bluuebunny/crossref_metadata_2025_split huggingface dataset:
PATH-62. RAMAN-BASED AI PLATFORM TO ACCELERATE THE DIAGNOSIS AND MANAGEMENT OF GLIOMAS VIA RAPID METHYLATION PROFILING
Authors: Lita Adrian , Sjöberg Joel , Păcioianu David , Siminea Nicoleta , Celiku Orieta , Păun Andrei , Gilbert Mark , Noushmehr Houtan , Petre Ion , Larion Mioara 
Published: November 2023

Abstract

BACKGROUND
DNA methylation profiling and subtyping of gliomas has become an integral part of the diagnosis and treatment of glioma patients but remains a challenge due to the sample preparation and time requirements of the existing methylation profiling technology. We hypothesized that machine learning and Raman spectroscopy, which creates fast molecular fingerprints of samples in situ based on chemical signatures rather than the use of exogenous dyes, can provide a fast alternative to the existing approaches for methylation profiling and classification of gliomas.

METHODS
Raman spectroscopy at the spatial resolution of less than 300nm was used for molecular fingerprinting of the regions of interest using 1mm2 Formalin-Fixed Paraffin-Embedded (FFPE) tissue spots from 45 patient samples with known LGm1 to LGm6 methylation subtypes. Spectral information (over 1738 frequencies) was used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Oversampling was used to obtain subtype-balanced data distributions. Supervised wrapper methods and random forests were used to identify the top 20 most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the findings.

RESULTS
We developed APOLLO - a novel platform based on spontaneous Raman spectroscopy and machine learning -- for predicting the DNA methylation subtypes of FFPE glioma tissue specimens. APOLLO discriminates tumors from non-tumor areas with 98% accuracy and discriminates IDH1mut versus IDH1WT tumors with 82% accuracy. APOLLO also achieved high discriminatory power between G-CIMP-high and G-CIMP-low molecular phenotypes (ROC of 0.75), subtypes of lower-grade IDH1mut gliomas with significantly different clinical outcomes. We determined that the Raman shifts important for discriminating IDH1mut versus IDH1wt tumors are associated with novel lipid-metabolism signatures of IDH1mut glioma.

CONCLUSIONS
The development of APOLLO allows fast, reliable, and accurate prediction of methylation subtypes of glioma which can speed diagnosis and, once validated on fresh tissue, can be implemented in the operating room.
PATH-64. COMPREHENSIVE CNS TUMOUR MOLECULAR DIAGNOSTICS USING THIRD GENERATION SEQUENCING
Authors: Patel Areeba , Hinz Felix , Dogan Helin , Goebel Kirsten , Schrimpf Daniel , Goeller Pauline , Ritter Michael , Pfeifer Lisa , Blume Christina , Berghaus Natalie , Payne Alexander , Goidts Violaine , Loose Matthew , Unterberg Andreas , Wick Wolfgang , Pfister Stefan , Sill Martin , von Deimling Andreas , Jones David , Schlesner Matthias , Sahm Felix 
Published: November 2023

Abstract
The WHO classification of CNS tumours 2021 recommends reporting a wide range of molecular alterations for WHO-compatible diagnoses. Conventional workflows warrant considerable investment and long turnaround times limited by batching. Nanopore sequencing enables long read sequencing, real-time targeting and simultaneous base modification detection with compact devices. Rapid-CNS2- a rapid, comprehensive adaptive sampling based sequencing pipeline with a turnaround time of 5 days was previously described. The ad-hoc methylation classification model employed by Rapid-CNS2 is limited to MNP v11 classes. This study presents an optimised Rapid-CNS2 and validation cohort that successfully reports hard-to-detect variants. We also present a whole genome sequencing workflow that affords direct upload to the MNP website for MNP v12 classification. We sequenced and analysed 156 samples from the Department of Neuropathology, University Hospital Heidelberg using Rapid-CNS2. RESULTS: were compared to NGS panel sequencing and methylation array analyses. Rapid-CNS2 showed improved resolution over NGS panel sequencing for CNAs. High accuracy was obtained for detection of SNV/Indels. Long reads spanning the breakpoint identified clinically relevant fusions in 10/10 cases. Subclonal SVs like EGFR vIII missed by NGS panel sequencing were detected with high confidence. Complete concordance was achieved for MGMT promoter methylation status and methylation classification for MNP v11 classes. The optimized Rapid-CNS2 workflow shortened the turnaround time to 30 hours from sample receipt and minimum input DNA required was reduced to 500ng. For WGS, samples were sequenced for 72h on a Promethion device. WGS enabled accurate reporting of SNVs, CNAs, SVs, MGMT promoter status in addition to MNP v12 classification directly from the website eliminating the need for ad-hoc training of methylation classification models. The presented nanopore sequencing based workflows provide feasible single assay alternatives to conventional molecular diagnostic approaches with the aim to make such analyses more accessible.
PATH-66. SEQ-ING THE SINES OF CENTRAL NERVOUS SYSTEM TUMORS IN CEREBROSPINAL FLUID DNA
Authors: Douville Christopher , Bettegowda Chetan 
Published: November 2023

