WOS

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

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Author: search.filters.author.Tokat, FatmaHas files: No

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    Multi-center real-world comparison of the fully automated Idylla (TM) microsatellite instability assay with routine molecular methods and immunohistochemistry on formalin-fixed paraffin-embedded tissue of colorectal cancer
    (SPRINGER, 2021-01-01) Velasco, Ana; Tokat, Fatma; Bonde, Jesper; Trim, Nicola; Bauer, Elisabeth; Meeney, Adam; de Leng, Wendy; Chong, George; Dalstein, Veronique; Kis, Lorand L.; Lorentzen, Jon A.; Tomic, Snjezana; Thwaites, Keeley; Putzova, Martina; Birnbaum, Astrid; Qazi, Romena; Primmer, Vanessa; Dockhorn-Dworniczak, Barbara; Hernandez-Losa, Javier; Soares, Fernando A.; Gertler, Asaf A.; Kalman, Michal; Wong, Chris; Carraro, Dirce M.; Sousa, Ana C.; Reis, Rui M.; Fox, Stephen B.; Fassan, Matteo; Brevet, Marie; Merkelbach-Bruse, Sabine; Colling, Richard; Soilleux, Elizabeth; Teo, Ryan Yee Wei; D'Haene, Nicky; Nolet, Serge; Ristimaki, Ari; Vaisanen, Timo; Chapusot, Caroline; Soruri, Afsaneh; Unger, Tina; Wecgowiec, Johanna; Biscuola, Michele; Frattini, Milo; Long, Anna; Campregher V, Paulo; Matias-Guiu, Xavier
    Microsatellite instability (MSI) is present in 15-20\% of primary colorectal cancers. MSI status is assessed to detect Lynch syndrome, guide adjuvant chemotherapy, determine prognosis, and use as a companion test for checkpoint blockade inhibitors. Traditionally, MSI status is determined by immunohistochemistry or molecular methods. The Idylla (TM) MSI Assay is a fully automated molecular method (including automated result interpretation), using seven novel MSI biomarkers (ACVR2A, BTBD7, DIDO1, MRE11, RYR3, SEC31A, SULF2) and not requiring matched normal tissue. In this real-world global study, 44 clinical centers performed Idylla (TM) testing on a total of 1301 archived colorectal cancer formalin-fixed, paraffin-embedded (FFPE) tissue sections and compared Idylla (TM) results against available results from routine diagnostic testing in those sites. MSI mutations detected with the Idylla (TM) MSI Assay were equally distributed over the seven biomarkers, and 84.48\% of the MSI-high samples had >= 5 mutated biomarkers, while 98.25\% of the microsatellite-stable samples had zero mutated biomarkers. The concordance level between the Idylla (TM) MSI Assay and immunohistochemistry was 96.39\% (988/1025)
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    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, Onur
    Mitosis 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.
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    Three Cases of Breast Metastases from Lung Cancer and Systematic Review of the Literature
    (GALENOS YAYINCILIK, 2021-01-01) Guldogan, Nilgun; Icten, Gul Esen; Tokat, Fatma; Tutar, Burcin; Kara, Halil; Korkmaz, Taner; Uluc, Basak Oyan; Demir, Gokhan
    Despite the high prevalence of lung cancer among other primary tumors, metastasis of this particular malignancy in the breast is very rare. We report three new cases of lung cancer with breast metastases and discuss radiological and clinical findings. Radiologically, each case displayed different characteristics. First, one of them had bilateral superficially and deeply located irregular lesions. Second, the patient presented with findings similar to inflammatory breast cancer. The third case had a circumscribed mass, resembling a benign complicated cyst. To guide clinicians for proper patient management, radiologists should be aware of the scope of typical and atypical imaging findings of metastatic involvement of the breast.
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    Expression of vascular endothelial growth factor in follicular cell-derived lesions of the thyroid: Is NIFTP benign or precancerous?
    (TURKISH SURGICAL ASSOC, 2022-01-01) Kurtulmus, Neslihan; Tokat, Fatma; Duren, Mete; Kaya, Hakan; Ertas, Burak; Ince, Umit
    Objective: Vascular endothelial growth factor (VEGF) is an angiogenic factor that plays an important role in physiological and pathological angiogenesis of the thyroid. The aim of the current study was to determine the expression characteristics of VEGF in follicular cell-derived lesions of the thyroid and to assess whether a new entity noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is precancerous. Material and Methods: Patients diagnosed with 33 follicular adenomas (FA), 41 invasive follicular variant papillary thyroid cancer (IN-FVPTC), and 40 NIFTP in surgical resection materials were evaluated retrospectively. Immunostaining was performed on 5-mu m paraffin tissue sections. The percentages of immunostaing for VEGF were evaluated on pathological materials. We used a percentage of labeled thyrocytes score (0, no labeling
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    Comparison of Two Different Antibody Clones of Programmed Cell Death Ligand 1 (PD-L1) with Immunohistochemical Method on Various Tumors
    (KARE PUBL, 2020-01-01) Tokat, Fatma
    Objectives: Programmed cell death ligand 1 (PD-L1) is the most important immune checkpoint protein in immune defense against tumors. PD-1/PD-L1 inhibitors are considered an option in cancer treatments. The evaluation of PD-L1 immunohistochemical staining is used as a biomarker to determine the decision and response of the use ofthese inhibitory drugs. There is a wide variety of clones and platforms for the PD-L1 antibody, and each pathology department uses different clones and platforms which causes confusion. Therefore, in this study, we evaluated the immunohistochemical staining of different clones in the same tumor. Methods: Overall, 90 cases comprising 47 lung, 11 breast, 9 colon, 6 stomach, and 7 pancreatic carcinomas and 10 other tumors were included in the study. Of these, 43 specimens were obtained by resection, 40 by tru-cut biopsy, and 7 by endoscopic biopsy. Sections prepared from formalin-fixed paraffin-embedded blocks were evaluated immunohistochemically with SP142 and SP263 clones. Results: In this study, we observed positive staining in 48.8\% (n=44) and negative staining in 51.2\% (n=46) among all cancers with SP263 clone, and positive staining in 33.3\% (n=30) and negative staining in 66.7\% with SP142 clone as well. This study also showed that compared to SP263, SP142 clone stained tumor cells less in lung, colon, stomach, pancreatic, and other carcinomas. Conclusion: In this study, we found different staining percentages for SP263 and SP142 in the same tumor. Pathologists conducting immunohistochemical studies for PD-L1 should indicate the staining percentages of tumors and the antibody clone they used in the reports. Meanwhile, oncologists should keep in mind which clone was stained, and that selecting SP142 is less positive to correct patients who can receive appropriate immunotherapy.