bluuebunny/crossref_metadata_2025 huggingface dataset:
INNV-10. OVERCOMING CHALLENGES AND OPTIMIZING PATIENT OUTCOMES WITH TUMOR TREATING FIELDS (TTFIELDS) THERAPY
Authors: Gage Gwyn P , Frongillo Peggy , Thomas Anne , Adams Nichelle Renae
Published: November 2022

BACKGROUND

Tumor Treating Fields therapy (TTFields; 200 kHz) is a noninvasive, locoregional, antimitotic treatment approved for newly diagnosed glioblastoma (ndGBM), recurrent GBM, and malignant pleural mesothelioma, and is being investigated in a range of solid tumors.

METHODS

Healthcare professionals (HCPs) specializing in radiation oncology, neuro-oncology, and medical oncology as well as patients with GBM and caregivers were asked to complete online surveys between October–December 2021 (HCPs) and July–September 2021 (patients/caregivers).

RESULTS

Overall, n=130 HCPs and n=50 patients/caregivers responded to the surveys. The majority (68%) of HCPs considered TTFields therapy + temozolomide a standard of care for ndGBM, yet only 35% of HCPs report strongly recommending it as a treatment option. Barriers to recommendation of TTFields therapy included the need for additional patient services and practice support with training, prescription process etc., and guidance on insurance coverage (31% for each), the need to increase patient awareness (20%), and lack of patient testimonials (20%). HCP perceptions of patient barriers were often higher than reported by patients themselves; time wearing device (46% vs 35%); treatment visibility (37% vs 24%), concerns with shaving head (37% vs 18%), and concerns with carrying device (34% vs 18%). Awareness of resources provided by the device manufacturer was relatively low (HCP: peer-peer knowledge exchange program [25%], TTFields therapy product brochures [16%–30%]; patient: webcasts [15%], patient website [23%], educational videos [25%–26%], educational brochures [26%–27%], buddy program [29%], and dedicated support service information [32%]), despite the desire for support.

CONCLUSION

Increasing awareness of existing HCP and patient support programs and resources continues to be critical in overcoming barriers to TTFields therapy initiation to optimize patient outcomes.
PATH-01. EVALUATION OF RAMAN SPECTROSCOPY AS A DIAGNOSTIC TOOL IN NEUROPATHOLOGY FOR TUMOR CLASSIFICATION
Authors: Georg Klamminger Gilbert , Mombaerts Laurent , Jelke Finn , Slimani Redouane , Mirizzi Giulia , Klein Karoline , Husch Andreas , Hertel Frank , Mittelbronn Michel , Borgmann Felix Kleine
Published: November 2022

BACKGROUND

Raman spectroscopy (RS) has shown its applicability in neurooncological diagnostics ranging from intraoperative tumor identification to peri- and postoperative tissue analyses. In the present study, we applied RS to track changes in the molecular vibrational status of a broad spectrum of formalin fixed paraffin-embedded (FFPE) intracranial neoplasms (primary brain tumors, meningiomas, brain metastases) and evaluated its potential as an additional method in the neuropathology toolbox, considering specific challenges when employing RS on FFPE tissue. Material and

METHODS

We examined 82 cases of intracranial neoplasms (679 individual measurements) by RS and applied a machine learning pipeline for recognition of spectral properties. The discrimination potential of the machine learning algorithms was evaluated using standard performance metrics such as AUROC and AUPR values, macro and weighted average of accuracy, precision, recall, and f1 scores. To address occurring misclassifications and further evaluate our models we searched for important Raman bands usable for tumor identification.

RESULTS

Using our trained machine learning model, we differentiated between different types of gliomas and determined the primary origin in case of a brain metastasis. We further spectroscopically diagnosed tumor types solely based on biopsy fragments of necrosis, something not possible by means of light microscopy. During the validation process we confirmed a high complexity within the spectroscopic data, possibly resulting not only from biological tissue which has undergone a rough chemical procedure but also from residual components of the fixation/paraffination process.

CONCLUSIONS

Our study demonstrates possibilities and limits of RS as a potential diagnostic tool in neuropathology, considering accompanying difficulties in the vibrational spectroscopic examination of FFPE tissue.
PATH-09. SUBTYPE-ASSOCIATED RNA SPLICING CONTROLLED BY GLIOMA DRIVER MUTATIONS REGULATES GLIOMAGENESIS
Authors: Song Xiao , Huang Tianzhi , Tiek Deanna , Miki Shunichiro , Goenka Anshika , Iglesia Rebeca , Furnari Frank , Hu Bo , Hu Bo
Published: November 2022

Dysregulated RNA alternative splicing (AS) is a hallmark of cancer and has been studied in cancers including glioma. However, whether the dysregulated AS and the regulating RNA-binding proteins (RBPs) drive gliomagenesis and the role of glioma driver mutations in shaping the RBP/AS landscape have not been studied. Here we describe two AS-defined glioma subtypes that correlate with IDH1 mutation, tumor grade, and patient prognosis. Oncogenic AS isoforms are exclusively associated with AS subtype 2, IDH1 wild-type gliomas. Modulation of representative AS isoforms or subtype-associated RBPs regulate glioma stem-like cell (GSC) tumorgenicity. With a human induced pluripotent stem cell-derived glioma model system, we demonstrate that specific glioma driver mutations are involved in shaping the subtype-associated AS. Lastly, we target a sub2-associated RBP with anti-sense oligonucleotides to inhibit GSC tumorigenicity. Our data establish relationships between glioma driver mutations and AS programs and identify dysregulated RBP/AS network as a therapeutic vulnerability.
PATH-11. THE LONGITUDINAL EVOLUTIONARY TRAJECTORY OF OLIGODENDROGLIOMA IS DRIVEN BY TREATMENT-ASSOCIATED GENETIC ALTERATIONS
Authors: Varn Frederick , Ye Gordon , Ghospurkar Padmaja , Wade Taylor , Khasraw Mustafa , Lipp Eric S , Hermes Beth , Elliott Carol , Costello Joseph F , Hong Chibo , French Pim , Vallentgoed Wies , Padovan Marta , Ng Ho-Keung , Li Kay , Gan Hui , Westcott Kerryn , Vaubel Rachael , Horbinski Craig , McCortney Kathleen , Paek Sun Ha , Moon Hyo-Eun , LaViolette Peter , Lowman Allison , Barnholtz-Sloan Jill , Wesseling Pieter , Verhaak Roel
Published: November 2022

