Fatih Incekara YOU? Author Swipe

Last 10y

Carl Jung: a life on the edge of reality with hypnagogia, hyperphantasia, and hallucinations Open

Fatih Incekara, Jan Dirk Blom · 2024

Whether the Swiss psychiatrist Carl Jung (1875–1961) became psychotic after his mid-thirties is much debated. His recently published Black Books, a seven-volume journal, reveal new insights into this debate. Based on a phenomenological ana…

Probing the glioma microvasculature: a case series of the comparison between perfusion MRI and intraoperative high-frame-rate ultrafast Doppler ultrasound Open

Ahmad Alafandi, Sadaf Soloukey, Fatemeh Arzanforoosh, Sebastian R. van der Voort, Fatih Incekara , et al. · 2024

Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection Open

Karin A. van Garderen, Sebastian R. van der Voort, Maarten M.J. Wijnenga, Fatih Incekara, Ahmad Alafandi , et al. · 2023

Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic …

Microvasculature Features Derived from Hybrid EPI MRI in Non-Enhancing Adult-Type Diffuse Glioma Subtypes Open

Fatemeh Arzanforoosh, Sebastian R. van der Voort, Fatih Incekara, Arnaud J.P.E. Vincent, Martin J. van den Bent , et al. · 2023

In this study, we used the vessel size imaging (VSI) MRI technique to characterize the microvasculature features of three subtypes of adult-type diffuse glioma lacking enhancement. Thirty-eight patients with confirmed non-enhancing glioma …

Appendix 4 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Excluded patients from TCIA-1p19qDeletion dataset

Online Figure 2 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of importance of imaging and demographic features

Appendix 2 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Radiogenomics algorithm

Appendix 3 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Evaluation metrics

Appendix 5 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Results of mixing EMC/HMC and TCIA datasets

Appendix 3 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Evaluation metrics

Data from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Purpose:Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether t…

Online Figure 1 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of the radiogenomics pipeline

Data from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Purpose:Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether t…

Appendix 5 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Results of mixing EMC/HMC and TCIA datasets

Online Figure 1 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of the radiogenomics pipeline

Appendix 1 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of MR settings

Appendix 4 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Excluded patients from TCIA-1p19qDeletion dataset

Online Figure 2 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of importance of imaging and demographic features

Appendix 1 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Overview of MR settings

Appendix 2 from Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapas, Mayke Gardeniers , et al. · 2023

Radiogenomics algorithm

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann , et al. · 2022

Background Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been develope…

Development and external validation of a clinical prediction model for survival in patients with IDH wild-type glioblastoma Open

Hendrik-Jan Mijderwijk, Daan Nieboer, Fatih Incekara, Kerstin Berger, Ewout W. Steyerberg , et al. · 2022

OBJECTIVE Prognostication of glioblastoma survival has become more refined due to the molecular reclassification of these tumors into isocitrate dehydrogenase ( IDH ) wild-type and IDH mutant. Since this molecular stratification, however, …

Noninvasive differentiation of molecular subtypes of adult nonenhancing glioma using MRI perfusion and diffusion parameters Open

Ilanah J. Pruis, Stephan R Koene, Sebastian R. van der Voort, Fatih Incekara, Arnaud J.P.E. Vincent , et al. · 2022

Background Nonenhancing glioma typically have a favorable outcome, but approximately 19–44% have a highly aggressive course due to a glioblastoma genetic profile. The aim of this retrospective study is to use physiological MRI parameters o…

The Erasmus Glioma Database (EGD): Structural MRI scans, WHO 2016 subtypes, and segmentations of 774 patients with glioma Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann , et al. · 2021

Intraoperative B-Mode Ultrasound Guided Surgery and the Extent of Glioblastoma Resection: A Randomized Controlled Trial Open

Fatih Incekara, Marion Smits, Linda Dirven, Eelke M. Bos, Rutger K. Balvers , et al. · 2021

Background Intraoperative MRI and 5-aminolaevulinic acid guided surgery are useful to maximize the extent of glioblastoma resection. Intraoperative ultrasound is used as a time-and cost-effective alternative, but its value has never been a…

Evaluating glioma growth predictions as a forward ranking problem Open

Karin A. van Garderen, Sebastian R. van der Voort, Maarten M.J. Wijnenga, Fatih Incekara, Georgios Kapsas , et al. · 2021

The problem of tumor growth prediction is challenging, but promising results have been achieved with both model-driven and statistical methods. In this work, we present a framework for the evaluation of growth predictions that focuses on t…

Imaging and Resection of Glioblastomain light of molecular markers Open

Fatih Incekara · 2021

Mapping tumour heterogeneity with pulsed 3D CEST MRI in non-enhancing glioma at 3 T Open

Esther A. H. Warnert, Tobias Wood, Fatih Incekara, Gareth J. Barker, Arnaud J.P.E. Vincent , et al. · 2021

WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann , et al. · 2020

Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a s…

WHO 2016 subtyping and automated segmentation of glioma using multi-task\n deep learning Open

Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, Georgios Kapsas, Renske Gahrmann , et al. · 2020

Accurate characterization of glioma is crucial for clinical decision making.\nA delineation of the tumor is also desirable in the initial decision stages but\nis a time-consuming task. Leveraging the latest GPU capabilities, we developed\n…

Fetching topic information...