Abstract
It is often challenging to distinguish cancerous from non-cancerous lesions in the brain using conventional diagnostic approaches. We here introduce an analytic technique called Real-CSF to detect cancers of the central nervous system from evaluation of DNA in the cerebrospinal fluid (CSF). Short interspersed nuclear elements (SINEs) from throughout the genome are PCR-amplified with a single primer pair and the PCR products are evaluated by next generation sequencing. Real-CSF assesses genome wide copy number alterations in each of 39 chromosome arms as well as focal amplifications of selected oncogenes. Real-CSF was applied to 280 CSF samples and correctly identified 67% of 184 cancers and 96% of 96 non-cancerous brain lesions. CSF analysis was considerably more sensitive than standard of care cytology and plasma cell-free DNA analysis in the same patients. Real-CSF therefore has the capacity to be used in combination with other clinical, radiologic, and laboratory-based data to inform the diagnosis and management of patients with suspected cancers of the brain.
NIMG-01. AI-BASED ASSESSMENT OF BRAIN VOLUME DECREASE AFTER SRS AND WBRT
Authors: Thiele Frederic , Kawula Maria , Katzendobler Sophie , Bodensohn Raphael , Kurz Christopher , Landry Guillaum , Weller Jonathan , Niyazi Maximilian 
Published: November 2023

Abstract

BACKGROUND
Stereotactic radiosurgery (SRS) for 4-10 brain metastases has been shown to be associated with prolonged survival as compared to whole brain radiotherapy (WBRT). Brain volume decrease after WBRT is a marker for poor survival. We aimed at comparing brain volume decrease in patients treated with either SRS or WBRT.

METHODS
Patients treated with SRS were drawn from the prospective dataset of the STEREOBRAIN study (DRKS00014694) that recruited from 2017 to 2020. Patients that received WBRT for 4-10 brain metastases were included retrospectively from 2012 - 2017. Images were registered to the MNI305 space utilizing the Advanced Normalization Tools (ANTs) Toolkit. Brain extraction was performed using the deep-learning based HD-BET UNet. Anatomical brain structures were labelled by employing the current state-of-the-art pretrained transformer based UNesT network using the MONAI deep-learning framework. Labels were bundled and analysed. Last pre-radiotherapy scans were compared with the last post-radiotherapy scans. The proportion of the ventricular system to the total intracranial volume was implemented as a measure of brain volume decrease.

RESULTS
Overall, 65 patients were included. 29 patients (45%) received SRS and 36 patients (55%) were treated with WBRT. Patients treated with SRS showed no significant increase in the volume of the ventricular system compared to the total intracranial volume (p= 0.13), whereas patients in the WBRT cohort did (p&lt; 0.01). The mean relative volume increase of the ventricular system was 33% in the SRS cohort. In the WBRT cohort, the mean increase was 54%. The median time between the two scans was 414 days in the SRS cohort and 244 days in the WBRT cohort.

CONCLUSION
Brain volume decrease is less extensive in patients treated with SRS than WBRT. The clinical implication of this finding, e.g., prolonged survival and better clinical status, is yet to be determined and quantified.
NIMG-02. ADVERSE RADIATION EFFECT VERSUS TUMOR PROGRESSION FOLLOWING STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASES: IMPLICATIONS OF RADIOGRAPHIC UNCERTAINTY
Authors: Salans Mia , Ni Lisa , Boreta Lauren , Theodosopoulos Philip , Morin Olivier , Braunstein Steve 
Published: November 2023

