Browsing by Author "Onay, Aslihan"

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Can Extraprostatic Extension Be Predicted by Tumor-Capsule Contact Length in Prostate Cancer? Relationship With International Society of Urological Pathology Grade Groups
(2020) Bakir, Baris; Onay, Aslihan; Vural, Metin; Armutlu, Ayse; Yildiz, Sevda Ozel; Esen, Tarik; 31670596
OBJECTIVE. The objective of our study was to evaluate the relationship between the tumor-capsule contact length, defined as tumor contact length (TCL), and extraprostatic extension (EPE) using the MRI-based TCL measurements and the real TCL measurements from pathology and to determine whether the International Society of Urological Pathology (ISUP) grade group of the tumors influenced this relationship. MATERIALS AND METHODS. In this retrospective study, we reviewed prostate multiparametric MRI (mpMRI) studies performed between 2012 and 2018 of 1576 patients and found that 134 patients also underwent radical prostatectomy (RP) after mpMRI. Finally, 86 patients with index lesions in contact with the prostate capsule in RP specimens were enrolled in the study. ROC analysis was used to evaluate the cutoff values of TCLs measured at pathology and TCLs measured on MRI in terms of EPE according to ISUP grade groups. RESULTS. There was no statistically significant cutoff value for pathology-based TCL measurements in individual ISUP grade groups and subgroups. Although not statistically significant, pathology-based TCL cutoff values decreased (from 21.0 to 11.0 mm) as ISUP grade group increased in terms of EPE, positivity. When the relationship between MRI-based TCL measurements and EPE was considered, statistically significant cutoff values (range, 145-16.6 mm) could be determined in many groups and subgroups with low ISUP grades (sensitivity, 66.7-100%; specificity, 52.8-93.0%; p = 0.006-0.042). However, no statistically significant cutoff value was found for high ISUP grades. CONCLUSION. ISUP grade groups may have an effect on the TCL-EPE relationship. When the MRI-based TCL and EPE relationship is evaluated independent of ISUP grade group, a cutoff value around 15-16 mm may be usable to predict EPE.
Evaluation of the most optimal multiparametric magnetic resonance imaging sequence for determining pathological length of capsular contact
(2019) Onay, Aslihan; Vural, Metin; Armutlu, Ayse; Yildiz, Sevda Ozel; Kiremit, Murat Can; Esen, Tarik; Bakir, Baris; 30777210
Objectives: To assess the most optimal multi-parametric magnetic resonance imaging sequence (Mp-MRI) in determining pathological length of capsular contact (LCC) for the diagnosis of prostate cancer extraprostatic extension (EPE). Methods: 105 patients with prostate cancer who underwent Mp-MRI of prostate prior to radical prostatectomy were enrolled in this retrospective study. LCC was determined from T2-weighted images (T2WI), Apparent Diffusion Coefficient (ADC) map, dynamic contrast-enhanced MRI (DCE-MRI) separately by two blinded radiologists. The LCCs in patients with and without EPE were compared with Mann Whitney-U test. The relationship between pathological LCC and the LCC that was measured from each Mp-MRI sequences were calculated by using Spearman test. The ability of all individual Mp-MRI sequences in determining pathological LCC was calculated by drawing receiver operator characteristic (ROC) curves. The diagnostic accuracy of LCC based on each MRI sequences for EPE diagnosis was also calculated with ROC curve analysis. Results: The patients with EPE had longer median LCC than patients without EPE for each Mp-MRI sequences and for both readers. In addition, the LCC showed a broader overlapping between patients with and without EPE on ADC map (reader-1, p = 0.01; reader-2, p = 0.01) when compared with T2WI (reader-1, p = 0.002; reader-2, p = 0.001) and DCE-MRI (reader-1, p = 0.001; reader-2, p = 0.001). LCC based on DCE-MRI showed the strongest correlation with pathological LCC. The area under the curve (AUC) based on LCC was higher when using the DCE-MRI (reader-1: 0.874, p = 0.030; reader-2: 0.862, p = 0.02) than when using T2WI and ADC map in predicting pathological LCC for both readers. While the LCC based on ADC map showed poor diagnostic accuracy, LCC based on T2WI and DCE-MRI had fair diagnostic accuracy for EPE diagnosis. Conclusion: The contact between prostate tumor and capsule seems to be a useful and objective parameter for evaluating the EPE of prostate cancer with Mp-MRI. More specifically, LCC based on DCE-MRI has highest correlation with pathological LCC and has better ability to predict pathological LCC when compared with other Mp-MRI sequences. However, the performance of LCC based on T2WI and DCE-MRI was similar for EPE diagnosis. It seems measurement of LCC from DCE-MRI and measurement of LCC from T2WI does not show any difference in clinical EPE assessment.