Oligodendroglioma is a subtype of diffuse glioma defined by a mutation in the isocitrate dehydrogenase (IDH) genes and a co-deletion of chromosome arms 1p and 19q. These tumors primarily occur in adult patients in their third and fourth decade of life and are universally fatal due to an inevitable recurrence that follows a treatment regimen of surgical resection and an optional combination of alkylating chemotherapy and/or radiation therapy. While initially slow growing, recurrent tumors exhibit increasingly aggressive phenotypes that become progressively more difficult to treat with conventional therapy. Currently, the molecular mechanisms and cellular phenotypes that drive this recurrence remain unknown. To understand these factors, we assembled a cohort of matched initial and recurrent oligodendroglioma samples from over 100 patients and performed whole-genome sequencing and whole-exome sequencing on each of them. To link these molecular profiles to cell state changes, we additionally performed bulk and single-nucleus RNA-sequencing on a subset of these tumor pairs. In nearly 40% of alkylating chemotherapy-treated patients, recurrent tumors presented with hypermutation that corresponded with an increase in neoplastic cell proliferation. Additionally, while individual somatic alterations specific to recurrence were relatively rare, we observed a subset of tumors that acquired deletions in the cell cycle regulator CDKN2A following treatment with radiotherapy. Acquisition of either of these features associated with shorter patient survival and higher grade at recurrence, implicating cell cycle dysregulation as a mechanism of treatment resistance and increased tumor severity. Together, these results indicate that oligodendrogliomas evolve in a treatment-specific manner following chemo- and radiation therapy and highlight key pathways that can be targeted to delay the onset of recurrence.
PATH-12. DIFFUSE HEMISPHERIC GLIOMA, H3 G34-MUTANT: A COMPREHENSIVE SEQUENCING AND HIGH-RESOLUTION GENOME-WIDE COPY NUMBER ANALYSIS
Authors: Trejo-Lopez Jorge , Kollmeyer Thomas , Praska Corinne , Raghunathan Aditya , Giannini Caterina , Vaubel Rachael , Nguyen Aivi , Jentoft Mark , Donev Kliment , Zheng Gang , DiGuardo Margaret , Kipp Benjamin , Jenkins Robert , Ida Cristiane
Published: November 2022

Diffuse hemispheric glioma, H3 G34-mutant (DHG_H3G34) is a novel tumor type in the 2021 WHO CNS classification. We describe a comprehensive sequencing and high-resolution genome-wide copy number analysis of a series of cases clinically tested by a single laboratory (2018-2022). Cases included tumors from 47 unique patients (1 reportedly recurrent) that had an H3-3A G34 mutation detected using an 187-gene mutation and fusion targeted neuro-oncology NGS panel (n=47). A subset (n=18) of cases was also tested Oncoscan chromosomal microarray. Median age at testing was 20 years (range, 12-50). H3-3A G34 mutations included G34R (n=44; 94%), G34V (n=2) and a novel G34E variant considered to be likely clinically relevant (n=1). All DHG_H3G34 were hemispheric tumors, and one tumor was multifocal with midline involvement. Concurrent mutations recurrently involved ATRX (n=38), TERT promoter (n=3; mutually exclusive with ATRX mutations), TP53 (n=43), PDGFRA (n=26), PTEN (n=5), NF1 (n=4, all patients over 20 years), PIK3CA (n=4) and CDKN2A (n=3). The single recurrent tumor also had an MSH6 mutation, and in addition to 3 other cases, had increased number of sequence variants suggestive of a high tumor mutational burden. A single case also showed an FGFR3::FAM184B (exon 17::exon 2) fusion. All cases with available chromosomal microarray data had unbalanced genomes with multiple chromosomal gains/losses, and regions of copy-neutral loss of heterozygosity. The most frequent recurrent (at least 50% of cases) abnormalities were losses involving 3q, 4q, 10q, 13q and 18q, and 17p copy-neutral loss of heterozygosity encompassing TP53 (all but one case had concomitant TP53 mutation), a pattern reminiscent of IDH-mutant astrocytomas. Oncogene amplifications (PDGFRA and PIK3CA) were detected in 3 (of 18) cases. CDKN2A/B deletion was observed in 8 cases (2 homozygous). In conclusion, we describe additional sequence and copy-number abnormalities in DHG_H3G34, expanding the spectrum of genetic changes associated with this novel tumor type.
PATH-14. MOLECULAR ANALYSIS OF ASTROCYTOMAS WITH A DOMINANT PILOMYXOID MORPHOLOGY
Authors: Ritacco Samantha , Decker Brennan , Killian J Keith , Williams Erik , Lechpammer Mirna
Published: November 2022

