Background Fine-needle aspiration cytology (FNAC) is one of the reliable methods in diagnosing breast cancers. Morphometric studies are done in benign and malignant neoplasms of various organs by using software, which measures cellular, cytoplasmic, and nuclear parameters. Nuclear parameters define the behavior of the neoplasm. This study aims to evaluate nuclear morphometry parameters in aspirated smears of breast lesions and determine the association between cytological findings with nuclear morphometry parameters. Methodology It's a retrospective cytology study from July 2020 to June 2022 conducted at a tertiary health care center in Kolar, Karnataka, India. The FNAC smears of breast mass were analyzed cytologically and were subjected to nuclear morphometry study. Nuclear parameters such asnuclear area, nuclear perimeter, nuclear Feret diameter, minimum Feret, and shape factor were captured inZen software (Zeiss, Oberkochen, Germany) and ImageJ software (National Institutes of Health,Bethesda, MD, USA;Laboratory for Optical and Computational Instrumentation [LOCI], University of Wisconsin-Madison, Madison, WI, USA). The association between nuclear morphometric findings and cytological findings was noted. A descriptive statistical analysis was done. Results Sixty cases of mass in the breast were considered for the study of which37 cases were benign and 23 were malignant. Nuclear morphometry parameters such asnuclear area, nuclear perimeter, nuclear Feret diameter, minimum Feret, and shape factor for benign breast lesions were 25.16 ± 3.2 µm2, 21.58 ± 1.89 µm, 6.5± 0.94 µm, 4.87 ± 0.50 µm, and 0.92± 0.02, respectively, and for malignant breast cases were 46.57 ± 12.24 µm2, 27.53 ± 3.26 µm, 10.08 ± 1.18 µm, 6.49± 0.88 µm, and 0.93 ± 0.01, respectively. The association of all nuclear parameters between benign and malignant lesions was statistically significant (P= 0.001). Conclusions Nuclear morphometric study in breast lesions is a concept that supplements FNAC findings in differentiating benign from malignant lesions.
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