Publications by authors named "Murat Ayhan"

10 Publications

  • Page 1 of 1

Nivolumab for metastatic uveal melanoma: a multicenter, retrospective study.

Melanoma Res 2021 May 25. Epub 2021 May 25.

Department of Medical Oncology, Bakirköy Sadi Konuk Training and Research Hospital Department of Medical Oncology, Koç University Department of Ophthalmology, Cerrahpasa Medical Faculty, Istanbul University Department of Medical Oncology, Kartal Lütfi Kirdar Training and Research Hospital, Istanbul, Turkey.

Systemic treatment options with proven efficacy for the treatment of metastatic uveal melanoma are limited. In this study, we aimed to evaluate the efficacy of nivolumab in metastatic uveal melanoma patients. In our multi-center study, the files of patients who received nivolumab treatment with a diagnosis of metastatic uveal melanoma were retrospectively reviewed and their information was recorded. Seventeen patients were enrolledand 16 patients were evaluable for efficacy. The objective response rate (ORR) was 18% including one confirmed complete response and two confirmed partial responses. The median progression-free survival (PFS) was 5.8 months (95% CI, 0.03-11.57 months), and the median overall survival (OS) was 10.5 months (95% CI, 3.87-14.14 months). Significant longer OS and PFS were observed in patients with the performance status of the Eastern Cooperative Oncology Group (ECOG-PS) 0. Although significant longer OS was detected in patients with low median lactate dehydrogenase (LDH) levels, no significant difference was found in PFS. Grade 1 and 2 fatigue and decreased appetite were the most common side effects associated with treatment (17%); grade 3 and 4 side effects were not observed. Immunotherapy is also emerging as a treatment option among the limited number of treatment options in metastatic uveal melanoma (mUM), but its efficacy needs to be demonstrated with prospective studies involving a larger number of patients.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1097/CMR.0000000000000744DOI Listing
May 2021

Factors affecting the mortality rate of patients with cancer hospitalized with COVID-19: a single center's experience.

J Chemother 2021 May 25:1-10. Epub 2021 May 25.

Department of Medical Oncology, Health Science University Kartal Dr. Lütfi Kırdar Training and Research Hospital, İstanbul, Turkey.

The main objective is to define the mortality of patients with cancer admitted to our hospital, their clinical and demographic characteristics, investigate the risk of COVID-19 for patients with cancer, and determine factors that affect the mortality rates of patients with cancer dying of COVID-19. A total of 2401 patients were admitted to our hospital with the diagnosis of COVID-19 from March 11th, 2020, to May 31st, 2020. Ninety-two out of a total of 112 cancer patients were included in this study based on the planned inclusion/exclusion criteria. The clinical, demographic, and laboratory features and treatments provided were studied, and their effect on mortality rates was analyzed. In our study the median age of the patients was 67 years, and 55.4% were male. More than half (56.5%) of our patients had metastasis. The mortality rate was 6.2% in the overall population with COVID-19, whereas it was 23.9% in patients with cancer. The mortality rate in patients with metastasis was statistically significantly higher compared with those without metastasis (34.0% vs. 10.3%  0.008). The mortality rate in patients still smoking was statistically significantly higher than in non-smokers (37.5% vs. 12.5%  0.033). The mortality rates of patients with high average C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), and D-dimer levels were statistically significantly higher than in those without, and the mortality rates of patients with lower average albumin and hemoglobin levels were statistically significantly higher than those without ( 0.001,  0.006,  0.041,  0.001,  0.001, and  0.028, respectively). Having metastases concurrent with COVID-19 was a statistically significant factor predictive of prognosis. Also, high CRP, ferritin, LDH, and D-dimer, and low albumin and hemoglobin were related to increased mortality rates. The predictive and prognostic role of possible factors related to prognosis is still unknown and further large, multicenter prospective studies are needed to confirm these results.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/1120009X.2021.1923153DOI Listing
May 2021

Does systemic anti-tumor therapy increase COVID-19 risk in patients with cancer?

J Oncol Pharm Pract 2021 May 7:10781552211015762. Epub 2021 May 7.

