bluuebunny/crossref_metadata_2025_split huggingface dataset:
P10.05.B ADVANCED MACHINE LEARNING TO DETECT HETEROGENEITY OF CELL CYCLE ALTERATIONS DURING TMZ CHEMOTHERAPY AT SINGLE CELL LEVEL
Authors: Villa G , Kückelhaus J , Yabo Y , Schnell O , Delev D , Benotmane J K , Heiland H D 
Published: October 2024

Abstract

BACKGROUND
Glioblastoma (GBM) stands as the most aggressive tumor of the CNS. Current treatment typically involves surgical intervention followed by cycles of Temozolomide (TMZ) chemotherapy in combination with radiotherapy. However, the efficacy of these treatments remains limited due to GBM’s ability to adapt and resist therapy. TMZ treatment leads to significant alterations in the cell cycle of GBM cells, with a decrease in cells in the G0-G1 phase and an increase in cells in the S or G2 phases. This shift in cell cycle distribution suggests a dual response: re-entry of tumor cells into the cell cycle and concurrent arrest induced by TMZ, potentially contributing to tumor resistance and disease progression. Here, we leverage the variable patterns of Ki-67, a marker of cellular proliferation, which expression fluctuates throughout the cell cycle, peaking during S, G2, and M phases to evaluate cellular response to TMZ treatment across various primary cell lines and tissue samples.

MATERIAL AND METHODS
We developed an image-based approach to investigate and reconstruct the cell cycle using advanced machine learning for pattern recognition to identify the detailed cell cycle status on single cell level. To characterized primary cell lines (n=6), we performed whole-genome DNA sequencing, whole-genome methylation sequencing and RNA-sequencing. For functional read-outs, we performed large-scale TMZ treatment in a time- and concentration dependent manner in cell culture and GBM-tissue slice models.

RESULTS
Our machine learning model of cell cycle reconstructions demonstrated a robust reconstruction of detailed cell cycles stages across all cell lines with the majority of cells assigned to G0-G1 stated without treatment. TMZ treatment resulted in a significant shift towards enhanced S/G2 phase (p &lt; 0.02) in 3 of 6 tested cell lines. Pseudo-time reconstruction revealed a dose-dependent alteration of the cell cycle showing significant changes with TMZ-doses below 50µmol. Cell lines which enhanced stem-like phenotypes based on epigenetically and transcriptionally characterisation demonstrated no cell cycle alterations even in high-dose treatment conditions (&gt;150µmol) regardless their MGMT promotor methylation status. This heterogeneity underscores the complexity of GBM and highlights the importance of personalized treatment strategies.

CONCLUSION
Our findings provide valuable insights into GBM’s cell cycle alterations and adaptive mechanisms of resistance independent of the MGMT methylation status as a known biomarker for TMZ response. TMZ-induced cell cycle alterations can provide a novel biomarker to detect treatment response and offer a foundation for developing personalized therapeutic approaches.
P10.16.A FULLY AUTOMATED APPROACH TO PREDICT THE VISUAL OUTCOME AFTER PITUITARY ADENOMA SURGERY BASED ON PREOPERATIVE MRI USING DEEP LEARNING
Authors: Zhang Y 
Published: October 2024

Abstract

BACKGROUND
Accurate prediction of the postoperative visual recovery is important to clinical decision-making for pituitary adenoma patients, while still remains a challenge in clinical practice. The purpose of the present study is to predict the visual outcome after pituitary adenoma surgery based on preoperative MRI using a fully automated approach with convolutional neural network (CNN).

MATERIAL AND METHODS
A total number of 205 qualified pituitary adenoma patients were retrospectively enrolled and subdivided into the recovery cohort (n = 132) and the non-recovery cohort (n = 73), according to results of the visual testing at least six months after endoscopic endonasal transsphenoidal surgery. Based on preoperative T2-weighted images, a fully automated model was built by connecting two CNN models for optic chiasm segmentation and visual outcome prediction, respectively. The optic chiasm automatically segmented by the state-of-the-art nnU-Net architecture were utilized for establishment of the predictive model for visual outcome based on ResNet18. The proposed model was first trained in the development set (n = 163) and then applied in the independent test set (n = 42). Moreover, the test set was further divided into three subsets according to different MR scanners to evaluated the generalizability of our model.