Pilomyxoid astrocytomas (PMAs) are tumors of infancy occurring in the hypothalamic chiasmatic region, with somewhat worse prognosis than pilocytic astrocytomas (PAs). We report molecular findings in PAs with a dominant pilomyxoid histomorphology across the various age groups and CNS locations. Foundation Medicine comprehensive genomic profiling (CGP) (FoundationOne, FoundationOneCDx, FoundationOneHeme) were performed during routine clinical care. A retrospective cohort analysis was done to evaluate the genomic characteristics of PMAs. 57 PMAs were identified, of which 6 occurred in patients < 12 months old (10.5%); 14 in 1-3 year old (24.6%); 15 in 4-10 y.o. (26.3%); 12 in 11-20 y.o. (21.1%), and 10 occurred in young adults 21-39 y.o. (17.5%). 30/57 (52.6%) were female and 27/57 (47.4%) male. 55 PMAs occurred in the brain and 2 in the spinal cord. Tumors were categorized by notable rearrangements and point mutations. The common fusion, KIAA1549:BRAF, was detected in 23/57 samples (40.4%). BRAF fusions with other gene partners (BRAF, TAX1BP1) occurred in 2, and BRAF V600E mutations in 4 patients. An additional 4 patients had FGFR1 fusions (FGFR1, TACC1) and 14 had FGFR mutations. Other alterations included 3 NF1 mutations, 2 NTRK family receptor tyrosine kinase fusions, 2 RAF gene fusions, 2 other (ATRX, ALK) alterations, and 1 VUS. Tumor Mutational Burden was under 10 mut/Mb for all patients. In this cohort of 57 PMAs, MAPK pathway genetic alterations were dominant drivers, similar to other PAs. We also observed other frequent genetic alterations not involving BRAF, including FGFR and NTRK gene family members that have FDA approved targeted inhibitors. While these also likely cause MAPK pathway activation, these non-BRAF variants are less common in PAs. Our report contributes to growing evidence that PMAs harbor a distinct molecular alteration profile, which is becoming increasingly important in the context of the evolving targeted therapeutics landscape.
PATH-19. CLINICAL AND MOLECULAR FEATURES OF GLIOBLASTOMA, IDH-WILDTYPE ARISING IN THE SETTING OF LI FRAUMENI SYNDROME
Authors: Kibe Yuji , Ohka Fumiharu , Motomura Kazuya , Aoki Kosuke , Maeda Sachi , Yamaguchi Junya , Nishikawa Tomohide , Mizutani Kosuke , Shimizu Hiroki , Hiramatsu Taku , Suzuki Kazuaki , Saito Ryuta
Published: November 2022

INTRODUCTION

Li Fraumeni Syndrome (LFS) is a rare autosomal dominant tumor predisposition syndrome caused by germline alterations of the TP53. Brain tumors are included in LFS core cancers. Choroid plexus carcinomas and medulloblastomas tend to occur in pediatric LFS patients, while gliomas tend to occur in adult LFS patients. There are few previous reports about gliomas arising in the setting of LFS. Here, we report clinical and molecular features of three cases of glioblastoma, IDH-wildtype arising in the setting of LFS.

RESULTS

Mean age at diagnosis was 40 years (range: 32-45). Two patients were male. Two patients had medical history of multiple tumors and family history of brain tumors. Magnetic resonance imaging of each patients revealed a left cerebellar peduncle lesion with faint contrast-enhancement, a right parietal lobe lesion with nodular contrast-enhancement and two lesions in the pons (no enhancement) and right parietal lobe (ring enhancement), respectively. All patients underwent chemotherapy including temozolomide and/or bevacizumab combined with radiation therapy (60Gy) after surgery. Mean progression free survival was 10.4 months (range: 5.1-17.0). All patients were admitted to palliative care mean 17.3 months (range: 14.4-21.4) after diagnosis. All patients harbored heterozygous germline mutations of TP53. Whole exome sequencing and copy number analysis of tumor tissues revealed TP53 mutation and PDGFRA amplification in all patients. However, EGFR amplification, the combination of gain of chromosome 7 and loss of chromosome 10 (7+/10-) and mutations of IDH, H3F3A, BRAF and TERT promoter were not detected in all tissues.

CONCLUSION

We experienced three cases of glioblastoma, IDH-wildtype arising in the setting of LFS. Although tumor locations and radiographic features were varied, all clinical courses were poor. Notably, all tumor tissues harbored TP53 mutation and PDGFRA amplification, while no tumor tissues harbored TERT promoter mutation, EGFR amplification and 7+/10-.
PATH-22. CLINICAL FEATURES AND MOLECULAR CHARACTERIZATION OF LEPTOMENINGEAL DISEASE IN PATIENTS WITH HIGH GRADE GLIOMA
Authors: Shoaf Madison , Chow Frances , Xiu Joanne , Glantz Michael , Aulakh Sonikpreet , Ashley David , Lipp Eric S , Lopez Giselle , Sumrall Ashley , Walker Phillip , Spetzler David , Nicolaides Theodore , Peters Katherine B
Published: November 2022

INTRODUCTION

Leptomeningeal disease (LMD) is a challenging complication of high grade glioma (HGG) and critical questions remain unanswered regarding clinicopathologic risk factors, molecular associations, and optimal treatment.

METHODS

Patients with molecularly-profiled HGG (Caris Life Sciences; Phoenix, AZ) with LMD at two institutions were included. Medical records were reviewed for clinicopathological characteristics, treatment, and outcome. Kaplan-Meier estimates of patient survival were performed on censored data using Cox’s proportional hazard model.

RESULTS

43 patients (male: 33, female: 10; median age: 56 years) were identified, comprising 41 grade 4 (glioblastoma: 38; gliosarcoma: 2; H3K27M diffuse midline glioma: 1) and 2 grade 3 tumors (astrocytoma: 1; pleomorphic xanthoastrocytoma: 1). LMD diagnosed at HGG diagnosis (n=18) versus recurrence (n=22) was associated with longer post-LMD survival [pLMD-OS: 15.3m vs. 4.8m, HR: 0.07, 95% CI: 0.02-0.29, p=0.0004] but similar overall survival [mOS: 15.3m vs. 12.3m; HR: 0.82; 95% CI: 0.36-1.85; p=0.63]. Pathology-diagnosed LMD (n=15) versus MRI-diagnosed LMD (n=26) was associated with longer post-LMD survival [pLMD-OS: 15.4m vs. 5.2m, HR: 14.9, 95% CI: 0.01-0.30, p=0.0004] but similar overall survival [mOS: 17.1m vs. 12.3m; HR: 0.66; 95% CI: 0.3-1.58; p=0.38]. Post-LMD survival was significantly prolonged for supratentorial (n=28) versus infratentorial/spinal (n=4) locations regardless of the diagnostic modality [pLMD-OS: 2.6m vs. 11.3m, HR: 14.4, 95% CI: 2.73-75.7, p=0.0017], and did not significantly differ between symptomatic (n=20) and asymptomatic (n=23) patients [pLMD-OS: 4.8m vs. 11.2m, HR: 1.75, 95% CI: 0.82-3.77, p=0.15). pTERT mutation (81%), EGFR amplification (43%), and MGMT methylation (33%) were prevalent but IDH1 mutation was rare (2.8%). Comparison with a separate glioblastoma cohort (n=1400) suggested more frequent amplification of CHIC2, MDM4, and KDR, higher mutation rates of RUNX1, APC, and RAD51C, colder tumor microenvironment (TME), and lower expression of immune checkpoint-related genes.

