bluuebunny/crossref_metadata_2025_split huggingface dataset:
INNV-41. SUCCESSFUL TREATMENT OF EXTREMELY RARE CASE OF PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOPLASMACYTIC LYMPHOMA WITH BENDAMUSTINE-RITUXIMAB REGIMEN: A CASE REPORT
Authors: Sy Kaitlyn , Mogla Sunveer , Singh Jaswinder , Shhwartz Eston , Dixit Sakshi , Kalra Amandeep 
Published: November 2023

Abstract
Lymphoplasmacytic lymphoma (LPL) is an uncommon low-grade non-Hodgkin B-cell lymphoma, with an annual incidence rate of 3 to 4 cases per million people. Primary central nervous system lymphoplasmacytic lymphoma (PCNSLPL) is even rarer, with fewer than 30 cases documented in the literature so far, to our knowledge. We report a case of a 40-year-old Caucasian male who presented with headaches and mild short-term memory problems. CT was suggestive of a right parietal mass. MRI revealed a right parietal homogeneously contrast-enhancing mass with T2 and flair changes extending to the right lateral ventricle, suggestive of vasogenic edema. An open biopsy was performed, and immunohistochemical analysis of the sample indicated lymphoplasmacytic lymphoma. PET scan was negative for hypermetabolic lesions. Bone marrow biopsy, serum protein electrophoresis, urine protein electrophoresis, and CSF analysis were unremarkable. A diagnosis of primary central nervous system lymphoplasmacytic lymphoma (PCNSLPL) was given after comprehensive workup. Treatment with 10 cycles of bendamustine and rituximab (BR regimen) resulted in a good response. The patient has been clinically stable and improving for over a year. Radiologically, there has been interval shrinkage of the right parietal mass and follow-up scans continue to be stable. The plan is to continue maintenance rituximab for 2 years. Although extremely rare, PCNSLPL should be considered in differential diagnoses of hematolymphoid tumors of the CNS and bendamustine with rituximab as induction treatment followed by maintenance rituximab might be considered.
PATH-02. NOVEL SOX10 INDEL MUTATIONS DRIVE SCHWANNOMAS THROUGH IMPAIRED TRANSACTIVATION OF MYELINATION GENE PROGRAMS
Authors: Williams Erik , Ravindranathan Ajay , Gupta Rohit , Stevers Nicholas , Suwala Abigail , Hong Chibo , Kim Somang , Yuan Jimmy , Wu Jasper , Barreto Jairo , Lucas Calixto-Hope , Chan Emily , Pekmezci Melike , Leboit Philip , Mully Thaddeus , Perry Arie , Bollen Andrew , Van Ziffle Jessica , Devine W Patrick , Reddy Alyssa T , Gupta Nalin , Basnet Kristen , Macaulay Robert , Malafronte Patrick , Lee Han , Yong William , Williams Kevin Jon , Juratli Tareq , Mata Douglas , Huang Richard , Hiemenz Matthew , Pavlick Dean , Frampton Garrett , Janovitz Tyler , Ross Jeffrey , Chang Susan , Berger Mitchel , Jacques Line , Song Jun , Costello Joseph , Solomon David 
Published: November 2023

