Araştırma Çıktıları
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Item Improvement of early detection of breast cancer through collaborative multi-country efforts: Medical physics component(ELSEVIER SCI LTD, 2018-01-01) Mora, Patricia; Faulkner, Keith; Mahmoud, Ahmed M.; Gershan, Vesna; Kausik, Aruna; Zdesar, Urban; Brandan, Maria-Ester; Kurth, Serap; Davidovic, Jasna; Salama, Dina H.; Aribal, Erkin; Odio, Clara; Chaturvedi, Arvind K.; Sabih, Zahida; Vujnovic, Sasa; Paez, Diana; Delis, HarryPurpose: The International Atomic Energy Agency (IAEA) through a Coordinated Research Project on ``Enhancing Capacity for Early Detection and Diagnosis of Breast Cancer through Imaging{''}, brought together a group of mammography radiologists, medical physicists and radiographersItem Does Lapatinib Increase Pulmonary Toxicity when Concurrently Used with Radiation Therapy? An Experimental Study with Wistar-Albino Rats(AKAD DOKTORLAR YAYINEVI, 2018-01-01) Yetmen Dogan, Ozlem; Guzel, Elif; Coban, Ilker; Suzer, Oner; Bese, NuranLapatinib is an oral receptor tyrosine kinase inhibitor which has shown activity in the treatment of metastatic breast cancer. There is no data regarding the side effects of combination of radiotherapy and Lapatinib. 40 female Wistar-albino rats (WAR) were divided into 4 groupsItem Is Season a Prognostic Factor in Breast Cancer?(ASIAN PACIFIC ORGANIZATION CANCER PREVENTION, 2013-01-01) Mutlu, Hasan; Akca, Zeki; Cihan, Yasemin Benderli; Kurnaz, Fatih; Aslan, Tuncay; Erden, Abdulsamet; Ugur, Hediye; Aksahin, Arzu; Buyukcelik, AbdullahBackground: Some studies have indicated an inverse relationship between cancer risk and sunlight exposure. Others have reported that the prognosis of some cancers such as prostate, colon, ovarian and non melanoma skin cancer, were affected by the season in which the cancer was diagnosed. In our study, we evaluated whether season is prognostic in Turkish patients with breast cancer. Materials and Methods: A total of 517 patients from Kayseri Training and Research Hospital were analysed retrospectively. Patients were divided into 4 groups according to season of cancer diagnosis: winter, spring, summer and autumn. The prognostic factors for disease free survival and overall survival were investigated. Results: No significant differences were found among groups regarding prognostic factors overall. Only estrogen receptor status and lymphovascular invasion were independent prognostic factors (p=0.001 and p=0.001 respectively). We found significantly differences for mean disease free survival among groups (p=0.019). Winter group had better mean DFS while summer group had worse DFS. Mean overall survival was similar in the four groups (p=0.637). Conclusions: The season is not an independent predictive factor. However, due to interaction with other factors, we think that the season of cancer diagnosis is important for cancer prognosis.Item Is Sunlight a Predisposing Factor for Triple Negative Breast Cancer in Turkey?(ASIAN PACIFIC ORGANIZATION CANCER PREVENTION, 2013-01-01) Mutlu, Hasan; Buyukcelik, Abdullah; Colak, Taner; Ozdogan, Mustafa; Erden, Abdulsamet; Aslan, Tuncay; Akca, ZekiIntraduction: There is known to be a relationship between vitamin D level and more aggresive breast cancer subtypes, especially triple-negative breast cancer (TNBC). It was reported that sunlight exposure has an effect on the prognosis of patients with cancer, possibly related to the conversion of vitamin D to its active form with sunlight. We aimed to evaluate the effect of sunlight exposure on patients with TNBC. Materials-Methods: A total of 1,167 patients with breast cancer from two different regions of Turkey (Antalya and Kayseri, regions having different climate and sunlight exposure intensity over the year) were analysed retrospectively. The ratio of patients with TNBC was identified in those two regions. Results: The ratio of patients with TNBC was 8\% and 12\% for Kayseri and Antalya regions, respectively, and this difference between the two groups was statistically significant (p=0.021). Discussion: Sunlight exposure may be associated with more prevalent TNBC. This finding should be investigated with a prospective study.Item MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue(SPRINGER LONDON LTD, 2022-01-01) Cayir, Sercan; Solmaz, Gizem; Kusetogullari, Huseyin; Tokat, Fatma; Bozaba, Engin; Karakaya, Sencer; Iheme, Leonardo Obinna; Tekin, Eren; Yazici, Cisem; Ozsoy, Gulsah; Ayalti, Samet; Kayhan, Cavit Kerem; Ince, Umit; Uzel, Burak; Kilic, OnurMitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a 68.7\% F1-score and 49.0\% F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists' decision.Item HER2/neu Status in Breast Cancer Specimens: Comparison of Immunohistochemistry (IHC) and Fluorescence in situ Hybridization (FISH) Methods(SOC CHILENA ANATOMIA, 2015-01-01) Saglican, Yesim; Ince, UmitHER2 amplification or overexpression is considered as disease outcome and a predictive marker of response to treatment in breast cancer. The present study aimed to compare the results of IHC and FISH for determining HER2 and to search the interpretational differences. Samples (n= 169), of which 31 were the paraffin blocks sent from outer centers, that underwent FISH analysis for HER-2 were included. Samples were re-reviewed by IHC in our laboratory. FISH test was negative in 131 (77.5\%) and positive in 38 (22.5\%). When those with previous IHC 0-1+ were re-reviewed, the results were found again 0-1+ and none of them was FISH positive. Inconsistency between re-reviewed IHC and previous IHC results was 25\% for those with 2+ score and 11\% for those with 3+ score. Consistency between IHC and FISH was 17\% and 67\% for previous IHC 2+ and 3+, respectively, whereas it was 23\% and \%75 for re-reviewed IHC 2+ and 3+, respectively. Whilst 79\% of the samples evaluated as 2+ by the inexperienced pathologist were found to be 0-1+ on the re-review, all of them were FISH negative. According to our results, we suggest that samples with IHC 2+ should be re-reviewed by consulting with an experienced pathologist.