Pattern of Presentation of Mammography in a Developing Country

2020 ◽  
Vol 10 (2) ◽  
pp. 33-37
Author(s):  
Birendra Raj Joshi

Introduction: Breast cancer is the second commonest cancer (7.2%) in Nepal and almost 54%of patients present in the advanced stage. It is the leading cause of cancer death in females.The objective of the study was to determine the type of mammography, composition of breastdensity and BIRADS category.Methods: The study was conducted in a tertiary hospital from Jan 1st to Oct 30th of 2019according to non-probability convenience sampling. A total of 388 persons were included inthe study. The mammographic findings were assessed by categories based on the BIRADSsystem.Results: Mammography for screening was 38 percent and diagnostic was 68 percent. Commonbreast compositions were B and C. More frequent BIRADS categories were seen in 1 and 2.Conclusion: Dense breast is common in mammography. BIRADS categories 1 and 2 weremore common than other categories.

Author(s):  
Yu Wang ◽  
Jiantao Wang ◽  
Haiping Wang ◽  
Xinyu Yang ◽  
Liming Chang ◽  
...  

Objective: Accurate assessment of breast tumor size preoperatively is important for the initial decision-making in surgical approach. Therefore, we aimed to compare efficacy of mammography and ultrasonography in ductal carcinoma in situ (DCIS) of breast cancer. Methods: Preoperative mammography and ultrasonography were performed on 104 women with DCIS of breast cancer. We compared the accuracy of each of the imaging modalities with pathological size by Pearson correlation. For each modality, it was considered concordant if the difference between imaging assessment and pathological measurement is less than 0.5cm. Results: At pathological examination tumor size ranged from 0.4cm to 7.2cm in largest diameter. For mammographically determined size versus pathological size, correlation coefficient of r was 0.786 and for ultrasonography it was 0.651. Grouped by breast composition, in almost entirely fatty and scattered areas of fibroglandular dense breast, correlation coefficient of r was 0.790 for mammography and 0.678 for ultrasonography; in heterogeneously dense and extremely dense breast, correlation coefficient of r was 0.770 for mammography and 0.548 for ultrasonography. In microcalcification positive group, coeffient of r was 0.772 for mammography and 0.570 for ultrasonography. In microcalcification negative group, coeffient of r was 0.806 for mammography and 0.783 for ultrasonography. Conclusion: Mammography was more accurate than ultrasonography in measuring the largest cancer diameter in DCIS of breast cancer. The correlation coefficient improved in the group of almost entirely fatty/ scattered areas of fibroglandular dense breast or in microcalcification negative group.


Author(s):  
Elizabeth Buckley ◽  
Elisabeth Elder ◽  
Sarah McGill ◽  
Zahra Shahabi Kargar ◽  
Ming Li ◽  
...  

Abstract Introduction Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks. Reducing variations in cancer treatment and survival is a key aim of the NSW Cancer Plan. Variations in breast cancer treatment and survival in NSW by remoteness and socioeconomic status of residence were investigated to determine benchmarks. Methods A retrospective cohort study used linked data for invasive breast cancers, diagnosed in May 2002 to December 2015 from the NSW Cancer Registry, with corresponding inpatient, and medical and pharmaceutical insurance data. Associations between treatment modalities, area socioeconomic status and residential remoteness were explored using logistic regression. Predictors of breast cancer survival were investigated using Kaplan–Meier product-limit estimates and multivariate competing risk regression. Results Results indicated a high 5-year disease-specific survival in NSW of 90%. Crude survival was equivalent by residential remoteness and marginally lower in lower socioeconomic areas. Competing risk regression showed equivalent outcomes by area socioeconomic status, except for the least disadvantaged quintile, which showed a higher survival. Higher sub-hazard ratios for death occurred for women with breast cancer aged 70 + years, and more advanced stage. Adjusted analyses indicated more advanced stage in lower socioeconomic areas, with less breast reconstruction and radiotherapy, and marginally less hormone therapy for women from these areas. Conversely, among these women who had breast conserving surgery, there was higher use of chemotherapy. Remoteness of residence was associated in adjusted analyses with less radiotherapy and less immediate breast reconstruction. In these short term data, remoteness of residence was not associated with lower survival. Conclusion This study provides benchmarks for monitoring future variations in treatment and survival.


2020 ◽  
Vol 9 (15) ◽  
pp. 5662-5671 ◽  
Author(s):  
Jenerius A. Aminawung ◽  
Jessica R. Hoag ◽  
Kelly A. Kyanko ◽  
Xiao Xu ◽  
Ilana B. Richman ◽  
...  

