bluuebunny/crossref_metadata_2025_split huggingface dataset:
IMG-04. ASSOCIATION BETWEEN MICROSTRUCTURAL INTEGRITY OF BRAIN SUBSTRUCTURES AND NEUROCOGNITIVE OUTCOMES: A MULTI-TRAJECTORY ANALYSIS IN CHILDHOOD BRAIN TUMOR SURVIVORS
Authors: Oglesby Ryan , Pathiravasan Chathurangi , Peterson Rachel , Acharya Sahaja 
Published: June 2024

Abstract

BACKGROUND
The relationship between the brain’s microstructural integrity and neurocognitive outcomes is not well characterized in childhood brain tumor survivors. In healthy children, mean diffusivity (MD) generally decreases and fractional anisotropy (FA) increases with age as the brain progressively myelinates and develops. Increasing MD and decreasing FA is suggestive of microstructural injury. We investigate the association between longitudinal neurocognitive outcomes and change in MD and FA in childhood brain tumor survivors.

METHODS
Patients diagnosed with a brain tumor at &lt; 20 years of age with ≥ 2 neurocognitive assessments and paired DTI at each neurocognitive timepoint were retrospectively identified and eligible for inclusion. Multi-trajectory modeling was applied to cluster patients into distinct neurocognitive trajectories based on intelligent quotient, processing speed and working memory. Linear mixed models were used to determine whether the clusters were associated with change in MD and FA over time.

RESULTS
A total of 61 patients were eligible with 151 neurocognitive assessments paired with DTI. Mean follow-up was 4.8 years. Multi-trajectory modeling identified two clusters of patients with distinct neurocognitive trajectories. Compared to the low-performance group, the high-performance group (45.5%) had higher baseline neurocognitive scores with less decline in all neurocognitive outcomes. The high-performance group was also associated with a greater decrease in MD and greater increase in FA of the hippocampus and several white matter tracts such as the corpus callosum, pontine crossing tract, anterior limb of the internal capsule and inferior cerebellar peduncle, after adjusting for age and sex (all p&lt;0.05). Additionally, the high-performance group was associated with a greater increase in FA in the superior, middle and inferior frontal gyri (all p&lt;0.05).

CONCLUSIONS
The high-performance neurocognitive group was associated with greater microstructural integrity over time in multiple brain substructures compared to the low-performance group, as measured by change in fractional anisotropy and mean diffusivity.
IMG-05. USING CONVOLUTIONAL NEURAL NETWORKS TO IDENTIFY COMMON MOLECULAR ALTERATIONS IN PEDIATRIC BRAIN TUMORS
Authors: Free Taylor , Moore Jonathon , Taras Michael , Ganapathy Shankar , Sreedher Gayathri , Wright Erin 
Published: June 2024

Abstract

BACKGROUND
Artificial intelligence (AI) and deep learning have had increased uses in healthcare. Initial studies have shown it is possible to predict multiple tumor types in adults and children using convolutional neural networks (CNN), a form of deep learning, using MRIs. In pediatric CNS tumors, molecular alterations are used in both diagnosis and treatment decisions. This project aims to verify that we can use deep learning to help diagnose and molecularly classify primary pediatric CNS tumors. Previously, we used Recurrent Health’s platform to predict high vs. low grade CNS lesions to &gt;95% recall and 85% precision in pediatric patients. The objective of this study is to validate the AI’s ability to predict common genetic alterations with 90% accuracy.

METHODS
This retrospective study evaluated patients (ages 0-21) with histologically confirmed primary CNS tumors that underwent BRAF molecular analysis. We collected demographic data: age, tumor location, and presence of BRAF mutations/fusions. The data was divided into a training and validation set (80%/20% split). To determine the viability of models, we will measure specificity, sensitivity, and F1 Score (Sorenson-Dice coefficient) for predicting genetic alterations. Model score confidence was achieved through K-fold cross-validation and statistical power analysis with a finite population coefficient to determine the appropriate sample size.