Abstract
Schwannomas are common peripheral nerve sheath tumors that can cause severe morbidity due to their stereotypic intracranial and paraspinal locations. However, the molecular drivers responsible for a substantial subset remain unknown. Through genomic profiling of 96 schwannomas, we identified novel in-frame insertion/deletion mutations in the SOX10 gene in 29% of sporadic tumors which uniformly lacked alterations in known nerve sheath tumor genes including NF1, NF2, LZTR1, and SMARCB1. These indel mutations clustered at the C-terminal end of the HMG-box domain, the DNA binding motif of the SOX10 transcription factor critical for Schwann cell differentiation. Schwannomas arising from non-vestibular cranial nerves (e.g. facial, trigeminal, vagus) were highly enriched for SOX10 mutations, whereas the vast majority of vestibular schwannomas had NF2 inactivation. DNA methylation profiling revealed that SOX10 mutant schwannomas clustered together with schwannomas harboring NF2 mutation or SH3PXD2A-HTRA1 fusion, indicating that SOX10 indel mutations define a novel schwannoma molecular subtype and not a distinct nerve sheath tumor entity. To investigate the mechanism by which SOX10 indel mutations promote schwannoma development, we stably transduced human fetal glial cells with either wildtype SOX10 or two different tumor-derived insertion mutants. RNA sequencing revealed that both SOX10 indel mutants, in contrast to wildtype SOX10, failed to activate expression of myelination and glial differentiation gene programs, including PMP2 which encodes the major peripheral myelin protein in mature Schwann cells. Electrophoretic mobility shift assay revealed that the indel mutants retained their DNA binding capacity to the SOX10 binding site in the PMP2 promoter, while a luciferase reporter assay revealed transactivation at the PMP2 promoter by wildtype SOX10 but not by the mutants. In summary, we identified a new recurrent genetic event in 29% of sporadic schwannomas that we speculate drives schwannoma development through impaired transactivation of myelination gene expression programs causing blocked differentiation of immature Schwann cells.
PATH-09. THE IMPACT OF MISMATCH REPAIR DEFICIENCY ON GLIOMAS IN CHILDREN, ADOLESCENTS, AND YOUNG ADULTS; A MULTI-CENTRIC, COLLABORATIVE STUDY LED BY THE IRRDC AND GLIOMA TASKFORCE
Authors: Negm Logine , Chung Jiil , Nobre Liana Figueiredo , Bennett Julie , Fernandez Nicholas , Komosa Martin , Zhang Cindy , Ku Michelle , Johnson Monique , Bianchi Vanessa , Aronson Melyssa , Stengs Lucie , Lim-Fat Mary-Jane , Keith Julia , Tsang Derek , Gao Andrew , Munoz David , Nguyen Lananh , Das Sunit , Levine Adrian , Das Anirban , Resnick Adam , Robinson Giles W , Nichols Kim , Wheeler David A , Hawkins Cynthia , Tabori Uri 
Published: November 2023

Abstract

BACKGROUND
Primary mismatch repair deficiency (pMMRD) is a pan-cancer mechanism caused by somatic mutations or inherited as part of Lynch Syndrome (LS) or constitutional mismatch repair deficiency (CMMRD). PMMRD results in universal hypermutation and microsatellite instability leading to chemoradiation resistance but sensitivity to immunotherapy. The prevalence of pMMRD in gliomas of children, adolescents, and young adults (CAYA), and the impact of germline inheritance is unknown.

METHODS
We harnessed functional genomic tools across population-based (Toronto), and multiple institutional (SJ, CBTN) cancer databases (n=1276) to determine the prevalence, subgroups, and impact of germline mutations in pMMRD gliomas.

RESULTS
Data from Toronto, SJ and CBTN reveals prevalence of 10%, 5% and 5% in pediatric high-grade gliomas (HGG). Molecularly, pMMRD is absent in gliomas harboring pediatric-type fusions, BRAF-V600E, and histone mutations, but enriched in HGG harboring IDH and TP53 mutations. In AYA (n=660), pMMRD was detected in 5% of IDH-WT and IDH-mutant high-grade astrocytomas, but notably absent in all oligodendrogliomas and other low-grade gliomas. Across this entire CAYA dataset, all except one pMMRD gliomas harbored germline MMR mutations (97%). Strikingly, LS predominated over CMMRD with significant difference in age of glioma onset (LS: 29-years versus CMMRD: 11-years; p&lt; 0.001, IRRDC dataset). Survival analysis (Kaplan-Meier) confirmed the poor overall survival (OS) for pMMRD and other pediatric subtypes of HGG (3-year OS &lt; 25%). In contrast, IDH-mutant pMMRD HGG have significantly worse OS than IDH-mutant non-pMMRD counterparts. Remarkably, immunotherapy significantly improves survival not only in children, but also for AYA patients with refractory pMMRD HGG (3-year OS &gt; 50%).

CONCLUSION
This large study demonstrates that pMMRD drives a significant proportion of HGG across CAYA with an alarming impact of germline predisposition. Specifically, unrecognized AYA LS patients with MMRD HGG. These findings support universal screening for MMRD in HGG to identify patients for surveillance and immunotherapy.
PATH-32. COMPARISON OF CLINICAL, RADIOGRAPHIC AND TUMOR GENOMIC ALTERATIONS BETWEEN CLASSIC AND MOLECULAR GLIOBLASTOMA (IDH WILDTYPE)
Authors: Lachance Daniel , Decker Paul , Kollmeyer Thomas , Praska Corinne , Kosel Matthew , Odukoya Lateef , Ida Cristine , Drucker Kristen , Panda Arijit , Conte Gian Marco , Moassefi Mana , Tobin W Oliver , Erickson Bradley , Galanis Evanthia , Eckel-Passow Jeanette , Jenkins Robert 
Published: November 2023

Abstract

INTRODUCTION
Prior to 2021, (classic) glioblastoma (GBM) required absence of IDH mutation and histologic features of hypercellularity, nuclear atypia, microvascular proliferation (MVP), and necrosis. Absence of histologic features can be a result of undersampling, with molecular, radiologic, and clinical features consistent with GBM. Accordingly, WHO 2021 defined molecular glioblastoma as diffuse glioma IDHwt, H3wt, with one or more of MVP, necrosis, TERT promoter mutation, EGFR gene amplification (EGFRamp), and/or +7/−10. We hypothesized that there is clinical and radiographic heterogeneity among tumors defined as molecular GBM.

