Leblebici, Asım
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Leblebici, Asim
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asim.leblebici@gmail.com
asimleblebici@iyte.edu.tr
asimleblebici@iyte.edu.tr
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01.01. Units Affiliated to the Rectorate
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Current Staff
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1NO POVERTY
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2ZERO HUNGER
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3GOOD HEALTH AND WELL-BEING
3
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4QUALITY EDUCATION
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5GENDER EQUALITY
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6CLEAN WATER AND SANITATION
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7AFFORDABLE AND CLEAN ENERGY
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8DECENT WORK AND ECONOMIC GROWTH
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10REDUCED INEQUALITIES
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11SUSTAINABLE CITIES AND COMMUNITIES
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
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13CLIMATE ACTION
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14LIFE BELOW WATER
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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14
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140
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4
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18
Citations
134
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3
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3
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259/211
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3
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| Journal | Count |
|---|---|
| Journal of Basic and Clinical Health Sciences | 2 |
| European Journal of Therapeutics | 1 |
Current Page: 1 / 1
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Now showing 1 - 3 of 3
Article The Association of Vertebrobasilar System Morphology and Geometry With the Posterior and Anterior Ischemic Stroke(Pera Yayıncılık Hizmetleri, 2025) Leblebici, Asım; Demirtas, İsmet; Ayyıldız, Sevilay; Ayyıldız, Behçet; Kuş, Koral Çağlar; Ayran, Ayşegül; Kurt, Mustafa AyberkObjective: Morphometric and geometric variations in the vertebrobasilar system (VBS) may influence cerebral hemodynamics, potentially contributing to ischemic strokes in both anterior and posterior circulatory territories. This study aimed to investigate the association between VBS morphology and ischemic stroke localization. Methods: This retrospective observational study analyzed multidetector computed tomography angiography images from 431 patients (187 females, 244 males, mean age: 65.3 ± 14.6 years). Patients were categorized into three groups: anterior circulation ischemic stroke (ACIS, n=184), posterior circulation ischemic stroke (PCIS, n=88), and control subjects (n=159). Morphometric parameters were assessed using 3D Slicer software. Results: Significant differences in basilar artery (BA) length were observed between stroke groups and controls, with ACIS and PCIS groups exhibiting longer BA lengths (p<0.05). Males had significantly longer vertebral artery (VA) lengths than females in the control and ACIS groups (p value < 0.05). The vertebrobasilar junction angle was significantly wider in females than in males (p value = 0.046). BA bending was predominantly directed to the right across all groups, with no significant differences between the stroke and control groups. VA dominance was more frequent on the left in ACIS and the right in PCIS, while VA hypoplasia was less common in stroke patients compared to controls, contrary to previous reports. Conclusion: While certain morphometric and geometric variations in the VBS were observed, the evidence for a direct association between these characteristics and the localization of ischemic stroke was limited and inconclusive. These findings suggest that vertebrobasilar morphology may not independently determine stroke localization.Article Ai-Assisted Survival Prediction in Colorectal Cancer: a Clinical Decision Support Tool(Dokuz Eylul Univ inst Health Sciences, 2024) Misirlioglu, Huseyin Koray; Leblebici, Asim; Calibasi-Kocal, Gizem; Ellidokuz, Hulya; Basbinar, YaseminPurpose: This study was planned to determine the problems and affecting factors that children encounter Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model's accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.Article Ai-Assisted Survival Prediction in Colorectal Cancer: a Clinical Decision Support Tool(dergipark, 2021) Leblebici, Asım; Mısırlıoğlu, Hüseyin Koray; Koçal, Gizem Çalıbaşı; Ellidokuz, Hülya; Başpınar, YaseminPurpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model’s accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.