Department of Medical Oncology, Kartal Dr. Lütfi Kirdar City Hospital, Health Science University, İstanbul, Turkey.

Purpose: We aimed to determine the COVID-19 infection rate and determine the factors that affect hospitalization and prognosis in patients receiving systemic chemotherapy (CT), immunotherapy (IT) and molecular-targeted therapies at our hospital within three months after the onset of COVID-19 pandemic.

Materials And Methods: The patients who received systemic treatment at chemotherapy unit with diagnosis of cancer between 11 March 2020 and 11 June 2020 were included. The clinical and demographic characteristics of patients, the systemic treatments that they received (CT, IT, targeted therapies), and the stage of disease were determined. For the parameters that affect the hospitalization of COVID-19 infected patients were also determined.

Results: Among 1149 patients with cancer, 84 of them were infected with COVID-19, and the median age of infected patients was 61.0 (IQR: 21-84) and 60.7% of them were male. As a subtype of cancers lung cancer was more frequent in the patients who infected with COVID compared with non-infected ones and the difference was statistically significant when the underlying malignities were compared (32.1% vs 19.0%,  = 0.031). The hospitalization rate and receiving COVID-19 treatment were more frequent in metastatic patients who were receiving palliative therapy, and the difference was statistically significant ( = 0.01,  = 0.03). In our study, infection rate was similar among patients treated with CT, IT and CT plus targeted therapy; however, fewer COVID-19 infections were seen at patients who received only targeted therapy.

Conclusion: COVID-19 infection is more frequent in cancer patients and tends to be more severe in metastatic cancer patients receiving anticancer treatment, and the continuation of palliative cancer treatments in these patients may cause increased cancer and infection-related morbidity and mortality.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/10781552211015762DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107490PMC
May 2021

Real-life comparison of the afatinib and first-generation tyrosine kinase inhibitors in nonsmall cell lung cancer harboring EGFR exon 19 deletion: a Turk Oncology Group (TOG) study.

J Cancer Res Clin Oncol 2021 Jul 12;147(7):2145-2152. Epub 2021 Jan 12.

Ankara Yildirim Beyazit University, Ankara, Turkey.

Background: The new second-generation tyrosine kinase inhibitors (TKIs) have superior survival outcome and worse toxicity profile when compared with first-generation TKIs according to the results of clinical trials. However, there are limited studies that investigate the efficacy and safety of the new generation TKIs in real-world patients. Thus, we aimed to compare the efficacy and safety of the afatinib, an irreversible inhibitor of ErbB family receptor, and first-generation TKIs in real-world patients.

Materials And Methods: We included advanced nonsmall cell lung cancer (NSCLC) patients who had EGFR exon 19del mutation and treated with afatinib or first-generation TKIs as upfront treatment between 2016 and 2020. All patient's information was collected retrospectively. The study cohort was divided as afatinib arm and erlotinib/gefitinib arm.

Results: A total of 283 patients at the 24 oncology centers were included. The 89 and 193 of whom were treated with afatinib and erlotinib/gefitinib, respectively. After 12.9 months (mo) of follow-up, the median PFS was statistically longer in the afatinib arm than erlotinib/gefitinib arm (19.3 mo vs. 11.9 mo, p: 0.046) and the survival advantage was more profound in younger patients (< 65 years). The 24-mo overall survival rate was 76.1% and 49.5% in the afatinib arm and erlotinib/gefitinib arm, respectively. Although all-grade adverse event (AE) rates were similar between the two arms, grade 3-4 AE rates were higher in the afatinib arm (30.7% vs. 15.2%; p: 0.004).

Discussion: In our real-world study, afatinib has superior survival outcomes despite worse toxicity profile as inconsistent with clinical study results and it is the good upfront treatment option for younger patients and elderly patients who have good performance status.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00432-020-03501-6DOI Listing
July 2021

Does the efficacy of regorafenib differ in chemotherapy refractory metastatic colorectal cancer patients who had mucinous pathology compared to those who had non-mucinous pathology?