The Relation Between Serum P-selectin, Thrombin-activatable Fibrinolysis Inhibitor Levels, and Carotid Artery Intima-media Thickness in Acute Ischemic Stroke
(2020) Okuyan, Dilek Yilmaz; Kurt, Seda Aladag; Onay, Aslihan; Karakus, Resul; Kocer, Belgin
Objective: Inflammation and migration of leukocytes to the brain parenchyma play a role in atherosclerosis and cerebral ischemic stroke. Migration occurs with the help of adhesion molecules on the surface of cerebral endothelial cells and leukocytes. P-selectin, an adhesion molecule, is present on the platelet and endothelial surface and allows leukocytes to loosely adhere to the endothelium, and its increase has been shown in acute ischemic stroke (AIS). Thrombin-activatable fibrinolysis inhibitor (TAFI) is a procarboxypeptidase molecule that can be another marker of AIS, which has been shown to increase the risk of thromboembolism and stroke 6-fold. Intima-media thickness (IMT) is thought to be associated with atherosclerotic diseases in carotid ultrasonography (USG) and increased risk of ischemic stroke has been found to be associated with increased carotid IMT. In this study, we investigated the relationship between P-selectin and TAFI levels, which have been shown to be effective for AIS via carotid IMT, and is considered significant for atherosclerosis. Materials and Methods: Forty patients with AIS and 22 healthy subjects were included in the study. In both groups, serum P-selectin and TAFI levels were studied at the time of presentation, and on day 7, day 14, and at one month; carotid IMT and stenosis rates were measured by Doppler USG. P-selectin and TAFI levels were compared with carotid IMT in both groups. Results: There was no significant difference between P-selectin levels and carotid IMT between the groups; TAFI levels were significantly higher in the patient group and were correlated with carotid IMT in both groups. Conclusion: TAFI increase has been suggested to be a marker of early atherosclerosis in asymptomatic atherosclerosis and ischemic stroke. A positive correlation between TAFT levels and carotid IMT and stenosis rates have been reported; however, the positive correlation between increased P-selectin levels in AIS and carotid IMT was not detected in our study.
The role of T2-weighted images in assessing the grade of extraprostatic extension of the prostate carcinoma
(2020) Onay, Aslihan; Ertas, Gokhan; Vural, Metin; Colak, Evrim; Esen, Tarik; Bakir, Baris; 32002569
Purpose Extraprostatic extension (EPE) is an unfavorable prognostic factor and the grade of EPE is also shown to be correlated with the prognosis of prostate cancer. The current study assessed the value of prostate magnetic resonance imaging (MRI) in measuring the radial distance (RD) of EPE and the role of T2 WI signs in predicting the grade of EPE. Materials and methods A total of 110 patients who underwent prostate MRI before radical prostatectomy are enrolled in this retrospective study. Eighty-four patients have organ confined disease and the remaining twenty-six patients have EPE all verified by histopathology. Prostate MRI examinations were conducted with 3T MRI scanner and phased array coil with the following sequences: T2 WI, T1 WI, DCE, DWI with ADC mapping, and high b-value at b = 1500 s/mm(2). The likelihood of EPE with 5-point Likert scale was assigned, several MRI features were extracted for each dominant tumor identified by using T2 WI. Tumors with Likert scales 4-5 were evaluated further to obtain MRI-based RD. The relationship between pathological and MRI-determined RD was tested. Univariate and multivariate logistic regression models were developed to detect the grade of pathological EPE. The inputs were among the 2 clinical parameters and 4 MRI features. Results There is a moderate correlation between pathological RD and MRI-determined RD (rho = 0.45, P < 0.01). In univariate and multivariate models, MRI features and clinical parameters possess varying significance levels (univariate models; P = 0.048-0.788, multivariate models; P = 0.173-0.769). Multivariate models perform better than the univariate models by offering fair to good performances (AUC = 0.69-0.85). The multivariate model that employs the MRI features offers better performance than the model employs clinical parameters (AUC = 0.81 versus 0.69). Conclusion Co-existence of T2 WI signs provide higher diagnostic value even than clinical parameters in predicting the grade of EPE. Combined use of clinical parameters and MRI features deliver slightly superior performance than MRI features alone.
Use Of Deep Learning Methods For Hand Fracture Detection From Plain Hand Radiographs
(2022) Ureten, Kemal; Sevinc, Huseyin Fatih; Igdeli, Ufuk; Onay, Aslihan; Maras, Yuksel; https://orcid.org/0000-0003-4213-9126; 35099027
BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7% , respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.