RESULTS
Our retrospective test included 136 patients with multiple scans for training, model validation and testing. The CNN was able to predict presence of BRAF fusions with &gt;95% recall/sensitivity, &gt;76% precision/specificity and F1/accuracy &gt;87% while predicting BRAFV600 mutations with &gt;75% recall/sensitivity, &gt;95% precision/specificity and F1/accuracy &gt;85%. These metrics indicate strong discrimination in classification.

CONCLUSIONS
Preliminary data suggests the accuracy of the model in identifying the presence of BRAF mutations, providing a potential opportunity for a non-invasive tool to help aid in diagnosis and help guide initiation of targeted therapy.
IMG-07. PEDIATRIC CRANIAL INCIDENTALOMAS: A SYSTEMATIC REVIEW UTILIZING MACHINE LEARNING
Authors: Cochrane Anne M , Abdelbaki Mohamed S 
Published: June 2024

Abstract

BACKGROUND
The use of diagnostic neuroimaging in the care of pediatric patients continues to increase, leading to a rise in incidentally discovered lesions. The understanding of appropriate assessment and management of these findings is critical to providing comprehensive and timely care. This review aims to analyze the current literature to characterize diagnostic and prognostic characteristics of incidentally found brain tumors in pediatric patients.

METHODS
Using the guidelines under the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), Pubmed and Medline (NCBI) databases were searched using selected keyword combinations and machine learning-assisted software. Case reports, case series, and retrospective cohort studies published up to December 2023 were included.

RESULTS
A total of 20 studies encompassing 627 patients were included in the review. The most common indications for imaging were headache (204; 33%), seizures (96; 15%), and trauma (78; 12%). Of studies providing information regarding localization, lesions were found in the supratentorial region in 327 (52%) cases and infratentorial region in 190 (30%). Of studies reporting imaging characteristics, T1 hypointensity (183/302; 61%), T2 hyperintensity (410/497; 82%), and no contrast enhancement (58/97; 60%) were the most common findings. Four hundred and thirty (68%) patients were observed after diagnosis, 83 (13%) patients underwent surgical intervention at diagnosis, and 40 (6%) patients underwent surgery after observation. Of 91 patients with histopathology data available, pilocytic astrocytoma (19; 21%) was most commonly reported.

CONCLUSION
In this study, we provide a review of diagnostic characteristics of incidentally discovered brain tumors in the pediatric population. Management of such lesions has changed greatly over the past several decades, and risks of surgical intervention should be weighed against a more conservative management and interval monitoring approach. These results may help guide discussions regarding disclosure of findings and recommendations for additional clinical management.
IMG-09. A DEEP LEARNING-BASED APPROACH FOR BRAIN TISSUE EXTRACTION USING MULTI- AND SINGLE-PARAMETRIC MRI IN PEDIATRICS
Authors: Gandhi Deep B , Gottipati Anurag , Tu Wenxin , Familiar Ariana , Haldar Shuvanjan , Khalili Neda , Jain Paarth , Viswanathan Karthik , Storm Phillip B , Resnick Adam C , Ware Jeffrey B , Vossough Arastoo , Nabavizadeh Ali , Kazerooni Anahita Fathi 
Published: June 2024

Abstract

BACKGROUND
Skull-stripping, the process of extracting brain tissue from MR images, is an important step for tumor segmentation and downstream imaging-based analytics such as AI-powered radiomic feature extraction. Existing skull-stripping models, designed for pediatric or adult patients, show limitations in accurately segmenting tumors in sellar/suprasellar regions. This limitation hinders their reliable application across different histologies of pediatric brain tumors. We propose a deep learning approach for fully automated skull-stripping, compatible with both single- or multi-parametric MRI sequences.