CONCLUSIONS

Clinicopathological characteristics affect post-LMD survival, and cohort comparison suggests molecular and TME differences in LMD-HGG tumors.
PATH-29. BIOLOGIC AND CLINICAL SIGNIFICANCE OF NEOPLASTIC AND MYELOID STATES IN DIFFUSE GLIOMAS
Authors: Mehani Bharati , Asanigari Saleembhasha , Chung Hye-Jung , Dazelle Karen , Aldape Kenneth
Published: November 2022

INTRODUCTION

Interactions between neoplastic cell characteristics and the immune microenvironment is an important yet understudied area in diffuse gliomas. We examined this by analyzing single cell RNAseq data (n=290,738) from 35 glioma patients (in-house and GSE182109) including 24 IDH-wildtype and 11 IDH-mutant tumors at the single cell level. Cells were assigned to specific classes, including neoplastic cell states and myeloid components, based on established methods. We used these data to develop a signature matrix to deconvolve bulk RNAseq data from TCGA diffuse gliomas.

RESULTS

Clustering of neoplastic cells revealed 4 cell states: proneural, mesenchymal, proliferative and cancer signaling. Elevated proneural cell state was, as expected, positively correlated with IDH-mutant tumors, and elevated mesenchymal cell state was correlated with IDH wildtype tumors (both p< 0.01, chi-square), Among IDH-mutant tumors, 1p/19q co-deletion was associated with the proneural state. Fourteen distinct myeloid cell subtypes were identified, and among them several were enriched for either microglial or macrophage (or both) markers. We examined relationships of neoplastic cell state with myeloid cell type abundance and significant associations were identified in both IDH-mutant tumors and IDH-wildtype tumors between specific myeloid cell states and myeloid clusters. Among IDH-mutant tumors, 1p/19q-co-deletion status was positively associated with abundance of a specific microglial cell cluster but negatively associated with abundance of 3 other myeloid cell clusters, suggesting substantial microenvironmental differences based on 1p/19q status in IDH-mutant tumors. In survival analyses, a specific myeloid cluster group proved interesting, with increased abundance of this group showing improved survival in both IDH-mutant and -wildtype tumors, in a manner that was enriched in specific neoplastic cell states. Overall, results suggest specific interactions between neoplastic cell characteristics, (cell states, IDH- and 1p/19q status), with myeloid cell and highlight the need of further studies understand the biological significance of the immune cell environment in diffuse gliomas.
PATH-36. GENOMIC MARKERS OF RECURRENCE RISK IN ATYPICAL MENINGIOMA FOLLOWING GROSS TOTAL RESECTION
Authors: Vaubel Rachael , Kumar Rahul , Weiskittel Taylor , Jenkins Sarah , Dasari Surendra , Uhm Joon , Lachance Daniel Honore , Brown Paul , Van Gompel Jamie , Kipp Benjamin , Sukov William , Giannini Caterina , Johnson Derek , Raghunathan Aditya
Published: November 2022

Atypical meningiomas (CNS WHO grade 2) show an intermediate risk of recurrence/progression, and molecular parameters informing management following gross total resection (GTR) remain to be established. We performed comprehensive genomic analysis in 63 patients who underwent GTR of an atypical meningioma. Median age was 59.8 years, 41 patients (65.1%) were female, and 8/62 patients (12.9%) underwent radiation therapy following resection. Follow-up was available for all patients (median 10 years), with recurrence/progression in 17 (5-year PFS: 84.2%; 10-year PFS: 71.0%). Tumors were evaluated by next generation sequencing (n=61), chromosomal microarray (n=63), DNA methylation profiling (n=62), and H3K27me3 immunohistochemistry (n=62). Next generation sequencing using a CLIA-certified 50-gene Neuro-Oncology specific panel identified pathogenic alterations in NF2 (n=31; 50.8%), SMARCB1 (n=5; 8.2%), AKT1 (n=2; 3.3%), TERT (n=1, 1.6%), SMO (n=1), SUFU (n=1), TP53 (n=1), and PTEN (n=1). There was trend toward decreased PFS in patients with NF2 alterations (p=0.078) which was not statistically significant. Copy number variants (CNVs) were identified using OncoScan (ThermoFisher). The most frequent CNVs were loss -22 (n=45; 71.4%), -1p (n=30, 47.6%), -14 (n=21, 33.3%), -6q (n=18, 28.6%), -10q (n=15, 23.8%), -X (n=13, 20.6%), and -18 (n=11, 17.4%). CNVs associated with decreased PFS included -1p (p=0.015), -7p (p=0.007), -10q (p=0.0007), and -18 (p=0.0376). DNA methylation profiling was performed using the Illumina MethylationEPIC array (n=62) and evaluated using the DKFZ classifier (v12.3). Based on the highest score, tumors matched to benign (n=32; 51.6%) or intermediate (n=29; 46.7%) meningioma subclasses or showed non-contributory profiles (n=1; 1.6%). Methylation class (benign vs. intermediate) showed no association with PFS (p=0.96). H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 (6.5%) tumors, which was insufficient for PFS analysis. Overall, our study supports CNV profiling of atypical meningiomas following GTR, which can be implemented using existing, clinically validated technologies. Molecular predictors could guide use of radiotherapy in these patients.
PATH-37. SPATIAL GENE EXPRESSION PROGRAMS AND PROTEIN SIGNALING MECHANISMS DRIVE MENINGIOMA EVOLUTION AND THERAPEUTIC VULNERABILITY
Authors: Lucas Calixto-Hope , Vasudevan Harish , Seo Kyounghee , Chen William , Liu S John , Choudhury Abrar , Magill Stephen , Solomon David A , Perry Arie , Raleigh David
Published: November 2022