RESULTS
The CNN model for optic chiasm segmentation achieved an average dice similarity coefficient of 0.831. The predictive model showed feasible performances in predicting the postoperative visual outcome, achieving an accuracy of 0.833 in the test set, with area under the receiver operating characteristic curve of 0.857, sensitivity of 0.815 and specificity of 0.867. Moreover, our model also demonstrated good generalizability, with accuracy of 0.813-0.875 across different MR scanners.

CONCLUSION
The proposed fully automated model represented the potential to be an accurate and generalizable approach for the prediction of the postoperative visual recovery of pituitary adenoma patients.
P10.23.B PREDICTING LOCAL CONTROL OF BRAIN METASTASES AFTER STEREOTACTIC RADIOSURGERY: A FUSION OF CLINICAL, RADIOMICS AND DEEP LEARNING FEATURES
Authors: Kanakarajan H , De Baene W , Sitskoorn M , Hanssens P 
Published: October 2024

Abstract

BACKGROUND
Timely identification of local failure after Stereotactic Radiosurgery (SRS) offers the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life. Previous studies showed that the addition of either radiomics or deep learning features to clinical features increased the accuracy of the models in predicting local control (LC) of brain metastases after SRS. To date, however, no study combined both radiomics and deep learning features together with clinical features to develop machine learning algorithms to predict LC of brain metastases. In this study, we examined whether a model trained with a combination of all these features could predict LC better than models trained with only a subset of these features.

MATERIAL AND METHODS
Pre-treatment brain MRIs and clinical data were collected retrospectively for 129 patients at the Gamma Knife Center of Elisabeth-TweeSteden Hospital (ETZ), Tilburg, The Netherlands. The patients were split into 103 patients for training and 26 patients for testing. The segment-based radiomics features were extracted using the radiomics feature extractor of the python radiomics package. The deep learning features were extracted using a fine-tuned 3D ResNet model and then combined with the clinical and radiomics features. A Random Forest classifier was trained with the training data set and then tested with the test data set. The performance was compared across 4 different models trained with clinical features only, clinical and radiomics features, clinical and deep learning features, and clinical, radiomics and deep learning features.

RESULTS
The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.82 and an accuracy of 75.6%. The prediction model with the combination of clinical and radiomics features demonstrated an AUC of 0.880 and an accuracy of 83.3% whereas the prediction model with the combination of clinical and deep learning features demonstrated an AUC of 0.863 and an accuracy of 78.3%. The best prediction performance was associated with the model that combined the clinical, radiomics and deep learning features with an AUC of 0.886 and 87% accuracy.

CONCLUSION
Machine learning models built on radiomics features and deep learning features combined with patient characteristics show potential to predict LC after SRS with high accuracy. The promising findings from this study demonstrate the potential for early prediction of SRS outcome for brain metastasis prior to treatment initiation and might offer the opportunity for appropriate treatment modifications that may result in improved treatment outcomes, patient survival, and quality of life.
P11.01.A ACROMEGALY’S EFFECT ON COGNITIVE FUNCTIONS IN PATIENTS WITH PITUITARY ADENOMA
Authors: Bala A , Olejnik A , Dziedzic T , Kunert P 
Published: October 2024

Abstract

BACKGROUND
Acromegaly is a condition caused by pituitary adenomas, which results in increased growth hormone (GH) secretion. For a long time, the main concern in acromegalic patients was on the physical symptoms and problems, which frequently overshadowed the possible neuropsychological outcomes. As a result, the impact of GH overproduction on cognitive functioning is still unclear. The study aimed to evaluate the association between acromegaly and cognitive performance among individuals with pituitary tumors.

