Browsing by Author "Ozcinar, Beyza"

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Bahcesehir long-term population-based screening compared to National Breast Cancer Registry Data: effectiveness of screening in an emerging country
(TURKISH SOC RADIOLOGY, 2021-01-01) Gurdal, Sibel Ozkan; Ozaydin, Ayse Nilufer; Aribal, Erkin; Ozcinar, Beyza; Cabioglu, Neslihan; Sahin, Cennet; Ozmen, Vahit
PURPOSE We aimed to show the effects of long-term screening on clinical, pathologic, and survival outcomes in patients with screen-detected breast cancer and compare these findings with breast cancer patients registered in the National Breast Cancer Registry Data (NBCRD). METHODS Women aged 40-69 years, living in Bahcesehir county, Istanbul, Turkey, were screened every 2 years using bilateral mammography. The Bahcesehir National Breast Cancer Registry Data (BMSP) data were collected during a 10-year screening period (five rounds of screening). BMSP data were compared with the NBCRD regarding age, cancer stage, types of surgery, tumor size, lymph node status, molecular subtypes, and survival rates. RESULTS During the 10-year screening period, 8758 women were screened with 22621 mammograms. Breast cancer was detected in 130 patients
Comparison of Qualitative and Volumetric Assessments of Breast Density and Analyses of Breast Compression Parameters and Breast Volume of Women in Bahcesehir Mammography Screening Project
(GALENOS YAYINCILIK, 2020-01-01) Gemici, Aysegul Akdogan; Aribal, Erkin; Ozaydin, Ayse Nilufer; Gurdal, Sibel Ozkan; Ozcinar, Beyza; Cabioglu, Neslihan; Ozmen, Vahit
Objective: We aimed to compare visual and quantitative measurements of breast density and to reveal the density profile with compression characteristics. Materials and Methods: Screening mammograms of 1399 women between May 2014 and May 2015 were evaluated by using Volpara 4th and 5th version. First 379 mammograms were assessed according to ACR BI-RODS 4th- edition and compared to Volpara. We categorized the breast density in two subgroups as dens or non-dens. Two radiologists reviewed the images in consensus. Agreement level between visual and volumetric methods and volumetric methods between themselves assessed using weighted kappa statistics. Volpara data such as fibroglandular volume (FGV), breast volume (BV), compression thickness (CT), compression force (CF), compression pressure (CP) were also analyzed with relation to the age. Results: 1399 mammograms were distributed as follows: 12.7\% VDG1, 39.3\% VDG2, 34.1\% VDG3, 13.9\% VDG4 according to the 4th edition of Volpara
Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis
(SAGE PUBLICATIONS INC, 2022-01-01) Kizildag Yirgin, Inci; Koyluoglu, Yilmaz Onat; Seker, Mustafa Ege; Ozkan Gurdal, Sibel; Ozaydin, Ayse Nilufer; Ozcinar, Beyza; Cabioglu, Neslihan; Ozmen, Vahit; Aribal, Erkin
Purpose: To evaluate the performance of an artificial intelligence (AI) algorithm in a simulated screening setting and its effectiveness in detecting missed and interval cancers. Methods: Digital mammograms were collected from Bahcesehir Mammographic Screening Program which is the first organized, population-based, 10-year (2009-2019) screening program in Turkey. In total, 211 mammograms were extracted from the archive of the screening program in this retrospective study. One hundred ten of them were diagnosed as breast cancer (74 screen-detected, 27 interval, 9 missed), 101 of them were negative mammograms with a follow-up for at least 24 months. Cancer detection rates of radiologists in the screening program were compared with an AI system. Three different mammography assessment methods were used: (1) 2 radiologists' assessment at screening center, (2) AI assessment based on the established risk score threshold, (3) a hypothetical radiologist and AI team-up in which AI was considered to be the third reader. Results: Area under curve was 0.853 (95\% CI = 0.801-0.905) and the cut-off value for risk score was 34.5\% with a sensitivity of 72.8\% and a specificity of 88.3\% for AI cancer detection in ROC analysis. Cancer detection rates were 67.3\% for radiologists, 72.7\% for AI, and 83.6\% for radiologist and AI team-up. AI detected 72.7\% of all cancers on its own, of which 77.5\% were screen-detected, 15\% were interval cancers, and 7.5\% were missed cancers. Conclusion: AI may potentially enhance the capacity of breast cancer screening programs by increasing cancer detection rates and decreasing false-negative evaluations.