PREVALENCE OF UNI-FOCAL BREAST CANCER ACCORDING TO THE QUADRANTS AND HISTOPATHOLOGICAL TYPE OF THE TUMORS
Abstract
Background: Breast cancer is one of the most common and deadly diseases in women, and its prevalence is rising. Breast mammography being the first tool in diagnosis of suspicious breast masses. The aim of this study was to determine the distribution of uni-focal Breast cancer according to different quadrants of the breast and histopathological type of the tumors.
Materials & Methods: This was a cross sectional observational study conducted from Jan 2021 to Sep 2022, at the breast center in Shar hospital located in Sulaymaniyah, the North of Iraq. Convenience sampling method was used and the study enrolled 140 female patients who had biopsy proven uni-focal Breast cancer. For each female patient, specific information were gathered from their data including their basic demographics and their pathological tumor profiles. The malignancy quadrants depending on mammography and ultrasound examinations were used to assess the site of quadrant specific breast cancer.
Results: In this study, the patients’ diagnostic age ranged between 25 to 91 years old, with a mean diagnosis range of 49.8 years. Among the included cases, 78 patients were premenopausal (56%) and 62 patients were post-menopausal (44%). The results of our study showed that the majority of tumors in uni-focal BCs were found in Upper Outer Quadrant +Axillary tail (60%). The tumor type according to the true-cut biopsy results showed that Invasive Ductal Carcinoma recorded highest incidence which was 122 cases (87.1%), followed by the Ductal Carcinoma Insitu 9 cases (6.4%), and Invasive Lobular Carcinoma 9 patients (6.4%).
Conclusion: The rate of breast cancer was slightly greater in premenopausal women than in postmenopausal women in this investigation. Our study revealed that the majority of uni-focal breast cancers are located in the upper outer quadrant and axillary tail.
Keywords
Full Text:
PDFReferences
Fahad Ullah, M. (2019). Breast Cancer: Current Perspectives on the Disease Status. In: Ahmad, A. (eds) Breast Cancer Metastasis and Drug Resistance.Adv Exp Med Biol 2019;1152:51-64. https://doi.org/10.1007/978-3-030-20301-6_4
Thomgkam J, Sukmak V, Klangnok P, editors. Application of Machine Learning Techniques to Predict Breast Cancer Survival. International Conference on Multi-disciplinary Trends in Artificial Intelligence; 2021: Springer. https://doi.org/10.1007/978-3-030-80253-0_13
Bright CJ, Rea DW, Francis A, Feltbower RG. Comparison of quadrant-specific breast cancer incidence trends in the United States and England between 1975 and 2013. Cancer Epidemiol 2016;44:186-194. https://doi.org/10.1016/j.canep.2016.08.019
Darbre PD. Recorded quadrant incidence of female breast cancer in Great Britain suggests a disproportionate increase in the upper outer quadrant of the breast. Anticancer Res 2005;25(3C):2543-50.
Tang Z, Ji Y, Zhang X, Xu W, Zhao L, Zhang J, et al. Primary Tumor Location is Associated with Prognosis for Women with Breast Cancer. Preprint from Research Square, 09 Mar 2021. https://doi.org/10.21203/rs.3.rs-284601/v1
Chotai N, Kulkarni S. Breast Imaging Essentials. Springer 2020, Singapore. https://doi.org/10.1007/978-981-15-1412-8_5
Solanki M, Visscher D. Pathology of breast cancer in the last half century. Hum Pathol 2020; 95:137-148. https://doi.org/10.1016/j.humpath.2019.09.007
Meenalochini G, Ramkumar S. Survey of machine learning algorithms for breast cancer detection using mammogram images. Mater.Today: Proc 2021;37:2738-43. https://doi.org/10.1016/j.matpr.2020.08.543
Yap MH, Goyal M, Osman F, Martí R, Denton E, Juette A, et al. Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med 2020 ; 107:101880. https://doi.org/10.1016/j.artmed.2020.101880
Weigelt B, Geyer FC, Reis-Filho JS. Histological types of breast cancer: how special are they? Mol Oncol 2010;4(3):192-208. https://doi.org/10.1016/j.molonc.2010.04.004
Li CI, Daling JR. Changes in breast cancer incidence rates in the United States by histologic subtype and race/ethnicity, 1995 to 2004. Cancer Epidemiol Biomarkers Prev 2007;16(12):2773-80. https://doi.org/10.1158/1055-9965.EPI-07-0546
Fondón I, Sarmiento A, García AI, Silvestre M, Eloy C, Polónia A, et al. Automatic classification of tissue malignancy for breast carcinoma diagnosis. Comput Biol Med 2018; 96:41-51. https://doi.org/10.1016/j.compbiomed.2018.03.003
Surakasula A, Nagarjunapu GC, Raghavaiah K. A comparative study of pre-and post-menopausal breast cancer: Risk factors, presentation, characteristics and management. J Res Pharm Pract 2014; 3(1): 12-18. https://doi.org/10.4103/2279-042X.132704
Aljarrah A, Miller W. Trends in the distribution of breast cancer over time in the southeast of Scotland and review of the literature. Ecancermedicalscience. 2014; 8: 427. https://doi.org/10.3332/ecancer.2014.427
Carvalho ED, Antonio Filho O, Silva RR, Araujo FH, Diniz JO, Silva AC, et al. Breast cancer diagnosis from histopathological images using textural features and CBIR. Artif Intell Med 2020 ;105:101845. https://doi.org/10.1016/j.artmed.2020.101845
Li C, Uribe D, Daling J. Clinical characteristics of different histologic types of breast cancer. Br J Cancer 2005;93(9):1046-52. https://doi.org/10.1038/sj.bjc.6602787
Do Nascimento RG, Otoni KM. Histological and molecular classification of breast cancer: what do we know? Mastology. 2020;30:1-8. https://doi.org/10.29289/25945394202020200024
DOI: https://doi.org/10.46903/gjms/22.01.1519
Refbacks
- There are currently no refbacks.
Copyright (c) 2024. Mahabad Abdalaziz Salih, Nawa Abdalhameed M Amin, Kawa Abdullah Mahmood, Abeer kadum Abass Alzuhairy, Lana R. A. Pshtiwan

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Gomal Medical College, Daraban Road, Dera Ismail Khan, Pakistan
ISSN: 1819-7973, e-ISSN: 1997-2067
Website: https://www.gmcdikhan.edu.pk
Phone: +92-966-747373