METHODS
We identified 121 patients that met the criteria for molecular GBM. 121 Classic GBM, defined as IDHwt with hypercellularity, MVP or necrosis, were matched to molecular GBM cases by age, sex, and diagnosis date. All cases were reviewed for relevant histologic features and molecular features. Examined clinical variables included: first symptom, time from first symptom to date of pathological diagnosis, date of first MRI, tumor location, radiographic features of enhancement, diffusion or mass effect, time to progression and overall survival. Clinical, radiologic, and molecular features were compared between classic and molecular GBM.

RESULTS
Currently,160 cases have been reviewed. Preliminary results show that there is a longer time from first symptom to diagnosis for molecular GBM. Molecular GBM is significantly more likely to be discovered coincidentally, and without enhancement or radiographic necrosis. Classic GBM is significantly more likely to present with focal neurologic deficit as first symptom, regional mass effect on index imaging and have monosomy 10.

CONCLUSION
While pathologic undersampling accounts for a significant proportion of molecular GBM, our data suggest that there are significant clinical, radiographic, and outcome differences between classical and molecular GBM. Final analysis of the entire cohort will help better define this clinical spectrum.
PATH-44. INTRA-OPERATIVE ANALYSES OF GENETIC ALTERATIONS IN THE CENTRAL NERVOUS SYSTEM TUMORS USING RAPID QUANTITATIVE PCR DEVICE
Authors: Ohka Fumiharu , Maeda Sachi , Yamaguchi Junya , Shimizu Hiroki , Motomura Kazuya , Takeuchi Kazuhito , Nagata Yuichi , Nishikawa Tomohide , Kibe Yuji , Takido Yuhei , Saito Ryuta 
Published: November 2023

Abstract
Recent comprehensive molecular analyses of central nervous system (CNS) tumors identified several diagnostic or prognostic markers. IDH mutation, TERT promoter (pTERT) mutation and CDKN2A homozygous deletion in adult-type diffuse gliomas, BRAF p.V600E mutation in circumscribed astrocytic gliomas, histone H3 gene mutations in pediatric-type diffuse high-grade gliomas or MYD88 mutation in primary CNS lymphoma (PCNSL) are quite important markers. Intra-operative identification of these molecular alterations could be effective for decision-making of tumor removal strategy. In this study, we established intra-operative gene analysis method using rapid quantitative PCR device, GeneSoC, by which we could obtain genotype of these important genes for 20 minutes in an operating room. Using GeneSoC, we could perform 50 cycles of quantitative PCR for approximately 12 minutes. We established rapid gene analyses of mutations of IDH1 p.R132H, pTERT C225T, pTERT C250T, H3 K27M, BRAF p.V600E and homozygous deletion of CDKN2A for diffuse gliomas and circumscribed astrocytic tumors and MYD88 p.L265P for PCNSL. For elution of DNA, we incubated at least 1mg of tumor tissue at 95°for 5 minutes. Combined this boiling method with GeneSoC, we could obtain those genetic alterations for 20 minutes after collection of tumor tissue. For 42 glioma cases and 10 PCNSL cases, we could obtain accurate genetic alterations (IDH1 p.R132H, pTERT C225T, pTERT C250T for gliomas or MYD88 p.L265P for PCNSL) in all cases, using this method. For detection of CDKN2A homozygous deletion, sensitivity and specificity are 100% and 83% in analyzed 50 cases, respectively. In this study, we could obtain the genotype of important molecular markers intra-operatively, not only during tumor removal but even during tumor biopsy, using GeneSoC device. This rapid genetic analysis might be effective not only for intra-operative diagnosis but also for detection of boundary between tumor and normal tissue.
NIMG-12. SPATIALLY MAPPING TREATMENT-EFFECT IN RECURRENT GLIOMA WITH PHYSIOLOGIC MRI AND DEEP LEARNING
Authors: Ellison Jacob , Tran Nate , Hanna Rany , Phillips Joanna , Shai Anny , Nair Devika , Molinaro Annette , Pedoia Valentina , Li Yan , Luks Tracy , Villanueva-Meyer Javier , Berger Mitchel , Hervey-Jumper Shawn , Aghi Manish , Chang Susan , Lupo Janine 
Published: November 2023

Abstract

INTRODUCTION
Discriminating between glioma tumor recurrence (rTumor) and treatment-induced effects (TxE) from chemo and radiotherapy using MRI is an ongoing challenge in neuro-oncology. Although deep learning has been used to characterize suspicious lesions, these models typically do not account for lesions that are comprised of mixtures of rTumor and TxE components. We hypothesize that using an approach that includes using tissue samples with known location on MRI, incorporating perfusion-weighted and diffusion-weighted with anatomical MRI, self-supervised pretraining, and a novel ensembling strategy will improve performance and attain spatial maps of rTumor and TxE for determining subsequent treatment.

