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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Author: search.filters.author.Ozmen, VahitSubject: search.filters.subject.breast cancer

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    Impact of Oncotype DX Recurrence Score on Treatment Decisions: Results of a Prospective Multicenter Study in Turkey
    (CUREUS INC, 2016-01-01) Ozmen, Vahit; Atasoy, Ajlan; Gokmen, Erhan; Ozdogan, Mustafa; Guler, Nilufer; Uras, Cihan; Ok, Engin; Demircan, Orhan; Isikdogan, Abdurrahman; Saip, Pinar
    Introduction: Breast cancer is the most common malignancy among Turkish women and the rate of early stage disease is increasing. The Oncotype DX (R) 21-gene assay is predictive of distant recurrence in ER-positive, HER2-negative early breast cancer. We aimed to evaluate the impact of the Recurrence Score (R) (RS) on treatment decisions and physician perceptions in Turkey. We also studied correlations between RS and routine risk factors. Patients and Methods: Ten academic centers across Turkey participated in this prospective trial. Consecutive breast cancer patients with pT1-3, pN0-N1mic, ER-positive, and HER2-negative tumors were identified at multidisciplinary tumor conferences. The initial treatment decision was recorded before tumor blocks were sent to the central laboratory. Each case was brought back to tumor conference after receiving the RS result. Both pre- and post-RS treatment decisions and physician perceptions were recorded on questionnaire forms. Correlations between RS and classical risk factors were evaluated using univariate and multivariate analyses. Results: Ten centers enrolled a total of 165 patients. The median tumor size was 2 cm. Of 165 patients, 57\% had low RS, 35\% had intermediate RS, and 8\% had high RS, respectively. The overall rate of change in treatment decision was 33\%. Initially, chemotherapy followed by hormonal therapy (CT+HT) was recommended to 92 (56\%) of all patients, which decreased to 61 (37\%) patients post-RS assay (p<0.001). Multivariate analysis indicated that progesterone receptor (PR) and Ki-67 scores were significantly related to RS. Conclusion: Oncotype DX testing may provide meaningful additional information in carefully selected patients.
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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.