2009 ◽  
Vol 2 (4) ◽  
pp. 371-386 ◽  
Author(s):  
Susan Bauer-Wu ◽  
Katherine Yeager ◽  
Rebecca L. Norris ◽  
Qin Liu ◽  
Karleen R. Habin ◽  
...  

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Ghada Khaled Ahmed ◽  
Mounir Sobhy Guirguis ◽  
Mona Gamalluldin Alsayed Alkaphoury

Abstract Background Breast cancer remains one of the leading causes of death in women over the age of 40 years. Breast cancer screening is used to identify women with asymptomatic cancer with the goal of enabling women to undergo less invasive treatments that lead to better outcomes, ideally at earlier stages and before the cancer progresses. Mammography is the best-studied breast cancer screening modality and the only recommended imaging tool for screening the general population of women. Objective to correlate the relation between ACR density of breast and breast cancer in screening program. Patients and Methods Our study included 40 women of breast cancer were depicted radiologically and histo-pathologically diagnosed after outreaching for screening by Digital Mammography by the Egyptian National Breast Cancer Screening Program in Ain Shams University Hospitals at period from January 2018 to October 2019.Their data were collected from the medical records of the program. Their age ranged between 40 and 65 years. Results According to the BI-RADS 5th edition 2013, cases were classified into four classes as follows: 6 were ACR-A (15.0%), 21 were ACR-B (52.5%), 12 were ACR-C (30.0%) and 1 were ACR-D (2.5%), So according to our study results dense breast shouldn’t be considered as a risk factor for breast cancer as we observed that the percentage of breast cancer in our study increases the most with average breast density ACR class B then increases with ACR class C and A respectively. Conclusion dense breast is not a risk factor for breast cancer, so further researches are needed to study the relationship between breast density and breast cancer in Egyptian population, to elucidate the role of breast density estimation in prediction of breast cancer considering the genotypical and phenotypical differences of the Egyptian population.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13586-e13586
Author(s):  
Richa Bansal ◽  
Bharat Aggarwal ◽  
Lakshmi Krishnan

e13586 Background: Screening mammography is often found to have low sensitivity in women with high density breast tissues. Alternate modalities of breast USG and MRI require high-quality expensive equipment making the regular screening with these modalities less affordable and accessible, particularly in resource-constrained settings This study evaluates the clinical performance of an AI-based test (Thermalytix) that uses machine learning on breast thermal images which could potentially be a low-cost solution for breast screening in low- and middle-income countries (LMICs). Methods: The prospective comparative study conducted from December 2018 to January 2020 evaluated the performance of Thermalytix in women with dense and non-dense breast tissue who presented for a health check-up at a hospital. All women underwent Thermalytix and mammography. Further investigations were recommended for participants who were reported as positive on either test. Sensitivity and specificity of Thermalytix were evaluated across age-groups, menopausal status, and breast densities. Results: Among the 687 women recruited for the study, 459 women who satisfied the inclusion criteria were included in the analysis. 168 women had ACR categories ‘c’ or ‘d’ dense breasts, of which 37 women had an inconclusive mammography report (BI-RADS 0). Overall, 21 women were detected with breast cancer in the study. Thermalytix demonstrated an overall sensitivity of 95.2% (95% CI, 76.1-99·9) and a specificity of 88.6% (95% CI, 85.2-91.4). Among women with dense breast tissue (n=168), Thermalytix showed a sensitivity of 100% (95% CI, 69.2-100) and a specificity of 81.7% (95% CI, 74.7-87.4). In women with ACR categories ‘c’ and ‘d’ dense breasts, mammography reported 22% of them as inconclusive (BI-RAD 0), while in the same sub-set of the population Thermalytix demonstrated a sensitivity of 100%. Conclusions: The AI-based Thermalytix demonstrated high sensitivity and specificity in the study cohort. It also fared well in women younger than 50 years and pre-menopausal women where routine mammography screening yields low sensitivity. Overall, this study introduces Thermalytix, a promising radiation-free, automated, and privacy-aware test that can supplement mammography for routine screening of women, especially in women with dense breast tissue, and has the potential to influence the clinical practice in LMICs by making breast cancer screening portable and affordable. Performance of Thermalytix and mammography in women with high breast densities (ACR categories ‘c’ and ‘d’ breasts). Clinical trial information: NCT04688086. [Table: see text]


2018 ◽  
Vol 21 ◽  
pp. S166
Author(s):  
JD Miller ◽  
MM Bonafede ◽  
SK Pohlman ◽  
KA Troeger

2019 ◽  
Vol 6 (2) ◽  
pp. 52-57
Author(s):  
Binod Bade Shrestha ◽  
Sujan Shrestha ◽  
Mikesh Karmacharya ◽  
Pradeep Ghimire

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