MATERIAL AND METHODS
The study included 24 patients with pituitary tumors secreting GH (age: 42.9±11.7; years of education: 12.8±3.6) and a control group of 24 demographically matched healthy participants. The study excluded participants who had a history of neurological or psychiatric illnesses, substantial visual field abnormalities, or were taking chronic medication for another illness. Subjects were tested with a comprehensive battery of neuropsychological tests, assessing memory (California Verbal Learning Test - CVLT, Rey-Osterrieth Complex Figure (ROCF) - recall, Digit Span from WAIS-R), attention (Color Trails Test, Visual Elevator from TEA), verbal fluency (semantic and phonemic), naming (Boston Naming Test), visuospatial functions (Rey-Osterrieth Complex Figure - copy, Block Design from WAIS-R), and executive functions (Tower of London - ToL, Stroop Test). To avoid the influence of medication intake, patients with adenoma underwent cognitive evaluations at the start of their medical therapy.

RESULTS
The study found significant differences in functioning between patients and healthy individuals, specifically in verbal (delayed recall in CVLT: p &lt; 0.01) and visual (ROCF: p &lt; 0.01) memory, attention (Visual Elevator from TEA: p &lt; 0.05), executive functions (ToL extra moves: p &lt; 0.05), and working memory (Digit Span backwards: p &lt; 0.05). There were no significant differences in naming, verbal fluency, or visuospatial skills. Larger tumors were correlated with greater deficiencies of attention (Visual Elevator from TEA: r = -0.6, p &lt; 0.05) and verbal memory (delayed recall in CVLT: r = -0.4, p &lt; 0.05). However, there were no associations between test outcomes and GH levels.

CONCLUSION
Overproduction of GH can have an impact on cognitive processes such as memory, attention, and executive functioning. Hence, the neuropsychological evaluation should be considered the gold standard in the diagnostic process of this group of patients.
P11.07.B RELIABILITY OF POSTOPERATIVE NEUROPSYCHOLOGICAL SCREENING IN PATIENTS WITH PRIMARY BRAIN TUMORS BY VIDEO CONSULTATION AND IN-PERSON
Authors: van Buijtene W , Butterbrod E , Rutten G , Gehring K 
Published: October 2024

Abstract

BACKGROUND
Since COVID-19 there has been an increase in consultations with patients in neuro-oncological practice via video teleconferencing (VTC). While VTC can increase access to care, the degree to which cognitive screening can be reliably conducted via VTC in brain tumor patients is unclear. The aim of this study is to investigate reliability of a predominantly computerized cognitive screening conducted via VTC (Teams) vs in-person in a Dutch hospital.

MATERIAL AND METHODS
Glioma and meningioma patients underwent an in-person neuropsychological screening as part of standard care before surgery. Between May 2020 and October 2022, patients underwent their 3-month follow-up as part of their standard care via VTC due to COVID. Before May 2020, patients underwent this follow-up in-person. The screening consisted of a computerized battery (CNS vital signs) and paper and pencil tasks (Fluency, Digit span forward and backward). We compared the stability of raw scores over time of patients undergoing VTC vs. in-person administration with intraclass correlation coefficients (ICCs, continuous scores) and absolute agreement (non-continuous scores). We also statistically compared percentages of patients showing reliable change between groups (α=0.05).

RESULTS
Compared to the in-person group (n=112), the VTC (n=112) group was younger (51±14 vs 58±14 years), contained more male patients (56% vs 34%) and more patients with higher education (49% vs 29%). The groups did not differ on other sociodemographic, tumor or treatment characteristics. The majority of test measures for both the VTC (13 out of 15) and in-person group (10 out of 15) had acceptable to excellent ICC values (range 0.50-0.91 vs 0.61-0.87). ICCs of reaction time scores on simpler tests, the Stroop test part 1 (0.45 vs 0.47), and a Continuous Performance test (0.37 vs 0.46), and of a Visual Recognition test (0.33 vs 0.44) were low for both groups. ICCs of a Verbal Recognition test (0.44 vs 0.65) and Digit Span test backward (0.44 vs 0.65) were low only for the VTC group. The degree of agreement on non-continuous measures was similar for both groups, except for Continuous Performance test accuracy. Significantly more patients in the VTC group showed reliable improvement on the Continuous Performance test (15% vs 11%).