METHODS
Using 218 pathology confirmed tissue samples with coordinates on multi-parametric MRI from 111 patients with suspected tumor progression, we developed a deep learning approach to spatially map intratumoral regions of rTumor and TxE. Our model leveraged 10mm isotropic cubes centered on the tissue sample coordinates that includes Diffusion-Weighted and Dynamic Susceptibility-Contrast Perfusion MRI and large amounts of pathologically unlabeled data from the lesion in self-supervised pretraining. The multiple networks of the ensemble were generated through repeated constrained cross validation (RC-CV) which enabled unique repetitions of training with more variation in class proportions.

RESULTS
Our best classifier performed with a RC-CV/test AUROC of 0.85/0.81 and was able to generate maps of tumor probability as well as ensenble variance and uncertainty. Ensemble variance was significantly higher in prediction failures of rTumor (P=.006) and epistemic uncertainty was significantly higher (P=.002) in prediction failures of TxE in training. Test Performance dropped by 14.7% when removing self-supervised pretraining, 21.3% when removing physiological imaging contrasts, and 1.2% when ensembling across standard 4-fold CV.

CONCLUSION
The AUROC of .81 supports the feasibility of discriminating between TxE and rTumor within a single lesion non-invasively. Physiologic MRI, pretraining on pathology unlabeled data, and ensembling networks, improve performance and help characterize model failures.
NIMG-14. AUTOPSY-BASED RADIO-PATHOMIC MAPS REVEAL DEMOGRAPHIC FACTORS IMPACT THE PRESENCE OF TUMOR OUTSIDE CONTRAST ENHANCEMENT IN GLIOMA PATIENTS
Authors: Bobholz Samuel , Lowman Allison , Duenweg Savannah , Winiarz Aleksandra , Stebbins Margaret , Flatley Michael , Nath Biprojit , Kyereme Fitzgerald , Connelly Jennifer , Coss Dylan , Krucoff Max , Banerjee Anjishnu , LaViolette Peter 
Published: November 2023

Abstract
Gliomas are vastly heterogenous tumors with wide-ranging pathological signatures; as such, there is a pressing need to understand how individualized factors affect tumor growth patterns to develop treatment plans and improve prognosis. This study used autopsy-based radio-pathomic maps of tumor probability to test the hypothesis that differences in patient sex and age impact the presence of non-enhancing tumor. This study used pre-surgical imaging and demographic data from 501 glioma patients from the UCSF-PDGM dataset. Specifically, T1, T1C, FLAIR, and ADC images were used to generate maps of tumor probability using a previously validated model. This model used autopsy tissue aligned to imaging as ground truth to develop models sensitive to identifying areas of non-enhancing tumor outside of traditional imaging signatures. Thresholded maps were used to delineate areas of tumor presence, and the non-enhancing tumor volume was computed for each patient as the predicted high probability tumor volume within non-enhancing FLAIR hyperintensity. A general linear model was then fit to assess the effect of age and sex on non-enhancing tumor volumes, using tumor grade and contrast-enhancing volume as covariates. Male patients trended towards greater non-enhancing tumor volumes compared to female patients (B=5676.6, p=0.053). Older patients were also found to have less non-enhancing tumor volume compared to younger patients (B=-261.1, p=0.015). These results suggest that demographic characteristics influence the progression of tumor beyond the angiogenic tumor mass and may therefore influence treatment efficacy and prognoses. Future research is essential to understanding how these sex differences relate to the worse prognosis historically observed for males with glioma, and how the reduced presence of non-enhancing tumor in older patients relates to a reduction in plasticity-induced tumor propagation.
NIMG-17. TOWARDS IMAGE - BASED PERSONALIZATION OF GLIOBLASTOMA THERAPY: A COMPREHENSIVE VALIDATION STUDY OF A NOVEL, DEEP LEARNING - DRIVEN TUMOR GROWTH MODEL FOR INDIVIDUALIZED RADIOTHERAPY
Authors: Metz Marie-Christin , Griessmair Michael , Ezhov Ivan , Peeken Jan , Weidner Jonas , Menze Bjoern , Wiestler Benedikt 
Published: November 2023

Abstract

BACKGROUND
The diffuse growth pattern of glioblastoma is one of the main challenges for accurate treatment planning. Computational tumor growth modeling has emerged as a promising tool to guide personalized therapy by estimating the tumor cell density in the whole brain. Here, we performed biological and clinical validation of a novel growth model, aiming to close the gap between the experimental state and clinical implementation.

