Significance of nuclear morphometry in cytological aspirates of breast masses.
BACKGROUND
Breast carcinoma is the most common malignancy globally. Cytological evaluation in breast lesions is largely subjective. Gradual progression of cells from normal to invasive involves nuclear changes that need to be viewed objectively.


OBJECTIVE
This study aims to apply nuclear morphometry on cytological breast aspirates. It evaluates its utility in differentiating benign vs. malignant lesions and correlates it with cytologic grading in malignant cases.


METHODS
Nuclear morphometric parameters of malignant and benign cases were compared. Parameters of malignant cases were correlated with cytologic grading.


METHODS
Cytology was used to categorize aspirates from breast lumps into malignant (53 cases) and benign (29 cases). One hundred cells per case in both groups were mapped on DEBEL Cytoscan and six geometrical and three textural parameters obtained were compared. In malignant cases, morphometry was correlated with Robinson's cytologic grading, which was further correlated in tissue sections (45 cases) with modified Scarff-Bloom-Richardson histologic grading.


METHODS
Students "t"-test was applied for comparison between benign and malignant cases. One-way ANOVA followed by Bonferroni's post hoc comparison was applied to compare the three cytologic grades. Results were considered significant when P<0.05.


RESULTS
Nuclear morphometry successfully differentiated between benign and malignant aspirates and correlated significantly with cytologic grades. Morphometry was especially useful in the diagnosis of atypical ductal hyperplasia and ductal carcinoma in situ. Useful parameters were mean nuclear area, long axis, short axis and total run length. Cytohistologic correlation was 83.3%, 88.9% and 88.9% for cytological grades 1, 2 and 3 respectively.


CONCLUSIONS
Nuclear morphometry was thus a useful objective tool in the evaluation of breast masses.
Department of Pathology, Army College of Medical Sciences, Delhi Cantt., Delhi, India.
Shivani
S
Kalhan
Suparna
S
Dubey
Sonia
S
Sharma
Sharmila
S
Dudani
Preeti
Monika
M
Dixit