CONCLUSION
Neuropsychological screening via video consultation is a promising method, as it can provide opportunities for more patients to receive care from home. Reliability was comparable for VTC and in-person administration for most measures. Performances over time assessed via VTC appeared less stable for Digit span backward, a paper and pencil test, and for Verbal Recognition accuracy, a computerized test. Future research into patient characteristics of the VTC group, and consequences for performances, is warranted.
P12.14.A PROFILING THE PERIPHERAL IMMUNE RESPONSE FOLLOWING NEUROSURGICAL RESECTION OF GLIOBLASTOMA
Authors: Patterson A B , Zeitlberger A M , Bozinov O , Ludewig B , Pikor N B , Neidert M C 
Published: October 2024

Abstract

BACKGROUND
To enhance the efficacy of immunotherapy against glioblastoma, we aim to develop a strategy for the ex-vivo expansion of tumor antigen-specific T cells, followed by an adaptive T cell transfer directly into the tumor cavity. This pilot project, investigating the peripheral immune response, will address the potential release of tumor antigens into the periphery during surgery by examining if there is an increase in the number of antigen-specific T cells following the resection.

MATERIAL AND METHODS
Patients are enrolled in the study before surgery and peripheral blood mononuclear cells (PBMCs) are isolated from venous blood collected as a baseline time point. A tissue lysate is created from bulk tumor tissue during surgery as the source of antigens for later cell stimulation experiments. A single cell suspension made from tumor tissue will provide two populations of cells: “normal” and “tumor-enriched” to validate the use of lysate-derived antigens. Patients are requested to return to the clinic 2, 4, and 12 weeks after surgery where PBMCs are isolated and viably frozen until all time points are collected. The tumor lysate, cells, and PBMCs are co-incubated and the expression profiles of PBMCs are examined via flow cytometry using a high-dimensional 30 marker panel. FACS-sorted antigen-specific T cells will further be profiled via single cell 5’ sequencing (10x Chromium V3) and functionally tested using autologous patient-derived organoid killing assays.

RESULTS
Preliminary data using longitudinal blood sampling shows that autologous lysate elicits activation of CD8+ T cells in patient PBMCs, especially 2 weeks after surgery, according to several classical T cell activation markers. Co-incubation conditions have been examined to determine the kinetics of CD137 expression, with 12 hours of incubation revealing the highest expression of CD137 over 48 hours. Concentrations of tumor lysate have also been compared between 100ng/mL and 3µg/mL. Interestingly, lower protein concentrations have shown higher activation of T cells. Lysate protein concentrations, cell ratios, and co-incubation times are tested in patient samples to ensure that the highest expression of CD137+ T cells is captured and analyzed.

CONCLUSION
The completion of this study will determine the best clinical time point for T cell collection of patient PBMCs for antigen-specific T cell expansion and subsequent administration of the immunotherapy directly into the tumor cavity.
P13.03.B CLINICAL APPLICATION OF HYPER ARC TECHNOLOGY FOR BRAIN METASTASES: INSIGHTS FROM A SINGLE INSTITUTION EXPERIENCE
Authors: Skoblar Vidmar M , Smrdel U 
Published: October 2024

Abstract

BACKGROUND
Hyper Arc is an innovative software introduced at the Institute of Oncology Ljubljana in June 2020. It enables high-precision treatment of multiple brain lesions with a single isocenter and with the potential to address an unlimited number of targets. This software is particularly useful in the local treatment of brain metastases, which are increasingly common causes of morbidity and mortality in patients with malignant diseases. Based on the latest research and guidelines, stereotactic radiosurgery is considered the primary choice to improve survival and may be the best treatment choice for multiple brain metastases.