METHODS
We developed 3D nnU-Net models trained on preprocessed MRI sequences (including pre- and post-contrast T1w, T2w, and FLAIR) from 336 patients with brain tumors across multiple tumor histologies such as low-grade, high-grade and brainstem gliomas, medulloblastoma, ependymoma, etc., aged between 3 months and 20 years (median age, 8.5 years). The training utilized manually generated brain masks, including the sellar/suprasellar region, from 153 patients and employed 5-fold cross-validation to split the data into inner training-validation sets. The models were then tested on a withheld set of 183 subjects. Additionally, we trained a single-parametric model on individual images, resulting in 612 training and 732 testing cases. Model performance was evaluated using the Dice similarity metric for segmenting both the entire brain and slices specifically containing the sella turcica.

RESULTS
The multi-parametric and single-parametric models achieved mean±sd Dice scores of 0.981±0.008 (median=0.983) and 0.979±0.009 (median=0.981), respectively. For the sellar/suprasellar slices, the scores were 0.983±0.009 (median=0.986) and 0.981±0.012 (median=0.984), respectively. These results indicate a high precision in segmenting not only the entire brain volume, but also the sellar/suprasellar region.

CONCLUSION
Our proposed deep learning-based skull-stripping approach, leveraging both multi-parametric and single-parametric MRI inputs, demonstrates excellent accuracy. These models, made publicly available, have potential for improving auto-processing pipelines in pediatric brain tumors.
IMG-12. CBTN BRAIN TUMOR SEGMENTATION INITIATIVE: UPDATES ON MODEL RELEASE, HGG SEGMENTATION, AND SURVIVAL ANALYSIS
Authors: Kazerooni Anahita Fathi 
Published: June 2024

Abstract

BACKGROUND
Assessment of treatment responses in pediatric brain tumors requires accurate tumor segmentation, particularly important for nonresectable tumors like diffuse midline glioma (DMG), including diffuse intrinsic pontine glioma (DIPGs). Evaluating tumor progression in these tumors relies on monitoring tumor size changes in longitudinal MRI exams which is challenged by their infiltrative growth patterns. Our team developed a comprehensive multi-institutional, multi-histology autosegmentation deep learning (DL) model leveraging data from the Children’s Brain Tumor Network (CBTN), involving continuous enhancements and enriched by an expanding dataset including post-surgical/post-treatment studies.

METHODS
Recently, we trained and validated this DL model (publicly available) on a multi-institutional dataset of 644 PBTs, including 273 pediatric-type diffuse high-grade gliomas (HGGs, including DMG/DIPGs), using pre-operative and post-operative/post-treatment multiparametric MRI scans. We tested the model in 23 longitudinal MRI exams of 8 DMGs from the PNOC003 and PNOC007 clinical trials and applied Cox regression for predicting overall survival (OS) using tumor volume, computed with our autosegmentation model, as a time-varying variable in 18 patients.

RESULTS
For HGGs in treatment-naïve sessions, our model demonstrated strong performance with median Dice scores of 0.89 for whole tumor and tumor core (encompassing all regions, except for edema), and 0.73 for enhancing tumor segmentation. In longitudinal MRI exams of DMGs from the aforementioned clinical trials, the model achieved median Dice scores of 0.79 for whole tumor, and 0.78 for enhancing tumor and tumor core segmentation. Cox regression showed the significance of longitudinal volumetric measurements (p=0.08) for predicting OS in DMGs.

CONCLUSIONS
Our approach provides reliable tumor segmentation in HGGs across diverse datasets, crucial for treatment response assessment. Looking ahead, we plan to validate our model on a larger set of longitudinal exams and use volumetric measurements to predict tumor progression. This ongoing effort aims to improve model performance, extend its generalizability, and mitigate potential model decay.
IMG-14. DIAGNOSTIC UTILITY OF THE T2-FLAIR MISMATCH SIGN FOR DNETS AND ITS ASSOCIATION WITH FGFR1 ALTERATIONS
Authors: Wheeler Charles A , Liang Jia , Li Yimei , Chiang Jason , Bag Asim K 
Published: June 2024

Abstract

BACKGROUND
The T2-FLAIR mismatch sign, hyperintense T2-weighted signals with corresponding hypointensity on FLAIR sequence, has emerged as a potential diagnostic tool for identifying specific brain tumor subtypes. We tested the diagnostic utility of the sign in dysembryoplastic neuroepithelial tumors (DNETs) with and without FGFR1 alterations.