Abstract
Despite efforts from RANO to standardize treatment response reporting, adverse radiation effect (ARE) following stereotactic radiosurgery (SRS) for brain metastases (BM) remains challenging to distinguish from tumor progression (TP) due to radiographic uncertainty (RU), often necessitating serial imaging until definitive diagnosis (DD) is resolved. We characterized management and implications of RU after BM irradiation for multiple primary tumor histologies. We identified 138 BM cases reviewed at our multi-disciplinary SRS Tumor Board (TB) for RU of ARE versus TP on MRI following SRS between 2015-2022. Target diameter (mm), histology, location, and SRS dose (Gy) on initial treatment and lesion diameter, associated symptoms, and supportive medications at the time of TB presentation were extracted from the medical record. Final lesion etiology, use of any intervention, and neurologic deficits upon DD were documented. Time from RU to DD was calculated. Median time from SRS to initial RU was 11.3 months (IQR 5.5-18.2). Median RU lesion diameter was 14.0 (IQR 8.0-20.0). At the time of RU, 43% of patients were symptomatic, 28% and 4% requiring steroids and bevacizumab, respectively. Time to DD following RU was &gt; 6 and 12 months in 28% and 8% of patients, respectively (maximum 47 months). Median lesion diameter at DD was 16.0 mm (IQR 10.3-24.0). Most (54%) had a DD of ARE, while 41% had TP; 23% and 77% were diagnosed pathologically and radiographically, respectively. Upon DD, 41% underwent local intervention with surgery and/or re-irradiation and 25% had a new neurologic deficit, including weakness (7%), ataxia (5%), and aphasia (4%). In an interdisciplinary setting, it took &gt; 6 months to reach a DD after RU in &gt; 25% of patients previously irradiated for BM. Diagnostic uncertainty and delay may lead to sub-optimal outcomes and symptom burden. Improved characterization of post-SRS imaging findings is needed in this population.
NIMG-09. APPLICATION OF FRACTIONAL TUMOR BURDEN MAPPING TO MONITOR GLIOBLASTOMA PROGRESSION IN THE PHASE 1 TRIAL OF GALLIUM MALTOLATE
Authors: Zoss Casey , Connelly Jennifer , Chitambar Christopher , Schmainda Kathleen 
Published: November 2023

Abstract

BACKGROUND
To address the need for improved glioblastoma (GBM) therapeutics, a Phase 1 trial of gallium maltolate (GaM) for recurrent GBM is ongoing. GaM targets GBM iron metabolism, representing a novel therapeutic mechanism to treat GBM. Monitoring GBM progression is typically accomplished by standard MRI protocols. However, distinguishing progression from treatment effect is often difficult using standard MRI alone; this is especially true for experimental therapeutics. Fractional tumor burden (FTB) mapping incorporates dynamic susceptibility contrast perfusion MRI to generate spatial and quantifiable tumor likelihood estimates. Here, we present three cases from the Phase I trial of GaM to illustrate application of FTB mapping in the clinical trial setting.

METHODS
Patients enrolled in the GaM trial receive pre-trial baseline imaging and subsequent imaging at 8-week intervals. For each imaging timepoint, an FTB map was created from tissue-validated thresholds of standardized relative cerebral blood volume (sRCBV) in areas of enhancement as determined by pre/post T1 subtraction.

RESULTS
Case #1 illustrates low FTB-determined tumor probability in a patient with minimal disease who did not progress while on trial for &gt; 33 weeks. Case #2 shows an increase in tumor probability concurrent with radiographic and pathology-confirmed progression soon after initiating GaM. Case #3 demonstrates an increase in tumor probability soon after GaM initiation which preceded an increase in volume by &gt; 10 weeks; GBM progression was confirmed by pathology.

CONCLUSIONS
The presented cases illustrate the utility of FTB mapping for monitoring GBM progression in the setting of GaM treatment. FTB maps could predict disease progression before standard MRI in some patients. Our experience implicates FTB mapping as an important imaging tool in future GBM clinical trials. The cases add to previous reports that FTB is useful in distinguishing progression from treatment effect with standard treatment regimens.
NIMG-15. COMBINED ADVANCED IMAGING AND MGMT STATUS FOR DISTINCTION BETWEEN EARLY TUMOR PROGRESSION AND PSEUDOPROGRESSION IN GLIOBLASTOMA
Authors: Diaz Maria , Joanta-Gomez Adela , Torres Denisse , Furnari Julia , Matari Nahill , Canoll Peter , Lignelli Angela , Lassman Andrew 
Published: November 2023

Abstract

BACKGROUND
Within 3-6 months of completing initial chemoradiotherapy, up to 50% of glioblastomas (GBM) develop worsening contrast enhancement on surveillance imaging, which could represent early progression (EP) or pseudoprogression (PsP). Reports suggest EP is more common in MGMT unmethylated (uMGMT) cases. While multiple advanced imaging studies have tried to address this issue (including magnetic resonance with perfusion [MRP], with spectroscopy [MRS], and FDG-PET), no non-invasive test reliably differentiates EP from PsP, yet the distinction is critical for subsequent management. We aimed to develop a multiparametric score including advanced imaging and MGMT status to improve interpretation.

