Breast Ultrasound Texture Features of Family History of Breast Cancer and Contraceptive Use in Nigerian Younger Women
Abstract Background: The impact of family history of breast cancer (FHBC) on the texture features of the breast is poorly understood. We sought to examine the effect of FHBC on the image texture features of breast sonograms of young asymptomatic women. Methods: This study estimated texture features in breast sonograms with ImageJ software in a cohort of 360 young asymptomatic women aged 19–29 years. The sonographic assessment was performed using a digital color Doppler ultrasonic diagnostic instruments (Xuzhou Kaixin Electronic Instrument: Model – DCU7; SN-2331531). Texture analysis of each region of interest was performed using the gray-level co-occurrence matrix plugin of the ImageJ software. An independent samples t -test was used to compare the texture features of the breast in women with FHBC and the control. Results: Tissue homogeneity and uniformity were significantly lower in women with FHBC ( P < 0.05), while tissue complexity (entropy) was significantly higher in subjects with FHBC ( P = 0.02). Correlation and contrast were statistically the same between these groups contraceptive use showed no statistical association with breast image texture features ( P > 0.05). Conclusion: In this study, breast tissue complexity, homogeneity, and uniformity were affected by FHBC. Breast texture features should be considered when assessing risk in women with FHBC.
Authors
- Akpama E. Egong
- Akwa Egom Erim (ORCID: https://orcid.org/0000-0002-5025-4098)
- Nneoyi Onen Egbe
- Ernest Ekpo (ORCID: https://orcid.org/0000-0002-6851-0969)
- Immanuel Atauyo Akpauso
- Precious Onwugamba
- Chimezie G. Akwaeze
- Stephen Clement Ovai
Institutions
- The University of Sydney (AU)
- University of Calabar (NG)
- Zankli Medical Centre (NG)
- University of Health Sciences Antigua (AG)
Publication Details
- Journal
- Journal of Medical Ultrasound
- Published
- 2026-05-16
- DOI
- https://doi.org/10.4103/jmu.jmu-d-25-00004
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00