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Öğe Occurence of chronic myeloid leukemia in two breast cancer survivors after 4 years(2011) Ocakçı, S; Görümlü, G; Sahin, F; Özsan, N.; Zekioğlu, O.; Uslu, R.; Saydam, GAynı hastada 2 ayrı malinitenin es zamanlı saptanması çok nadir bir durumdur. Özellikle meme kanseri ya da Hodgkin lenfoma tanısı için kemoterapi ve/veya radyoterapi alan ve kür elde edilen insanlarda beklenen yasam süresi yeterince uzun olduğu için, bu tedavilere bağlı ikincil kanser görme sansı fazladır. Burada etiyolojik ajan olarak verilen kemoterapi ya da radyoterapi suçlanabilir. Ama aynı hastada 2 ayrı malinitenin es zamanlı saptanmasının mekanizması tam olarak aydınlatılamamıstır. Bu yazıda, meme kanseri ve kronik myeloid lösemi tanısı alan 2 olgu sunulmaktadır.Öğe Papiller sinsityal metaplazi ve villoglandüler endometrioid adenokarsinom birlikteliği(2007) Akbulut, Metin; Terek, C. M; Zekioğlu, O.; Özdemir, N.Endometriyal karsinomlarda ve bazı non-neoplastik endometriyal proliferasyonlarda papilla olusumu ve farklı epiteliyal metaplaziler görülebilir. Papiller sinsityal metaplazi özellikle küretaj materyallerinde, seröz papiller karsinom ile karısabilir ya da altta yatan bir tümör ile birliktelik gösterebilir. Bu çalısmada 52 yasındaki kadın hastada papiller sinsityal metaplazi ve villoglandüler endometrioid adenokarsinom birlikteliği sunulmustur.Öğe Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study(Galenos Publishing House, 2023) Aslan, Ö.; Oktay, A.; Katuk, B.; Erdur, R.C.; Dikenelli, O.; Yeniay, L.; Zekioğlu, O.PURPOSE High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. METHODS A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. RESULTS Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was ob-served between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-under-the-curve score were obtained in classifying the data as benign or malignant. CONCLUSION Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum exci-sions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology–pathology compatibility and by using an artificial intelligence model. © 2023, Turkish Society of Radiology. All rights reserved.
