János Báskay

János Báskay

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

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János Báskay has a total of 27 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Open Access

    Multimodal, Personalized Treatment of Pineal Region Tumors in Adulthood—A Single Center Study

    by Tamás Mezei, János Báskay, Péter Pollner, Lukács Németh, Balázs Markia, Gábor Nagy, András Bajcsay, László Sipos

    Background: Tumors of the pineal region account for less than 1% of supratentorial neoplasms in adults and represent a distinct neuro-oncological challenge. Their management requires a multidisciplinary and multimodal approach. Traditionally, direct surgical resection was considered the primary treatment modality. Recent advances in minimally invasive techniques and onco-radiotherapy have paved the way for safer and more personalized treatment strategies, in line with the principles of precision medicine. This study aims to present our institutional approach, which relies on a combination of endoscopic and radiotherapy-based techniques. Methods: A retrospective, single-center clinical study was conducted involving 28 adult patients who underwent endoscopic third ventriculostomy and biopsy of a pineal region tumor between January 2014 and March 2025. Descriptive statistics, permutation tests with bootstrap-derived confidence intervals, Fisher’s exact test, and Kaplan–Meier survival analysis were applied for data evaluation. Results: Endoscopic intervention resulted in clinical improvement in 78% of cases. A significant increase in performance status was observed in the postoperative period (<0.001) compared to preoperative results. Radiotherapy contributed to either tumor regression or disease stabilization. Conclusions: Based on our findings, the combination of endoscopic intervention and personalized radiotherapy represents a safe and effective treatment strategy, offering a compelling alternative to direct surgical resection, which is reserved as a second-line treatment.

  • Open Access

    Censoring Sensitivity Analysis for Benchmarking Survival Machine Learning Methods

    by János Báskay, Tamás Mezei, Péter Banczerowski, Anna Horváth, Tamás Joó, Péter Pollner

    (1) Background: Survival analysis models in clinical research must effectively handle censored data, where complete survival times are unknown for some subjects. While established methodologies exist for validating standard machine learning models, current benchmarking approaches rarely assess model robustness under varying censoring conditions. This limitation creates uncertainty about model reliability in real-world applications where censoring patterns may differ from training data. We address this gap by introducing a systematic benchmarking methodology focused on censoring sensitivity. (2) Methods: We developed a benchmarking framework that assesses survival models through controlled modification of censoring conditions. Five models were evaluated: Cox proportional hazards, survival tree, random survival forest, gradient-boosted survival analysis, and mixture density networks. The framework systematically reduced observation periods and increased censoring rates while measuring performance through multiple metrics following Bayesian hyperparameter optimization. (3) Results: Model performance showed greater sensitivity to increased censoring rates than to reduced observation periods. Non-linear models, especially mixture density networks, exhibited higher vulnerability to data quality degradation. Statistical comparisons became increasingly challenging with higher censoring rates due to widened confidence intervals. (4) Conclusions: Our methodology provides a new standard for evaluating survival analysis models, revealing the critical impact of censoring on model performance. These findings offer practical guidance for model selection and development in clinical applications, emphasizing the importance of robust censoring handling strategies.


  • Open Access

    Microarchitectural Study of the Augmented Bone Following a Modified Ridge Splitting Technique: Histological and Micro-Computed Tomography Analyses

    by Dorottya Pénzes, Csilla Szerencse, Martin Major, György Szabó, Endre Kontsek, János Báskay, Péter Pollner, Bence Tamás Szabó, Csaba Dobó-Nagy, Dániel Csete, et al.

    Objectives: The aim of this matched prospective cohort study was to examine the microarchitecture of the augmented bone following a modified alveolar ridge splitting procedure and compare it to that of native bone. Methods: In the test group, patients underwent a modified ridge split osteotomy procedure to restore the width of the posterior segment of the mandible. Patients with sufficient bone width for dental implant placement in the posterior region of the mandible following 3-month-long spontaneous healing after tooth removal were included in the control group. In both study groups, bone biopsy samples were harvested and dental implants were placed. Histomorphometry and micro-CT analysis were performed. Results: Altogether, 15 patients were included in this study (7 patients in the test group, with 14 bone core biopsies harvested, and 8 patients in the control group, with 13 bone core biopsies harvested). Percentage bone volume (BV/TV) in the micro-CT analysis (22.088 ± 8.094% and 12.075 ± 4.009% for the test and control group, respectively) showed statistically significant differences between study groups. Conclusions: Based on histological and micro-CT analyses, the modified ridge splitting procedure with autologous bone block harvested from the retromolar area results in a dental implant recipient bone microarchitecture superior to that of the extraction sockets left to heal undisturbed for a 3-month-long healing period.

  • Open Access

    Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?

    by János Báskay, Dorottya Pénzes, Endre Kontsek, Adrián Pesti, András Kiss, Bruna Katherine Guimarães Carvalho, Miklós Szócska, Bence Tamás Szabó, Csaba Dobó-Nagy, Dániel Csete, et al.

    Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. Methods: A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker’s CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Results: Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with p-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland–Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. Conclusions: This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.