Araştırma Çıktıları

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Author: search.filters.author.Aribal, ErkinSubject: search.filters.subject.breast cancer

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    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.
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    3D Automated Breast Ultrasound System: Comparison of Interpretation Time of Senior Versus Junior Radiologist
    (AVES, 2019-01-01) Arslan, Aydan; Ertas, Gokhan; Aribal, Erkin
    Objective: This study aimed to compare the automated breast ultrasound system (ABUS) reading time of breast radiologist to a radiology resident independent of the clinical outcomes. Materials and Methods: One hundred women who underwent screening ABUS between July and August 2017 were reviewed retrospectively. Each study was examined sequentially by a breast radiologist who has more than 20 years of experience in breast radiology and third year resident who has 6 months of experience in breast radiology. Data were analyzed with Spearman' correlation, Wilcoxon Signed Ranks Test and Kruskal-Wallis Test and was recorded. Results: The mean age of patients was 42.02 +/- 11.423 years (age range16-66). The average time for senior radiologist was 223.36 +/- 84.334 seconds (min 118 max 500 seconds). The average time for junior radiologist was 269.48 +/- 82.895 seconds (min 150 max 628 seconds). There was a significant difference between the mean time of two radiologists (p=0.00001). There was a significant difference regarding the decrease in the reading time throughout study with the increase of number of cases read by the breast radiologist (p<0.05)