoplasmic ratio and the ratio of largest to smallest nuclear diameter (L:S ratio) on all breast aspirates (after histopathology correlation) performed at the Department of Pathology, MVJ MC and RH in a two year period. Statistical analysis was done to find out the significance of the five nuclear parameters in the benign and malignant categories. Methods: A total of 60 patients with a history of breast mass referred for FNAC to the Department of Pathology, MVJ MC &RH were taken for the study. Period of study: 2 years - August 2010 and July 2012. Morphometric analysis was done on Haematoxylin & Eosin stained aspirates using the Image J Morphometric Software for image processing and analysis developed by National Institutes of Health, USA. The five parameters were measured on 100 cells spread evenly on the slide surface. Correlation of results with histopathology was done using it as the gold standard. Any discrepancy in preformed cytological diagnosis was rectified after correlation. Statistical analysis was done using Student t-Test and one way ANOVA. Results: In this study, all the nuclear parameters were found to be significantly higher in the malignant lesions when compared to benign lesions (p<0.0001). Conclusion: Interactive computerized nuclear morphometry is an efficient and successful tool in distinguishing benign and malignant lesions. When faced with an inconclusive diagnosis of aspirates of breast masses, image analysis can help in the further classification of such lesions providing a more appropriate triage for surgical biopsy.