QUANTITATIVE NUCLEAR MORPHOMETRIC FEATURES ASSOCIATED WITH BENIGN AND MALIGNANT BREAST LESIONS

Brian O. Otieno; Mercy W. Kamau; David K. Mwangi

doi:10.69980/bs.v12i2.6808

Brian O. Otieno Department of Pathology, University of Nairobi, Nairobi, Kenya
Mercy W. Kamau Department of Medical Laboratory Sciences, Kenyatta University, Nairobi, Kenya
David K. Mwangi Department of Biomedical Sciences, Moi University, Eldoret, Kenya

DOI: https://doi.org/10.69980/bs.v12i2.6808

Keywords: Breast lesions, Nuclear morphology, Morphometry, Benign and malignant diagnosis, Fine-needle aspiration cytology

Abstract

Quantitative evaluation of nuclear morphology may improve the distinction between benign and malignant breast lesions by capturing structural differences in nuclear size, texture, and contour. A cross-sectional secondary-data analysis was conducted using the Breast Cancer Wisconsin (Diagnostic) dataset. Nuclear morphological characteristics derived from breast fine-needle aspiration images were evaluated, including radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension. Descriptive analysis, group comparisons, correlation analysis, and binary logistic regression were used to examine morphological patterns associated with diagnosis. Malignant breast samples showed a consistent pattern of larger nuclear dimensions and greater contour irregularity than benign samples. Radius, perimeter, area, compactness, concavity, and concave points demonstrated clear diagnostic separation, whereas fractal dimension showed limited distinction. Strong correlations were observed among size-related features and among contour-related characteristics, indicating substantial overlap between related morphometric measures. After adjustment for correlated predictors, nuclear radius, texture, smoothness, and concavity remained independently associated with malignant diagnosis. Quantitative nuclear morphology provides useful information for differentiating benign and malignant breast lesions. Features reflecting nuclear enlargement, surface variation, and contour irregularity appear particularly relevant to malignant diagnosis. These findings support the value of integrated morphometric assessment in breast cytology and provide a basis for further validation using histopathological, clinical, and molecular information.

Downloads

Download data is not yet available.