MATERIAL AND METHODS
From June 2020 to December 2023, a total of 525 stereotactic radiosurgeries for brain metastases were performed. We analyzed all irradiated patients.

RESULTS
The most common metastases treated were from lung cancer (45.7%), followed by breast cancer (17.7%), malignant melanoma (16.6%), kidney (13.5%), digestive tract (3.8%), testes (1.1%), thyroid (1%) and ovaries (0.6%). The number of procedures increased over the years, with 24 in 2020, 121 in 2021, 172 in 2022, and 203 in 2023. 68.4% of patients are still alive. The highest number of irradiated metastases was 19, completed in just 20 minutes. Before the introduction of this technique, only 5 to 6 metastases could be treated due to the need for individual isocenters for each lesion. With the new technology, the same amount of time is required whether treating one or multiple metastases.

CONCLUSION
The treatment of brain metastases has become an increasingly complex field that requires multidisciplinary decisions regarding local and systemic treatment strategies as well as follow-up care. In the past two decades, the role of radiotherapy evolved from being solely palliative to offering ablative local treatments that can potentially improve survival rates. Hyper Arc is an innovative software for implementing stereotactic radiosurgery allowing monoisocentric irradiation of multiple lesions in a short time. This has led to an increase in the number of stereotactic radiosurgery performed at the Institute of Oncology Ljubljana.
P14.10.B DIGITAL AND AI TECHNOLOGY APPIED IN NEUROSURGERY PROMOTES THE FORMATION OF A NEW PARADIGM OF PREOPERATIVE PLANNING, VIRTUAL SURGERY & INTRAOPERATIVE GUIDANCE FOR BRAIN TUMORS
Authors: Yang X , Zhang P , Guo J , Shi W , Chen T , Chen T 
Published: October 2024

Abstract

BACKGROUND: I
n this study, we aims to establish a new paradigm of preoperative planning, virtual surgery, and intraoperative guidance for brain tumor surgery, based on a new preoperative planning and intraoperative guidance system for brain tumors.

MATERIAL AND METHODS
(1) Complete multimodal cranial imaging, functional mapping, and fiber tracing before surgery. Reconstruct the overall structure of the brain, including the cortical sulcus and gyrus, cortical vein, functional area adjacent to the brain tumors, and their connectivity fiber; (2) Analyze the topological pathways surrounding brain lesions and complete preoperative planning; (3) Applying virtual reality and augmented reality technology to surgical decision-making, preoperative training, and virtual surgery to enhance the realism of preoperative planning and the effectiveness of surgical training; (4) Introducing augmented reality technology in surgical positioning and scalp flap planning; (5) Based on the automatic partitioning and topological structure of the cortex, determine cortical approach to the tumor; (6) Real time fusion of surgical navigation and ultrasound imaging to guide the resection of deep tumors.

RESULTS
(1) Through virtual reality and augmented reality technology, preoperative topological structure analysis of tumors can be achieved, which can predict neural function remodeling and facilitate the maximum removal of brain parenchymal tumors; (2) Virtual reality technology and augmented reality technology, through visualization and three-dimensional presentation, promote young doctors to understand the relationship between the brain structure at the lesion site and the overall brain structure, as well as the relationship between the lesion and brain function and surrounding blood vessels. They are a new way of surgical training; (3) The guidance of digital technology can achieve optimal positioning and skin flap design, avoiding the mismatch between the skin flap designed solely based on the surgeon's experience and the surgical approach in a visual way, which can cause difficulties in surgical resection; (4) The automated partitioning technology and topological structure display of the cerebral cortex help to enhance understanding of cortical partitioning and indication of cortical pathways during surgery, which can replace electrophysiological techniques in judging the central sulcus; (5) The fusion technology of neuronavigation and intraoperative ultrasound can compensate for the non real-time nature of navigation and the limitation of ultrasound only indicating lesion sound and shadow, achieving real-time and good structural guidance during surgery.

CONCLUSION
We believe that the development of digital and intelligent technology will continue to help us establish a developing strategy for precision neurosurgery in the new era.