METHODS
124 patients from The Cancer Genome Archive (TCGA) and 397 patients from the UCSF Glioma Dataset were assessed for significant correlations between clinical data, genetic pathway activation maps (generated with PARADIGM; TCGA only), and infiltration (Dw) as well as proliferation (ρ) parameters stemming from a Fisher-Kolmogorov growth model that is adjusted to the patient’s anatomy using deep learning. To further evaluate clinical potential, we performed the same growth modeling on preoperative MRI data from 30 patients of our institution and compared model-derived tumor volumes (that serve as alternative clinical target volumes) and recurrence coverage with standard radiotherapy plans.

RESULTS
The parameter ratio Dw/ρ (p &lt; 0.05 in TCGA) as well as the simulated tumor volume (0.5 tumor cell density) (p &lt; 0.05 in TCGA/UCSF) were significantly inversely correlated with overall survival. We found a significant correlation between 11 proliferation pathways and the estimated proliferation parameter ρ, proving its biological plausibility. Depending on the cutoff value for tumor cell density, we observed significant improvement of recurrence coverage without significantly increased total radiation volume utilizing model-derived target volumes instead of standard radiation plans. More precisely, a cutoff value of 0.5 tumor cell density yielded the best results here.

CONCLUSIONS
Identifying a significant correlation between computed growth parameters, and clinical and biological data, we highlight the potential of tumor growth modeling for individualized therapy of glioblastoma. This might improve accuracy of radiation planning in the near future.
NIMG-39. BENCHMARKING MULTI-LABEL PREDICTION OF TARGET GENE MUTATION STATUS IN GLIOBLASTOMA USING RADIOGENOMICS AND MULTI-PARAMETRIC MR IMAGING
Authors: Yu Fanyang , Guo Jun , Kazerooni Anahita Fathi , Akbari Hamed , Toorens Erik , Sako Chiharu , Mamourian Elizabeth , Bagley Stephen J , Binder Zev , Lustig Robert A , Brem Steven , O'Rourke Donald M , Ganguly Tapan , Bakas Spyridon , Nasrallah MacLean P , Chaudhari Pratik , Davatzikos Christos 
Published: November 2023

Abstract

PURPOSE
In vivo imaging-based prediction of gene mutation status can significantly contribute to the diagnosis and treatment of glioblastoma. Radiogenomics provides a non-invasive approach to capturing the phenotypical characteristics of molecular alterations. We build machine learning (ML) models on radiogenomics and MR imaging data for benchmarking the prediction of multiple target genes in a single institution dataset.

METHODS
Multi-parametric MRI scans (T1, T1-Gd, T2, T2-FLAIR, DSC, DTI) of 286 individuals with glioblastoma were used to extract radiomics features, including histograms, morphological and textural descriptors. Genetic markers were obtained through a targeted next generation sequencing panel. We built ML models to predict mutation status in two settings: (a) 4 frequently-occurring mutations (EGFR, PTEN, NF1, TP53), (b) 13 mutations including some that are infrequent (PDGFRA, MET, FGFR2, PIK3CA, PIK3R1, BRAF, MDM4, CDKN2A). The models include k-nearest neighbor classifiers, fully-connected neural networks w/o dimensionality reduction trained on radiomics data, convolutional (CNN) and self-attention-based (ViT) networks trained on MR imaging data. All models were trained with 5-fold cross validation on discovery cohort (n=257) and tested on replication cohort (n=29).

RESULTS
Multi-label prediction models achieved highest accuracies of 75.9% / 88.6% for 4 / 13 mutations using radiomics features. Models trained with radiomics features have a marginally better average F1 score than CNNs and ViTs trained on MR imaging data (by 4.1% / 4.6% for 4 / 13 mutations).

CONCLUSION
We had modest success in finding highly distinct MR signatures of multiple target mutations in glioblastoma using multi-label ML methods, presumably because of the small sample size and the molecular, the spatial heterogeneity of the tumor, and the difficulty of simultaneously learning multiple imaging signatures. This highlights the importance of pooling and harmonizing radiogenomics data from multiple institutions to increase the sample size, and of incorporating biological domain knowledge into ML models.