Intratumor heterogeneity drives cancer evolution and resistance to therapy, but unbiased approaches to elucidate mechanisms driving intratumor heterogeneity have been lacking. Here, we integrate spatial gene expression programs and protein signaling mechanisms across 16 meningioma samples to define how intratumor heterogeneity drives molecular classification, temporal evolution, or spatial evolution of the most common primary intracranial tumor. Spatial transcriptomic analysis was performed on 38,718 regions and spatial profiling of 72 proteins comprising proliferation, stress, microenvironment, immune, or signaling modules was performed on 82 regions. DNA methylation, copy number variant, targeted gene expression profiling, targeted DNA sequencing, histologic, or immunohistochemical analyses (Ki67, H3K27me3, p16) were performed on all meningiomas to study intratumor heterogeneity in the context of pre-existing classification schemes. Primary meningioma cells, CRISPR interference, pharmacology, and 3D co-culture models were used for mechanistic and functional validation. Spatial analyses revealed significant intratumor or intertumor heterogeneity irrespective of meningioma histologic subtype or grade, DNA methylation group (Merlin-intact, Immune-enriched, Hypermitotic), copy number variant (1p loss, 1q gain, 6p gain, 9p loss, 14q loss, 22q loss), gene expression risk score, or driver mutation (NF2, CDKN2A/B, TERT promoter, BAP1, SMARCB1, ARID1A). Spatial analyses on paired histologically or molecularly distinct regions from individual meningiomas (n=4), or on paired primary and recurrent meningioma samples (n=9), revealed conserved proliferation, immune, or signaling mechanisms underlying meningioma evolution. Mechanistic and functional studies validated therapeutic vulnerabilities across spatial clusters to combinations of FDA-approved small molecules inhibiting the cell cycle (abemaciclib), epigenetic regulators (vorinostat), the DNA damage response (nariparib), MAPK signaling (erlotinib, selumetinib), or PI3K-AKT signaling (copanlisib). In summary, these data define spatial gene expression programs and protein signaling mechanisms driving meningioma evolution and therapeutic vulnerability, shedding light on past clinical failures and elucidating novel combinations of concurrent or sequential molecular therapies to treat meningiomas that are resistant to standard interventions.
NIMG-01. PREDICTING POST-STEREOTACTIC RADIOTHERAPY MAGNETIC RESONANCE IMAGE OUTCOMES OF BREAST CANCER METASTASES TO THE BRAIN
Authors: Pandey Shraddha , Kutuk Tugce , Mills Matthew , Abdalah Mahmoud , Stringfield Olya , Latifi Kujtim , Moreno Wilfrido , Ahmed Kamran , Raghunand Natarajan
Published: November 2022

BACKGROUND

Stereotactic radiosurgery (SRS) is a cornerstone in the management of Breast Cancer Metastases to the Brain (BCMB). While control rates are high following SRS, radiation necrosis is a rare but potentially devastating long-term toxicity. There is a clinical need for automated/semi-automated methods to assess tumor response and optimize the RT plans for local control with minimal long-term toxicity. Multiparametric MRI (mpMRI), particularly Apparent Diffusion Coefficient of water (ADC) maps, contain information that is mechanistically relatable to voxel-level tumor response to RT. We report a deep learning-based approach to predict post-SRS ADC maps, FLAIR, T2-weighted (T2W), T1-weighted unenhanced (T1W) and contrast-enhanced (T1WCE) images, from pre-SRS T1W, T1WCE, T2W and FLAIR images, ADC maps, and the delivered RT dose map. These “forward models” will enable the radiation oncologist to simulate radiologic outcomes and iteratively optimize RT plans for local control with minimal toxicity.

METHODS

We trained a variant of the pix2pix Generative Adversarial Network (GAN) on MRI and RT dose map data from 18 BCMB patients treated with stereotactic radiation with confirmed controlled and locally recurrent metastases. Patients were treated with stereotactic radiation dose of 1-40 Gy between 2013-2019.

RESULTS

On test data from 6 BCMB patients, the trained forward model predicted post-SRS ADC values within the Gross Tumor Volume (GTV) that were broadly in agreement with ground truth post-SRS ADC maps. In agreement with expectations, the forward model also predicts increasing post-RT ADC within the GTV with increasing simulated RT doses in the range of 1-71 Gy. We have also explored an inverse model to predict the RT dose map required to produce “prescribed” post-SRS ADC values within the GTV.

CONCLUSIONS

We envision that the forward models will assist the radiation oncologist in initial RT dose plan optimization, while the inverse model may be useful for daily RT plan optimization.
NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET
Authors: Jekel Leon , Bousabarah Khaled , Lin MingDe , Merkaj Sara , Kaur Manpreet , Avesta Arman , Aneja Sanjay , Omuro Antonio , Chiang Veronica , Scheffler Björn , Aboian Mariam
Published: November 2022

PURPOSE

Monitoring metastatic disease to the brain is laborious and time-consuming, especially in the setting of multiple metastases and when performed manually. Response assessment in brain metastases based on maximal unidimensional diameter as per the RANO-BM guideline is commonly performed1, however, accurate volumetric lesion estimates can be crucial for clinical decision-making2 and enhance outcome prediction3. We propose a deep learning (DL)-based auto-segmentation approach with the potential for improvement of time-efficiency, reproducibility and robustness against inter-rater variability. Materials and

METHODS

We retrospectively retrieved 259 patients with a total number of 916 lesions from our institutional database from 2014 - 2021. Patients with prior history of local radiation therapy or surgery were excluded. Manually generated trainee segmentations were revised and adjusted by a board-certified radiologist and served as ground truth for evaluation of segmentation accuracy. Model performance was tested via dice-similarity-coefficient (DSC). Volumetric measurements were then obtained within our PACS-integrated workflow on Visage 7 (Visage Imaging, Inc., San Diego, CA) at the click of one button.

