WOS

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

Browse

Author: search.filters.author.Afsar, Cigdem UsulHas files: No

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Comparison of serum NEDD-9, CA 15-3, and CEA levels and PET metabolic parameters in breast cancer patients with 18 F-FDG PET/CT
    (ASSOC MEDICA BRASILEIRA, 2020-01-01) Arslan, Esra; Aral, Hale; Aksoy, Tamer; Afsar, Cigdem Usul; Karabulut, Senem; Trabulus, Fadime Didem Can; Gursu, Riza Umar; Cermik, Tevfik Fikret
    OBJECTIVE: Analyze the over expression of neural precursor cell expressed developmentally down-regulated protein 9 (NEDD-9) deregulated associated with a poor prognosis in various carcinomas. Our objective was to investigate the relationship between the levels of NEDD-9, CA 15-3, and CEA and PET (SUVmax, MTV40, TLG40) with the clinical parameters of patients with breast cancer (BC). METHODS: One hundred and eleven patients (82 BC patients who underwent 18F-FDG PET/CT and 29 healthy controls) were evaluated. SUVmax, MTV, and TLG of the primary tumor were compared with the molecular and histopathological subtypes. 18F-FDG, MTV, and TLG were evaluated based on the clinical data, i.e., nodal involvement, distant metastasis, ER and PR status, Ki-67, serum levels of NEDD-9, CA15-3, and CEA. We compared the NEDD-9 in the BC and healthy control groups. RESULTS: The mean +/- SD of SUVmax in the 82 patients was 13.0 +/- 8.6. A statistically significant relationship (p = 0.022) was found between the molecular subtypes and 18F-FDG uptake. The relationship between 18F-FDG uptake and TLG measured in patients <50 years, ER-PR negativity, and HER2 positivity were statistically significant (p=0.015, 0.007, 0.046, and 0.001, respectively). MTV40, TLG40, and CA 15-3 in metastatic patients were statistically significant (p=0.004, 0.005, and 0.003, respectively). NEDD-9 in the BC group was significantly higher than in the healthy group (p=0.017). There was a positive correlation between SUVmax and 1(167 and CA 15-3
  • No Thumbnail Available
    Item
    Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion
    (TURKISH SOC RADIOLOGY, 2020-01-01) Yardimci, Aytul Hande; Kocak, Burak; Bektas, Ceyda Turan; Sel, Ipek; Yarikkaya, Enver; Dursun, Nevra; Bektas, Hasan; Afsar, Cigdem Usul; Gursu, Riza Umar; Kilickesmez, Ozgur
    PURPOSE Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis gastric cancers. In this work, we aimed to investigate the potential role of computed tomogray (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoa (GAC) using a machine learning (ML) approach. METHODS Sixty-eight patients who under went total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric. RESULTS Among 271 texture features, 150 features had excellent reproducibility, which wer e included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI arid five for PNI. Considering all eight ML algorithms, mean AUC arid accuracy ranges for predicting LVI were 0.777-0.894 and 76\%-81.5\%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0A82-0.754 and 54\%-68.2\% respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively. CONCLUSION ML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Over-all, the method was more successful in predicting LVI than PNI.
  • No Thumbnail Available
    Item
    HIF-1 alpha Levels in patients receiving chemoradiotherapy for locally advanced non-small cell lung carcinoma
    (ASSOC MEDICA BRASILEIRA, 2019-01-01) Afsar, Cigdem Usul; Uysal, Pelin
    AIM: To examine the relationship between treatment response and hypoxia-inducible factor-1 alpha (HIF-1 alpha) levels in patients with locally advanced non-small cell lung cancer (NSCLC) who received chemoradiotherapy (CRT). METHODS: Eighty patients with NSCLC were included in the study and treated at Acibadem Mehmet Ali Aydinlar University Medical Faculty. HIF-1 alpha levels were measured before and after CRT by the enzyme-linked immunosorbent assay (ELISA) method. RESULTS: Patients' stages were as follows