METHODS
We retrospectively reviewed records of patients diagnosed with GBM from 2016-2022 who had changes on anatomic imaging concerning for EP vs. PsP ≤6 months after chemoradiotherapy and underwent ≥ 2 advanced imaging sequences (MRP, MRS, FDG-PET); definitive distinction between EP and PsP was made by either histology from surgical resampling or subsequent clinico-radiographic evolution. Associations between clinical factors and EP were investigated with logistic regression. Sensitivity and specificity of each imaging modality and of a composite score (1 point each for MRP consistent with EP, MRS consistent with EP, and uMGMT) were calculated.

RESULTS
46 evaluable patients (57% male; median age 58.5 years, range 31-80) were included; outcome (EP in 29 [63%], PsP in 17 [37%]) was determined histologically and clinically in 15 (33%) and 31 (67%) of cases, respectively. In multivariate analysis, uMGMT was predictive of EP (OR=2.63, 95% CI=1.29-37.55, p&lt; 0.05). Sensitivity/specificity to predict EP were 78%/77% for uMGMT, 76%/29% for MRP, 85%/67% for MRS, 86%/0% for FDG-PET (8 patients) and 88%/75% for composite score of 2-3 (vs. 0-1).

CONCLUSION
Combining MRS, MRP and MGMT results can improve accuracy in distinguishing EP from PsP in GBM. MGMT status should be incorporated in the interpretation of advanced imaging to distinguish EP from PsP.
NIMG-20. ASSESSMENT OF RESPONSE TO REGORAFENIB IN RECURRENT GLIOMA PATIENTS USING FET PET AND MRI INCLUDING ADC VALUES
Authors: Werner Jan-Michael , Lohmann Philipp , Kabbasch Christoph , Wollring Michael , Tscherpel Caroline , Stoffels Gabriele , Goldbrunner Roland , Langen Karl-Josef , Fink Gereon , Galldiks Norbert 
Published: November 2023

Abstract

INTRODUCTION
Currently, data on O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET and MRI parameters for the evaluation of response to regorafenib in glioma patients remain scarce.

METHODS
Twenty patients (age range, 30-72 years) with recurrent CNS WHO grade 3 or 4 gliomas (glioblastoma, 85%) were treated according to the REGOMA trial. FET PET and MRI were performed at baseline and follow-up after the second cycle. Static FET PET parameters comprised tumor-to-brain ratios (TBR) and metabolic tumor volumes (MTV). Parameters derived from dynamic FET PET acquisition were time-to-peak and slope. MRI response assessment was based on RANO criteria. Additionally, various apparent diffusion coefficients (ADC) parameters were obtained from diffusion-weighted MRI. Thresholds derived from FET PET and ADC parameters were defined using ROC analyses to predict an overall survival (OS) of ≥6 months. The predictive values of FET PET parameters, ADC values, and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates.

RESULTS
Patients received a median of three regorafenib cycles (range, 2-10 cycles). After treatment initiation, the median follow-up was 8.6 months (range, 3.2-27.3 months). After two cycles of regorafenib, a reduction of mean TBR values by ≥10% predicted significantly longer OS (9.9 vs. 5.3 months; P=0.023). Additionally, absolute mean TBR values ≤2.0 at follow-up were prognostic and associated with a significantly longer OS (10.6 vs. 4.5 months; P=0.007). In contrast, MTV, dynamic PET parameters, RANO criteria, and ADC values were not predictive for response or were prognostic (all P &gt;0.05). Multivariate survival analyses revealed that relative mean TBR changes were most potent in predicting response to regorafenib (P=0.038; HR, 0.246) and absolute mean TBR values for prognostication (P&lt; 0.001; HR, 0.005).

CONCLUSION
In contrast to MRI metrics, FET PET parameters are clinically valuable for identifying responders to regorafenib early after treatment initiation and helpful for prognostication.
NIMG-21. SERIAL QUANTITATIVE MRI RADIOMICS SIGNATURE FOR PREDICTION OF SURVIVAL IN GLIOBLASTOMA – A PILOT STUDY
Authors: Chen Cecil , Martin Philip , Koh Eng-Siew , Brighi Caterina , Aly Farhannah , Rai Robba , Holloway Lois , Metcalfe Peter , Pillay Sugendran 
Published: November 2023

Abstract

INTRODUCTION
Radiomics in glioblastoma (GBM) imaging incorporates mathematical and statistical quantification of complex imaging features as surrogate biomarkers of tumour physiology. Delta-radiomics extends this capability by monitoring changes in these features over time. This study aims to evaluate selected radiomic and delta-radiomic features of diffusion-weighted imaging (DWI) as potential early predictors of overall survival (OS) in GBM patients undergoing adjuvant chemo-radiotherapy (aCRT).