References

1.Abreu, N., Filipe, J., André, S., & Marques, J. C. (2020). Granular cell tumor of the breast: correlations between imaging and pathology findings. Radiologia Brasileira, 53(2), 105-111.
2.Baker, Q. B., Banat, S., Eaydat, E., & Alsmirat, M. (2018, October). Automated detection of benign and malignant in breast histopathology images. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-5). IEEE.
3.Cima, L., Kaya, H. A. N. D. A. N., Marchiò, C., Nishimura, R., Wen, H. Y., Fabbri, V. P., & Foschini, M. P. (2022). Triple-negative breast carcinomas of low malignant potential: review on diagnostic criteria and differential diagnoses. Virchows Archiv, 480(1), 109-126.
4.Cserni, G. (2020). Histological type and typing of breast carcinomas and the WHO classification changes over time. Pathologica, 112(1), 25.
5.Das, A., Nair, M. S., & Peter, S. D. (2020). Computer-aided histopathological image analysis techniques for automated nuclear atypia scoring of breast cancer: a review. Journal of digital imaging, 33(5), 1091-1121.
6.Feng, Y., Zhang, L., & Yi, Z. (2018). Breast cancer cell nuclei classification in histopathology images using deep neural networks. International journal of computer assisted radiology and surgery, 13(2), 179-191.
7.Fischer, E. G. (2020). Nuclear morphology and the biology of cancer cells. Acta cytologica, 64(6), 511-519.
8.Jamidi, S. K., Li, J. J., Aphivatanasiri, C., Chow, M. B., Chan, R. C., Ng, J. K., ... & Tse, G. M. (2021). Papillary lesions of the breast: a systematic evaluation of cytologic parameters. Cancer Cytopathology, 129(8), 649-661.
9.Kala, C., Athar, M., Kala, S., Khan, L., Jauhari, R. K., & Satsangi, A. (2019). Clinical and cyto-morphological characterization of triple negative breast cancer. Journal of Cytology, 36(2), 84.
10.Kashyap, A., Jain, M., Shukla, S., & Andley, M. (2018). Role of nuclear morphometry in breast cancer and its correlation with cytomorphological grading of breast cancer: A study of 64 cases. Journal of cytology, 35(1), 41.
11.Katayama, A., Toss, M. S., Parkin, M., Ellis, I. O., Quinn, C., & Rakha, E. A. (2022). Atypia in breast pathology: what pathologists need to know. Pathology, 54(1), 20-31.
12.Katayama, A., Toss, M. S., Parkin, M., Sano, T., Oyama, T., Quinn, C. M., ... & Rakha, E. A. (2022). Nuclear morphology in breast lesions: refining its assessment to improve diagnostic concordance. Histopathology, 80(3), 515-528.
13.Li, J., Zhou, Y., Li, Y., & Liu, Y. (2021). Nuclear Morphological Characteristics in Breast Cancer: Correlation With Hormone Receptor and Human Epidermal Growth Factor Receptor 2. Analytical Cellular Pathology, 2021(1), 3037993.
14.Ohashi, R., Hayama, A., Matsubara, M., Watarai, Y., Sakatani, T., Naito, Z., & Shimizu, A. (2018). Breast carcinoma with osteoclast-like giant cells: A cytological-pathological correlation with a literature review. Annals of Diagnostic Pathology, 33, 1-5.
15.Rajbongshi, N., Bora, K., Nath, D. C., Das, A. K., & Mahanta, L. B. (2018). Analysis of morphological features of benign and malignant breast cell extracted from fnac microscopic image using the pearsonian system of curves. Journal of cytology, 35(2), 99.
16.Rakha, E. A., Brogi, E., Castellano, I., & Quinn, C. (2022). Spindle cell lesions of the breast: a diagnostic approach. Virchows Archiv, 480(1), 127-145.
17.Saba, T., Khan, S. U., Islam, N., Abbas, N., Rehman, A., Javaid, N., & Anjum, A. (2019). Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images. Microscopy research and technique, 82(6), 775-785.
18.Sanati, S. (2019). Morphologic and molecular features of breast ductal carcinoma in situ. The American journal of pathology, 189(5), 946-955.
19.Stachs, A., Stubert, J., Reimer, T., & Hartmann, S. (2019). Benign breast disease in women. Deutsches Ärzteblatt International, 116(33-34), 565.
20.Sun, H., Ding, Q., & Sahin, A. A. (2023). Immunohistochemistry in the diagnosis and classification of breast tumors. Archives of pathology & laboratory medicine, 147(10), 1119-1132.
21.Talu, C. K., Leblebici, C., Ozturk, T. K., Hacihasanoglu, E., Koca, S. B., & Gucin, Z. (2018). Primary breast carcinomas with neuroendocrine features: clinicopathological features and analysis of tumor growth patterns in 36 cases. Annals of diagnostic pathology, 34, 122-130.
22.Tevatia, M. S., Mishra, P., Baranwal, A. K., Nichat, P. B., Shelly, D., Awasthi, S., & Sengupta, P. (2021). Primary breast tumors with mesenchymal morphology. Journal of Laboratory Physicians, 13(04), 362-367.
23.UCI Machine Learning. (n.d.). Breast Cancer Wisconsin (Diagnostic) data set [Data set]. Kaggle. https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data
24.Yu, C., Chen, H., Li, Y., Peng, Y., Li, J., & Yang, F. (2019). Breast cancer classification in pathological images based on hybrid features. Multimedia Tools and Applications, 78(15), 21325-21345.

QUANTITATIVE NUCLEAR MORPHOMETRIC FEATURES ASSOCIATED WITH BENIGN AND MALIGNANT BREAST LESIONS

Abstract

Downloads

References

Indexed & Abstracted In