METHODS
We evaluated the T2-FLAIR mismatch sign in a cohort of 43 patients with definitive or probable DNET. Low-grade glioma patients with definite non-DNET diagnoses were chosen as control (n=43). Receiver operative curve analysis was performed, and the corresponding odds ratios were calculated.

RESULTS
Of the 43 DNET cases, 35 were definite, and 8 were probable DNETs. FGFR1 alterations were confirmed in 9 definitive DNETs. The area under the curve (AUC) for diagnosing DNET with the T2-FLAIR mismatch sign was 0.884 (DeLong CI: 0.2, 0.95) (Accuracy: 0.88, 95% CI: 0.80, 0.94; Sensitivity: 0.84; Specificity: 0.95); for definite DNET it was 0.891 (DeLong CI: 0.82, 0.96) (Accuracy: 0.90, 95% CI: 0.81, 0.95; Sensitivity: 0.87; Specificity: 0.94); for probable DNET it was 0.852 (DeLong CI: 0.69, 1;) (Accuracy 0.92, 95% CI: 0.81, 0.98; Sensitivity: 0.84; Specificity: 0.75), and for DNET with FGFR1 alterations was 0.977 (DeLong CI: 0.95, 1) (Accuracy 0.96, 95% CI: 0.87, 1; Sensitivity: 1; Specificity: 0.81). The odds of having DNET (definite +probable) if the T2-FLAIR mismatch sign was positive was 81.96 (CI: 16.25, 852.48, P&lt;0.001), and DNET with FGFR1 is 315.4 (CI: 312.3, 318.5, P&lt;0.001).

CONCLUSION
The T2-FLAIR mismatch sign is a reliable radiological biomarker for DNET and strongly predicts FGFR1 alterations in these tumors.
IMG-34. THE UTILITY OF [18F]FET PET IMAGING IN PAEDIATRIC CNS TUMOURS: A SINGLE INSTITUTION EXPERIENCE IN SYDNEY, AUSTRALIA
Authors: Govender Dinisha , Chung David 
Published: June 2024

Abstract

BACKGROUND
MRI remains the standard imaging modality in paediatric CNS tumours. Whilst Positron emission tomography (PET) with O-(2-[18F]fluoroethyl)-L-tyrosine ([18F-FET) is a well-established tool in adult central nervous system (CNS) tumours, established data on its diagnostic utility and impact on clinical management in children and adolescents remain limited.

METHODS
Single centre retrospective review of database of all paediatric and adolescent patients (age 0-19years) who underwent 18F]FET-PET imaging with cross referencing of patient electronic medical records. Conducted at the Children’s Hospital at Westmead in Sydney, Australia.

RESULTS
116 patients underwent 18F-FET PET imaging between October 2012 and January 2024. 28 patients had additional comparative fluorodeoxyglucose (FDG) CNS dedicated PET imaging. Primary indications for FET PET imaging included differentiating between a neoplastic versus non-neoplastic process (23.3%), identifying active residual tumour (14.7%), characterising tumour grade in patients including in those with the known tumour predisposition syndromes (6%), distinguishing between progression pseudo-progression (2.5%) and providing additional localisation or biopsy guidance (8.6%). Malignant transformation, gliomatosis and extent of disease was also noted as indications. 15 patients were able to avoid a biopsy or invasive surgery. Nearly 60% of FET PET imaging was considered clinically useful with the management of 52/112 (46%) patients being supported or changed. Management changes included change second look surgery, change of radiation field, change of treatment and access to a clinical trial. Only 3 scans were reported to have technical challenges in resolution or poor spatial correlation with MRI. Further analysis is currently being conducted, reviewing standard uptake values (SUV’s) of histologically proven low/high grade tumours and T2 enhancing/non-enhancing lesions.