Curr Probl Cancer 2021 Jun 22;45(3):100670. Epub 2020 Oct 22.

Department of Medical Oncology, Health Science University Kartal Dr. LütfiKırdar Training and Research Hospital, İstanbul, Turkey.

Purpose: To investigate the importance of mucinous histopathology on the assessment of tumor response in patients with metastatic colorectal cancer (mCRC) receiving regorafenib.

Materials And Method: All patients diagnosed with histologically confirmed mCRC in 2 oncology centers between 2013 and 2018 were retrospectively analyzed. Among 678 patients diagnosed with mCRC, 103 patients were treated with regorafenib. Ninety-four of these patients who had used at least 2 cycles of regorafenib and evaluable for treatment response were included in the analysis. Histopathologically, 18 patients with mucinous adenocarcinoma and 76 patients with nonmucinous adenocarcinoma were compared in terms of response rate and survival durations.

Results: Median follow-up duration of 6 months, median age of the patients was 61 (34-77) years. While 19.1% of the patients had mucinous histology, 80.9% had nonmucinous histology. The overall response rate was significantly lower in the mucinous subgroup than the nonmucinous subgroup (5.6% vs 43.4%, respectively, P = 0.003). Similarly, both progression-free survival (3.0 vs 4.0 months, respectively, P = 0.011) and overall survival duration were shorter in the mucinous subgroup (3.0 vs 7.0 months, P = 0.016, respectively) compared with the nonmucinous subgroup.

Conclusion: The histological subgroup may predict tumor response in mCRC patients receiving regorafenib. Its efficacy on nonmucinous histology had significantly more favorable than mucinous subtype.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.currproblcancer.2020.100670DOI Listing
June 2021

Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

Med Image Anal 2020 08 18;64:101724. Epub 2020 May 18.

Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany; Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany. Electronic address:

Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.media.2020.101724DOI Listing
August 2020

[Potential of methods of artificial intelligence for quality assurance].

Ophthalmologe 2020 Apr;117(4):320-325

Universitäts-Augenklinik Tübingen, Universität Tübingen, Tübingen, Deutschland.

Background: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.

Objective: This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology.

Methods: Relevant aspects from the literature are discussed.

Findings: Systems based on artificial deep neural networks achieve remarkable results in the diagnostics of eye diseases, such as diabetic retinopathy and are very helpful, for example by segmenting optical coherence tomographic (OCT) images and detecting lesion components with high fidelity. To train these algorithms large data sets are required. The quality and availability of such data sets determine the continuous improvement of the algorithms. The comparison between the AI algorithms and physicians for image interpretation has also enabled insights into the diagnostic concordance between physicians. Current challenges include the development of methods for modelling decision uncertainty and improved interpretability of automated diagnostic decisions.

Conclusion: Systems based on AI can support decision making for physicians and thereby contribute to a more efficient quality assurance.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00347-020-01063-zDOI Listing
April 2020

Leveraging uncertainty information from deep neural networks for disease detection.

Sci Rep 2017 12 19;7(1):17816. Epub 2017 Dec 19.

Institute for Ophthalmic Research, Eberhard Karls University, Tübingen, Germany.

Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0-20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-017-17876-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5736701PMC
December 2017

Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer's disease.

Int J Data Min Bioinform 2013 ;7(2):146-65

Center for Advanced Computer Studies, University of Louisiana at Lafayette, 301 E. Lewis St., 201-G Oliver Hall (ACTR), Lafayette, LA 70503, USA.

Alzheimer's Disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approaches that use a combination of clinical assessments. In this study, we compare Naive Bayes (NB) with variations of Support Vector Machines (SVMs) for the automatic diagnosis of AD. 3D Stereotactic Surface Projection (3D-SSP) is utilised to extract features from PET scans. At the most detailed level, the dimensionality of the feature space is very high. Hence we evaluate the benefits of a correlation-based feature selection method to find a small number of highly relevant features; we also provide an analysis of selected features, which is generally supportive of the literature. However, we have also encountered patterns that may be new and relevant to prediction of the progression of AD.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1504/ijdmb.2013.053194DOI Listing
March 2014