RESULTS

For model training and evaluation, a train-test split of 90:10 on patient-level was performed (n= 234:25 (Patients), n= 861:55 (Lesions). A DL-algorithm (nnUNet) was incrementally trained on 10 batches of 23 patients. The DSC of the U-Net gradually increased throughout the training process and heuristically reached a plateau of 0.85. The sensitivity of the algorithm was 83% with detection of 46 out of 55 lesions in the testing dataset. The lesions that were not detected by the algorithm were below 5 mm in size. The false positive rate was 8% (n=4/50).

CONCLUSION

Our study demonstrates the feasibility of PACS-based integration of automatized segmentation workflows of brain metastases. An incremental-training approach is recommended to adapt DL algorithms to specific hospital settings.
NIMG-03. PREOPERATIVE DEXAMETHASONE LEADS TO SIGNIFICANT REDUCTIONS IN PERITUMORAL EDEMA FOR PATIENTS WITH BRAIN METASTASES
Authors: Tan Hao , Bowden Stephen , Cheaney Barry , Richie Emma , de Leeuw Charles , Wood Matthew , Raslan Ahmed , Han Seunggu , Barajas Ramon
Published: November 2022

INTRODUCTION

Corticosteroids are commonly used preoperatively in patients with brain metastases to mitigate symptoms related to vasogenic peritumoral edema (PTE). Though these tumors are highly prevalent, surprisingly few studies have examined the effect of steroids on PTE evident on MRI. We investigated whether steroid administration was associated with greater reduction in PTE in brain metastasis patients compared to steroid-naïve patients. Additionally, we sought to determine if PTE reductions reflect preoperative neurologic improvement and whether an association existed between change in PTE and preoperative steroid dosing.

METHODS

We identified 27 patients with brain metastases and two pre-operative MRIs—one pre- and one post-steroid administration. PTE and tumor volume was calculated from FLAIR or T2 sequences using 3D Slicer. PTE was quantified with Edema Index (EI). Pertinent demographic, clinical, and dexamethasone dosing data were extracted from patient charts. Descriptive statistics were performed to compare demographic, clinical, and radiographic data between patient strata. A Pearson's R correlation was performed to assess for an association between percent change in PTE and dexamethasone dosing. All statistics were computed in R with a threshold of α= 0.05.

RESULTS

Patients that received preoperative dexamethasone demonstrated significantly greater percent reduction in PTE compared to their steroid naïve counterparts (p = 0.02). This finding was despite similar time elapsed between MRI studies across both groups. Similar PTE changes were observed in those with and without preoperative neurologic improvement. No significant correlation was found between percent-change in PTE and dexamethasone dosing.

CONCLUSIONS

We are the first to demonstrate that preoperative dexamethasone administration significantly reduces PTE in patients with brain metastases when compared to a steroid naïve reference group. Curiously, a superior reduction in PTE was not reflective of neurologic improvement. The clinical significance of this merits further study.
NIMG-09. BRAIN METASTASIS FROM PANCREATIC CANCER: OUR EXPERIENCE AND SYSTEMATIC REVIEW
Authors: Destefano Frank , Morell Alexis , Marks Katya , Fernandez Sophia , Mayo Timothy , Smith Grace , Merenzon Martin , Shah Ashish , Luther Evan , Ivan Michael , Komotar Ricardo , Eichberg Daniel
Published: November 2022

BACKGROUND AND PURPOSE

The goal of this study was to systematically review the existing literature on neurosurgical management and outcomes of brain metastasis from pancreatic cáncer in comparison to our institutional experience of this patient cohort.

METHODS

Following the PRISMA guidelines, a systematic literature review was performed using PubMed, Ovid Embase, Scopus, and Web of Science databases from date of database inception to January 2022. Studies were selected based on predetermined inclusion and exclusión criteria. Simulteanously, a retrospective analysis was conducted on patients who underwent neurosurgical evaluation and treatment for intracranial metastatic lesions from pancreatic origin at a single institution.

RESULTS

Of the total 292 articles in the original literature search, 17 studies were ultimately selected. A total of 23 patients with brain metastases of pancreatic origin were included. Median overall survival from original diagnosis of pancreatic cancer was 22 months (3-84). In our institution’s cohort, 4 patients were identified with a median overall survival of 30.5 months (2- 108). Our institutional cohort experienced a prolonged median overall survival (3 vs 30.5 months, p=0.03) in comparison to the literature.

CONCLUSIONS

Brain metastasis is a rare and fatal outcome of pancreatic cancer that carries a poor survival. However, based on the data presented in this review, there are patient-specific and treatment-related factors that could signal better prognosis. Further studies are needed to elucidate multimodal therapy and survival to suggest a more personalized decision-making process.
NIMG-11. THE EXTENT OF NECROSIS IN BRAIN METASTASES MAY PREDICT SUBTYPES OF PRIMARY CANCER AND OVERALL SURVIVAL IN PATIENTS RECEIVING CRANIOTOMY
Authors: Yoo Jihwan , Cha Yoon Jin , Ahn Sung Jun , Park Hun Ho
Published: November 2022