CONCLUSIONS
This small study demonstrated the clinical benefit and utility of 18F-FET PET imaging in paediatric CNS tumours. Our experience has demonstrated significant surgical and diagnostic advantage in tumour localisation as well as avoiding surgery where able.
IMMU-06. CAR DESIGN SHAPES THE BRAIN TUMOR IMMUNE MICROENVIRONMENT: IMPACT ON ACTIVITY AND PERSISTENCE OF ANTI-GLIOMA ADOPTIVE T CELL IMMUNOTHERAPY
Authors: Haydar Dalia , Ibañez-Vega Jorge , Crawford Jeremy , Yi Zhongzhen , DeRenzo Chris , Gottschalk Stephen , Roussel Martine , Thomas Paul , Krenciute Giedre 
Published: June 2024

Abstract

BACKGROUND
B7-H3 CAR T-cells effectively eradicate brain tumors in xenograft models and have proven safe in clinical trials, yet no objective responses were observed in patients. The limited efficacy is partly attributed to the immunosuppressive tumor microenvironment (TME), a characteristic not fully recapitulated in investigations using xenograft models. In this study, we set to optimize B7-H3 CAR structure and examine the effects of CAR design on the brain TME using an immunocompetent glioma model.

METHODS
We generated a panel of fully murine B7-H3 CARs with variations in transmembrane, co-stimulatory, and activation domains. This enabled us to comprehensively investigate their effector functions within the intact immune system. High-dimensional flow cytometry and single-cell RNA sequencing were then used to investigate changes in the brain TME following CAR T-cell therapy.

RESULTS
While five out of six B7-H3 CARs with single co-stimulatory domains exhibited potent functionality in vitro, optimizing co-stimulation and signaling failed to enhance their anti-glioma efficacy in vivo. To further enhance therapeutic effectiveness and persistence, we incorporated 4-1BB and CD28 co-stimulation through transgenic expression of 4-1BBL on CD28-based CAR T-cells. Although this design significantly improved anti-glioma efficacy in vitro, it conferred no additional benefits in vivo. Analysis of the TME revealed that CAR T-cell therapy influenced the immune composition of the brain TME. Successful anti-tumor responses were dictated by the recruitment, activation, and spatial localization of unique inflammatory ‘immune hubs’ containing specific subsets of macrophages and endogenous T-cells. Interestingly, depletion of brain macrophages using CSF1R inhibitor markedly inhibited CAR T-cell anti-tumor activity.

CONCLUSION
Our study highlights the critical role of CAR structural design and its modulation of the TME in mediating the efficacy of CAR T-cell therapy in brain tumors. Yet, more specific targeting and/or reprogramming of the TME is warranted for the successful design of effective adoptive immunotherapies for high-grade glioma.
IMMU-11. CLINICAL EXPERIENCE WITH THE CONCEPT OF MULTIPHASE COMBINED TREATMENT FOR ADULTS WITH GBM
Authors: Van Gool Stefaan W , Kosmal Jennifer , Van de Vliet Peter , Kampers Linde F C , Stuecker Wilfried 
Published: June 2024

Abstract

BACKGROUND
We published survival data of 50 adults with GBM treated with individualized multimodal immunotherapy (IMI) as part of a multiphase combined treatment (Van Gool et al, https://doi:org/10.1016/bs.mcb.2023.06.001). Analysis was repeated with new observation data.

METHODS
Patient definition for retrospective analysis: start IMI between 27/05/2015 and 31/10/2023, &gt;18y, newly diagnosed, IDH1 wild-type, MGMT promoter-methylation status documented, survival known, no second malignancy.

