Literature DB >> 25339004

Pathological profile of patients with breast diseases in Shiraz.

Abbas Rezaianzadeh1, Mojtaba Sepandi, Majid Akrami, Hamidreza Tabatabaee, Abdolreza Rajaeefard, Sedigheh Tahmasebi, Abdolrasoul Talei.   

Abstract

BACKGROUND: Around 200,000 breast disorders are annually diagnosed all over the world. Fibrocystic changes are the most common breast disorder and fibroadenoma is the most prevalent benign breast tumor. The present study aimed to determine the spectrum, type and prevalence of breast masses in women referred to Shiraz University of Medical Sciences between 2004 and 2012 .
MATERIALS AND METHODS: A cross-sectional study was conducted on the diagnostic reports data.
RESULTS: A total of 640 samples were studied. Most 57.3% of masses were detected in the left breast, 65%, 28.2% and 6.1% of cases presenting with benign, malignant, and inflammatory lesions, respectively. Among all the samples the most prevalent diagnosis (37.7%) was fibroadenoma and fibrocystic lesions (17%). 174 samples (96% of the malignant cases) were invasive. 6.5% of the benign, and 37% of the malignant cases occurred in post menopause women and the differences were statistically significant. Among those with malignant tumors lymph nodes were involved in 25.6% of menopausal women and 44.2% of non-menopausal ones, and the difference was statistically significant.
CONCLUSIONS: Regular clinical breast examination beside mammographic follow-ups, especially during menopause, should be carried out as a priority and a national organized program should be designed for screening breast disorders.

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Year:  2014        PMID: 25339004     DOI: 10.7314/apjcp.2014.15.19.8191

Source DB:  PubMed          Journal:  Asian Pac J Cancer Prev        ISSN: 1513-7368


  3 in total

1.  Age distribution and types of breast lesions among Afghan women diagnosed by fine needle aspiration cytology (FNAC) at a tertiary care centre in Afghanistan: a descriptive cross-sectional study.

Authors:  Ramin Saadaat; Jamshid Abdul-Ghafar; Ahmed Maseh Haidary; Soma Rahmani; Nooria Atta
Journal:  BMJ Open       Date:  2020-09-01       Impact factor: 2.692

2.  Assessing Breast Cancer Risk with an Artificial Neural Network

Authors:  Mojtaba Sepandi; Maryam Taghdir; Abbas Rezaianzadeh; Salar Rahimikazerooni
Journal:  Asian Pac J Cancer Prev       Date:  2018-04-25

3.  Medullary Breast Carcinoma and Invasive Ductal Carcinoma: A Review Study.

Authors:  Vahid Zangouri; Majid Akrami; Sedigheh Tahmasebi; Abdolrasoul Talei; Ali Ghaeini Hesarooeih
Journal:  Iran J Med Sci       Date:  2018-07
  3 in total

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