Although necrosis is common in brain metastasis (BM), its biological and clinical significances remain unknown. We evaluated necrosis extent differences by primary cancer subtype and correlated BM necrosis to overall survival post craniotomy. We analyzed 145 BM patients receiving craniotomy. Necrosis to tumor ratio (NTR) was measured. Patients were divided into two groups by NTR: BMs with sparse necrosis and with abundant necrosis. Clinical features were compared. To investigate factor relevance for BM necrosis, multivariate logistic regression, random forests, and gradient boosting machine analyses were performed. Kaplan–Meier analysis and log-rank tests were performed to evaluate the effect of BM necrosis on overall survival. Lung cancer was a more common origin for BMs with abundant necrosis (42/72, 58.33%) versus sparse necrosis (23/73, 31.51%, p< 0.01). Primary cancer subtype and tumor volume were the most relevant factors for BM necrosis (p< 0.01). BMs harboring moderately abundant necrosis showed longer survival, versus sparse or highly abundant necrosis (p=0.03). Lung cancer BM may carry larger necrosis than BMs from other cancers. Further, moderately abundant necrosis in BM may predict a good prognosis post craniotomy.
NIMG-15. INTEGRATED RADIOGRAPHIC AND PATHOLOGICAL ANALYSIS OF MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNSTS) REVEALS IMAGING CORRELATES OF MOLECULAR ALTERATIONS AND CLINICAL OUTCOME
Authors: Diwanji Devan , Texiera Stephanie , Lucas Calixto-Hope , Calabrese Evan , Reddy Alyssa , Pekmezci Melike , Jacques Line , Chin Cynthia , Vasudevan Harish
Published: November 2022

Malignant peripheral nerve sheath tumors (MPNSTs) are the most common cause of death in patients with neurofibromatosis type 1 (NF-1) yet non-invasive diagnosis and risk stratification of NF-1-associated peripheral nerve tumors remains challenging. Moreover, the relationship between radiographic features and pathologic measures such as mitotic index and necrosis, or molecular markers of malignant transformation such as H3K27 trimethylation and Schwann cell marker (S100B, SOX10) expression remain unknown. Here, we integrate positron emission tomography (PET) (n=32 studies from 18 patients) and magnetic resonance imaging (MRI) (n=20 studies from 20 patients) with immunohistochemical analysis and clinical follow-up in a total of 20 patients with pathologically confirmed MPNST diagnosis. The median age was 33 years (range 5-74 years), n = 11 were male (55%) and n=13 (65%) harbored a clinical diagnosis of NF-1. Anatomically, n=6 (30%) tumors arose near the spine, n=5 (25%) arose in the extremities, and n=4 (20%) arose in the sacrum. By PET, the average preoperative SUVmax was 8.53 with a trend toward increased SUVmax in Ki-67 high tumors (SUVmax 10.58 vs. 6.47, p=0.08). No significant differences in SUVmax based on H3K27 trimethylation, SOX10, or S100B expression was noted. The appearance of cystic necrosis by MRI was significantly associated with H3K27 trimethylation loss (Chi-square p=0.04) with a trend toward increased KI-67 labeling (43% vs. 24%, p = 0.07), suggesting cystic necrosis identifies aggressive lesions. No other features were correlated with H3K27 trimethylation status or Ki-67 labeling index. With regard to clinical outcome, dichotomized SUVmax identified patient subgroups with significant differences in overall survival (OS), with high SUVmax tumors demonstrating improved survival (median OS: 250 months vs. 60 months, log-rank test p=0.02). Our data support a relationship between radiographic features, histopathologic characteristics and clinical outcomes. Future work will include longitudinal analysis, validation in larger, multi-institutional cohorts and incorporation of radiomic approaches.
NIMG-25. OPTIMIZATION OF ARTIFICIAL INTELLIGENCE ALGORITHMS FOR LOW-RESOURCE/CLINICAL ENVIRONMENTS: FOCUS ON CLINICALLY-RELEVANT GLIOMA REGION DELINEATION
Authors: Baheti Bhakti , Thakur Siddhesh , Pati Sarthak , Karkada Deepthi , Panchumarthy Ravi , Wu Junwen , Mohan Suyash , Nasrallah MacLean , Shah Prashant , Bakas Spyridon
Published: November 2022

BACKGROUND

Diffuse astrocytic glioma are common and aggressive malignant primary brain tumors with grim prognosis. Artificial intelligence (AI) has shown promise across predictive, prognostic, and diagnostic neuro-oncology applications, towards improving patient management. However, clinical translation and deployment are hampered by AI models’ requirements for explicit acceleration cards, which are not typically considered in clinical environments. Here, we seek the execution of AI models in such clinical/low-resource environments, facilitated by mathematical optimizations rather than investing in acceleration cards, and focus on the use case of delineating clinically-relevant regions, namely the entire tumor burden (ETB) and tumor core (TC).

METHODS

We identified the BraTS20 retrospective cohort of 369 glioma cases, each described by 4 structural multi-parametric MRI (mpMRI) scans (T1,T1Gd,T2,T2-FLAIR). The Generally Nuanced Deep Learning Framework (GaNDLFv0.0.14/PyTorchv1.8.2) was used to train the original AI model (ResUNet), by randomly sampling 40 643 patches from each mpMRI scan. We then investigate the contributions of post-training mathematical optimizations. Quantitative performance evaluation of the original and optimized (GaNDLFv0.0.14/OpenVINOTM-INT8-v2022.1.0) models were based on 125 unseen hold-out cases (BraTS20 validation dataset), using the dice similarity coefficient (DSC), while profiling in a consumer-grade workstation, i.e., typical clinical hardware configuration.

RESULTS

Negligible delineation performance differences were observed between the original and optimized AI models, for both ETB (DSCoriginal/DSCoptimized= 0.877/0.876) and TC (DSCoriginal/DSCoptimized= 0.773/0.772). However, the optimized model yielded substantial improvements in latency (up to 5.4x faster inference) and 53% less memory footprint.

CONCLUSIONS

Post-training mathematical optimization of AI models yields substantial gains in latency and memory usage, while maintaining their accuracy. Although we focused on delineation, we anticipate these mathematical optimizations to be applicable in other AI models. Post-training optimization is a promising approach for deploying AI models on consumer-grade CPUs, and hence facilitating their translation in low-resource/clinical environments, potentially contributing to improved patient management, treatment decisions, and response assessment.