RESULTS
Forty (14 female, 26 male) GBM patients were MGMT-promoter-unmethylated (unmeth), while 31 (15 female, 16 male) were MGMT-promoter-methylated (meth). Comparing the molecular subgroups, both respective median age; 48 years (range 18-65) and 54 years (range 26-72), and respective Karnofsky performance index; 70 (range 50-100) and 80 (range 60-100), were equal. Patient distribution (&lt; complete resection, complete resection, not documented) was equal: 17/15/8 (unmeth) versus 17/11/3 (meth). Both groups were treated similarly: in median 37 (range 0-147) sessions of modulated electrohyperthermia, in median 37 (range 5-147) bolus injections of Newcastle Disease Virus, in median 2 (range 0-6) IO-Vac® vaccines with a median total number of 30.7x10e6 DCs (range 0-158x10e6). The median overall survival (mOS) for the total group was 27 months. The 2-year overall survival (OS) was 57.9 % and the 3-year OS 37.1 %. The mOS for unmeth patients was 22.1 month, with a 2-year OS of 42.7%. The mOS for meth patients was 37.7 months, with a 2-year OS of 75.5%. Sampling five control arms of contemporary RCTs as external control arm (Liau et al, JAMA Oncol 2023) showed for unmeth and meth patients a mOS of 14.6 resp. 21.3 months with a 2-year OS of 21% resp. 42%, which reflects the current prognosis for these patients.

CONCLUSIONS
Our analysis confirms that the integration of IMI during and after standard of care improves the OS. Similar multiphase combined treatment strategy, including IMI, should be considered to treat childhood GBM.
LGG-04. TRAMETINIB COMBINED WITH CHEMOTHERAPY FOR INFANT BRAF-FUSED CHIASMATIC GLIOMA
Authors: Toledano Helen , Michaeli Orli , Kaadan Walid , Caduri Shlomi , Friedland Rivka , Dotan Gad , Konen Osnat 
Published: June 2024

Abstract

BACKGROUND
Infant chiasmatic gliomas present a major therapeutic challenge due to large size, threat to vision and progressive nature. Infants &lt;1 year respond poorly to Vincristine/Carboplatinum chemotherapy (SIOP-LGG2). BRAF fusion is common, so MEK inhibitors have been used second line in older children, but time to response is slow (median ~20 months). Data on the use of these drugs in infants is limited as is data on the safety and efficacy of combining chemotherapy with MEK inhibitors.

METHODS
We report two infants treated successfully for BRAF fused chiasmatic glioma - both presented age 5 months with nystagmus and diencephalic syndrome. Both had significant tumor progression within 6 weeks of starting Vincristine/ Carboplatin which responded rapidly to the addition of Trametinib.

RESULTS
Case 1 required tumor debulking at week 6 of chemotherapy due to progressive hyponatremia, hydrocephalus and brainstem compression. Surgery caused diabetes insipidus, hemianopia and hemiparesis. Tumor growth continued despite reinstating chemotherapy. Addition of trametinib caused tumor shrinkage by 75% within 12 weeks and 90% by 1 year. Minirin doses were reduced by 80% on Trametinib. After 1.3 years, chemotherapy was stopped & Trametinib continued alone. Case 2 - tumor growth and severe hyponatremia by week 6 of chemotherapy prompted addition of Trametinib without surgery. There was tumor reduction of 50% over 6 months and improvement of severe failure to thrive. Hyponatremia, requiring sodium and urea supplementation, stabilized. Bilateral vision and age-appropriate development are maintained. Hypertension developed early, but resolved completely after stopping and then gradually reintroducing Trametinib. Recurrent diarrhea required dose reduction.

CONCLUSIONS
We show the safety and efficacy of combined treatment with chemotherapy and MEK inhibition in infants and postulate that both the combination and the young age at initiation of therapy may have contributed to the excellent outcome. We urge for clinical trials of combination therapy in this age group.