METHODS
Imaging and data from n=12 newly diagnosed GBM patients prospectively recruited at Liverpool Hospital, New South Wales, were retrospectively analysed. Patients underwent serial MRI via 3.0T Siemens Magnetom-Skyra scanner, including anatomical and DWI sequences, before aCRT (baseline), and at 3 (FU1) and 6 weeks (FU2) post aCRT commencement. Gross tumour volume (GTV) was manually segmented on contrast-enhanced T1-weighted images. Four first-order histogram radiomic features (mean, skewness, entropy and 10th-percentile) and volume were extracted from the GTV on DWI sequences at each timepoint. Delta-radiomic features were calculated from the difference between measurements at baseline and each follow-up. Linear correlation between OS versus baseline and delta-radiomic features were calculated with Pearson’s coefficient (r). Statistically significant correlations were tested on an external validation dataset of n=8 GBM cases from The Cancer Imaging Archive (ACRIN-FMISO-Brain and ACRIN-DSC-MR-Brain datasets).

RESULTS
A significant positive correlation was found between OS and delta-entropy at FU2 (r = 0.639, p = 0.02). No significant correlation was found between OS and baseline or delta mean, skewness, or 10th percentile (p &gt; 0.05). No significant correlation was found between OS and delta-volume at FU1 (r = 0.134, p = 0.68) or FU2 (r = 0.100, p = 0.76).

CONCLUSION
Our results indicate a potential utility of delta-entropy for prediction of overall survival.

RESULTS
will be validated on an external dataset. Further evaluation of radiomic and delta-radiomic features on a larger sample size is warranted.
NIMG-22. COMPARISON OF NON-INVASIVE, ML-GENERATED WHOLE-BRAIN SPATIAL RADIOPATHOMIC MAPS DERIVED FROM ANATOMICAL AND DIFFUSION-WEIGHTED MRI ON PATIENTS WITH NEWLY DIAGNOSED-GLIOMA
Authors: Adegbite Oluwaseun , Tran Nate , Molinaro Annette , Phillips Joanna , LaViolette Peter , Bobholz Samuel , Ellison Jacob , Li Yan , Luks Tracy , Shai Anny , Nair Devika , Villanueva-Meyer Javier , Connelly Jennifer , Berger Mitchel , Hervey-Jumper Shawn , Aghi Manish , Chang Susan , Lupo Janine 
Published: November 2023

Abstract

INTRODUCTION
Spatial heterogeneity in the glioma microenvironment is difficult to capture through singular biopsy samples. Identification of malignant tumor regions can help guide diagnosis and treatment planning. This study compares radiopathomic maps from 2 strategies derived from anatomical and diffusion-weighted MRI: 1) a support vector machine model trained on an MRI dataset with tissue samples of known spatial coordinates to generate spatial maps of probability of KI-67 and cellularity, and 2) a bagged random-forest model derived from 40+ person glioma brain bank of autopsy tissue coregistered to end-of-life MRI to generate spatial maps of cellularity and tumor probability (TPM). We hypothesize that TPMs from autopsy samples will correlate with KI-67 predictions, while cellularity values among methods will correlate.

METHODS
Spatial maps of tumor pathology from autopsy- and tissue sample-based models were generated on 55 patients newly-diagnosed with glioblastoma that were scanned with anatomical, diffusion-weighted, and metabolic imaging. Mean values were extracted from each map within the T2-hyperintense, non-enhancing lesion (NEL), the contrast-enhancing lesion (CEL), normal-appearing white matter (NAWM), and 5mm diameter spheres centered at the location where tissues were taken. Spearman correlation analysis was used to elucidate the strength of the linear relationship within the selected regions between the two maps.

RESULTS
Correlations between KI-67 and TPM values were highest and most significant at the tissue sample locations and CEL (ρ=0.306, p=0.002 and ρ=0.312, p=0.044 respectively). Cellularity maps generated from the autopsy-based model showed no significant correlation with newly diagnosed cellularity values at tissue sample locations, potentially highlighting the influence of treatment.

CONCLUSION
This study examines the current state of ML-generated spatial mapping, highlighting similarities within CEL, but need to account for the effects of subsequent treatment and progression when comparing tumor beyond the CEL between models derived between different time points.