*author Siddhesh Thakur is an equal contributing first author.
NIMG-35. THE DEEP TOPOLOGY OF GLIOMAS
Authors: Ruffle James , Mohinta Samia , Brandner Sebastian , Hyare Harpreet , Nachev Parashkev
Published: November 2022

Tumor heterogeneity is increasingly recognized as a major obstacle to therapeutic innovation in neuro-oncology. Gliomas typically exhibit a spectrum of genetic mutations, reflecting multiple, potentially complex molecular interactions, and vary widely in their imaging appearances across size, morphology, texture, anatomical distribution, and many other spatial properties. This multifaceted diversity obscures the relationship between tumor genotypic and imaging features, and their joint influence on disease evolution and treatment susceptibility. Though opaque to simple statistical methods, it may nonetheless be accessible to models with the expressivity to capture complex interactions between many different factors. Studying an international sample of 4064 IDH-wildtype glioblastoma, IDH-mutant oligodendroglioma and astrocytoma, here we develop a highly expressive modelling framework based on deep learning and Bayesian models of networks with the power to capture the multimodal heterogeneity of glioma. We map the characteristic distribution of tumor tissue components across the brain, and link their spatial properties to regional connectivity, normal gene expression, and tumor genetics. We derive a hierarchical anatomical representation of these multimodal patterns of variation, and quantify their comparative functional impact by reference to meta-analytic maps of brain function. Our results reveal the deep topological structure of gliomas, enabling richer, more closely individuating phenotyping, the development of more robust predictive models of treatment and prognosis, and illuminating multimodal associations of potential mechanistic significance.
NIMG-36. MR PERFUSION IS A BETTER PREDICTOR OF TUMOR RECURRENCE AND TREATMENT RESPONSE THAN STANDARD MRI IN SOME OLIGODENDROGLIOMA PATIENTS
Authors: Connelly Jennifer , Prah Melissa , Schmainda Kathleen
Published: November 2022

Dynamic susceptibility contrast (DSC) perfusion MRI (pMRI) has proven efficacy in the evaluation of glioma grade, progression and response to therapy. While not currently integrated within RANO (Response Assessment in Neuro Oncology) criteria, pMRI is often assessed alongside standard imaging of astrocytic glioma. In contrast, evaluation of pMRI in oligodendroglioma, has been less well-studied despite its potential to evaluate the comparatively greater vascularity of its “chicken-wire” capillary networks. Moreover, standard imaging to evaluate oligodendroglioma course can be confounded by slow-growing enhancement that does not meet RANO threshold for progression, or disease control despite low or absent radiographic response, and radiation-induced leukoencephalopathy resembling hyperintense changes on T2w and FLAIR imaging. Consequently, there is a clinical need to understand the potential role of pMRI in the evaluation of oligodendroglioma. As an initial step, we present here a case study of a patient with oligodendroglioma for which pMRI bested standard imaging in evaluation throughout radiographic disease surveillance. A 32 y/o gentleman with oligodendroglioma WHO grade 2 (IDH mutant, 1p19q codeletion) completed standard treatment, including surgical resection, irradiation, and chemotherapy. During surveillance imaging, a region of hyperperfusion on pMRI developed. However, since standard imaging was stable, RANO criteria was not met and observation was continued without intervention. Subsequent standard imaging demonstrated clear radiographic progression, reflecting the radiographic findings of pMRI but with delayed manifestation. Systemic chemotherapy was resumed with successively stable standard MR imaging. In contrast, the previous region of hyperperfusion improved over serial pMRI, and more consistently mirrored the patient’s clinical course. This case illustrates the timely radiographic importance of pMRI in assessing stability of tumor as well as treatment response. Considerations for the inclusion of pMRI within RANO criteria should be explored further.
NIMG-37. JOINT LEARNING OF IMAGING AND GENOMIC DATA REVEALS DISTINCT GLIOBLASTOMA SUBTYPES
Authors: Guo Jun , Kazerooni Anahita Fathi , Akbari Hamed , Toorens Erik , Sako Chiharu , Mamourian Elizabeth , Koumenis Constantinos , Bagley Stephen , Binder Zev A , Lustig Robert , O'Rourke Donald , Ganguly Tapan , Bakas Spyridon , Nasrallah MacLean , Davatzikos Christos
Published: November 2022

PURPOSE

The significant heterogeneity of glioblastoma is typically displayed on both phenotypical and molecular levels. Non-invasive in vivo approaches to characterize this heterogeneity would potentially facilitate personalized therapies. Here we leverage advanced unsupervised machine learning to integrate radiomic imaging features and genomics to identify distinct subtypes of glioblastoma.

METHODS

A retrospective cohort of 571 IDH-wildtype glioblastoma patients were collected with pre-operative multi-parametric MRI (T1, T1CE, T2, T2-FLAIR, DSC, DTI) scans (available in 462/571 patients) and targeted next-generation sequencing (NGS) data (available in 355/571 patients). Radiomic features (n= 971) were extracted from these MRI scans and a subset of 12 features were selected by L21-norm minimization. A total of 14 key driver genes in the 5 main pathways that are most frequently altered in glioblastoma were chosen. Subtypes were identified by a joint learning approach called Anchor-based Partial Multi-modal Clustering (APMC) on both radiomic and genomic modalities.

RESULTS

Three distinct glioblastoma subtypes were discovered by APMC based on 14-dimension NGS data together with 12 selected radiomic features representing characteristics from histograms, shape, and volumetric measures for different tumor sub-regions. The identified subtypes were 1) high-risk; 2) medium-risk; and 3) low-risk, in terms of their overall survival outcome in Kaplan-Meier analysis (p= 5.52e-6, log-rank test; HR= 1.51, 95%CI:1.20-1.74, Cox proportional hazard model). The three subtypes also displayed different molecular characteristics: subtype 1 exhibited increased frequency of mutation in [EGFR, PIK3CA, PTEN, NF1], subtype 3 showed frequently mutated [PDGFRA, ATRX], while subtype 2 did not show significant differences for mutations in any of these genes.

CONCLUSION

Our results revealed the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities could help better understand the molecular basis of phenotypical signatures of glioblastoma and further provide insights into the biologic underpinnings of tumor formation and progression.