bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-37. “SYNTHETIC” PERFUSION MRI WITH ADJUSTABLE ACQUISITION PARAMETERS IN BRAIN TUMORS USING DYNAMIC SPIN-AND-GRADIENT-ECHO ECHOPLANAR IMAGING
Authors: Sanvito Francesco , Yao Jingwen , Cho Nicholas , Raymond Catalina , Telesca Donatello , Pope Whitney , Everson Richard , Salamon Noriko , Boxerman Jerrold , Cloughesy Timothy , Ellingson Benjamin 
Published: November 2024

Abstract

INTRODUCTION
Dynamic susceptibility contrast (DSC) perfusion MRI can assess normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) in brain tumors, which can aid in differential diagnosis, molecular profiling, and identification of disease progression. However, CBV-optimized and PSR-optimized protocols differ in terms of acquisition parameters. Additionally, DSC dependency on acquisition parameters limits the universalizability of diagnostic cutoffs for nrCBV and PSR across institutions and scanners. This study aims to generate “synthetic” DSC datasets with adjustable synthetic acquisition parameters using dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI) to: 1) simultaneously generate nrCBV and PSR with optimal acquisition parameters, 2) compare DSC datasets with heterogeneous external cohorts.

METHODS
Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection. Multiple synthetic DSC datasets with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo gradient-echo (GE) data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. For each patient, synthetic protocols were set to match guideline-compliant CBV-optimized protocols, PSR-optimized protocols, and protocols used in external cohorts from the literature, for comparison.

RESULTS
Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection. Conversely, suboptimal PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman’s ρ=0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p&lt;0.0001), supporting a role of synthetic DSC for multicenter comparisons.

CONCLUSIONS
Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with a single injection, facilitating use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion parameters across heterogeneous multicenter datasets.
NIMG-41. MULTI-SITE, PROSPECTIVE TRIAL EVALUATING FET-PET IN GLIOBLASTOMA (FIG) STUDY (TROG 18.06): PRELIMINARY RESULTS OF CENTRAL NUCLEAR MEDICINE REVIEW OF FET-PET BIOLOGIC TARGET VOLUME DELINEATION FOR RADIATION PLANNING
Authors: Koh Eng-Siew , Francis Roslyn , Lee SzeTing , Lau Eddie , Thomas Elizabeth , Whitehead Angela , Cook Olivia , Dykyj Rachael , Moore Alisha , Rossi Alana , Thomas Paul , Pattison David , Akhurst Tim , Alipour Ramin , Hsiao Edward , Schembri Geoffrey , Lin Peter , Yap June , Ly Tam , Kirkwood Ian , Vallat Wilson , Krishna Dayanethee Krishna , Khan Shahroz Khan , Ngai Stanley Ngai , Yu Chris , Beuzeville Scott , Yeow Tow Chan , Barry Nathaniel , Ebert Martin , Gan Hui , Moffat Bradford , Fitt Greg , Ng Sweet Ping , Pinkham Mark , Bailey Dale , Scott Andrew M 
Published: November 2024

Abstract

BACKGROUND
To provide an update from the multi-site trial evaluating O-(2-[18F]-fluoroethyl)-L-tyrosine Positron Emission Tomography (FET-PET) in Glioblastoma (FIG) study and 2) assess the impact of central Nuclear Medicine physician (NMP) review of prospective FET-PET1 delineation of the biological target volume (BTV) for radiotherapy (RT) planning. Material and methods Up to 210 adult GBM participants across 11 Australian sites will undergo FET-PET post-surgery/pre-chemo-RT [CRT] (FET-PET1), one month post CRT (FET-PET2) and at suspected progression (FET-PET3). Group 1 participants enter at timepoint 1 (FET-PET1 with MRI1), with Group 2 entering at timepoint 2. Adjuvant RT target volumes are derived per standard contrast MRI with hybrid post-hoc RT volumes then incorporating the FET-PET1 NM-derived BTV utilising MiM version 7.0. All trial sites and NMP have passed credentialling which included three benchmarking cases involving BTV delineation.

RESULTS
Trial recruitment commenced in January 2021, with 189 (n=126 Group 1 and n=63 Group 2) participants enrolled to date, with a target of 140 Group 1 participants. During trial credentialling, results demonstrated variations in FET-PET1-derived BTV in 25/72 (34.7%) - 13 minor and 12 major.Currently, 42 of 112 participant FET-PET1 with BTV delineation cases across 10 sites have undergone central review, with 7/42 (16.7%) requiring resubmission. Reasons for resubmission/protocol deviation included incorrect imaging sequence selection within the MiM workflow (n=3), Static GTV overcontouring (n=3) and dynamic volume of interest change in size/position during MiM workflow use (n=1).

CONCLUSION
The FIG trial will complete recruitment in 2024 with analyses planned at one year post CRT completion. Despite improvements in resubmission rates compared to the credentialling phase, central review of prospective FET-PET1-derived BTV delineation remains important in ensuring protocol adherence. The FIG study is the largest prospective multi-site study of its kind addressing FET-PET’s impact on adjuvant radiation planning and its role in management of pseudoprogression and prognostication.
NIMG-47. MULTI-INSTITUTIONAL VALIDATION OF AN AI-BASED MODEL FOR PREDICTION OF TUMOR INFILTRATION AND FUTURE RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM
Authors: Mohan Suyash , Garcia Jose , Akbari Hamed , Kwak Sunwoo , Baik Kyunglok , Shalaby Mahmoud , Matsumoto Yuji , Bakas Spyridon , Kamel Peter , Tippareddy Charit , Badve Chaitra , Lee Matthew , Barnholtz-Sloan Jill , Sloan Andrew , Chakravarti Arnab , Palmer Joshua , Dicker Adam , Flanders Adam , Shi Wenyin , Jain Rajan , LaMontagne Pamela , Marcus Daniel , Sotiras Aris , Balana Carmen , Capellades Jaume , Puig Josep , Cepeda Santiago , Stefano Anna Luisa Di , Wiestler Benedikt , Woodworth Graeme , ORourke Donald , Nasrallah MacLean , Davatzikos Christos 
Published: November 2024

Abstract

BACKGROUND
Glioblastoma is an infiltrative primary brain tumor with poor prognosis despite multimodal therapy. Recurrence is inevitable secondary to tumor cell infiltration in the peritumoral tissues, beyond contrast enhancing margins, which is the target for surgical resection. We hypothesize that a machine learning model constructed from a diverse, inter-institutional dataset can improve accuracy of generated tumor infiltration maps, thus guiding precision targeted therapies.

METHODS
731 MRI scans of treatment-naïve glioblastoma patients from 10 institutions were included. All patients had pre-operative multiparametric-MRI (T1, T1Gd, T2, T2-FLAIR, ADC), and underwent complete resection of the enhancing tumor followed by standard-of-care chemoradiotherapy. 42 patients were used as an independent validation set, and 689 were used for training. Of these 239 patients had histopathologically confirmed recurrence with corresponding MRI scans, which were used as ground-truth for evaluating the location of recurrence using a leave-one-site-out (LSO) method. An AI model combining deep learning and SVM was used to develop a predictive model for infiltration. We validated the generalizability of our results in an unseen, multi-institutional data set.

RESULTS
Our model predicted locations of recurrence with odds ratio (99% CI) 37.6 (37.1-38.1) on the LSO testing set and 24.3 (23.4-25.2) on the validation set, indicating that areas labeled highly infiltrated were over 37 and 24 times more likely to coincide with future recurrence respectively.

CONCLUSIONS
We demonstrate that AI-based pattern analysis from multiparametric-MRI can predict tumor infiltration in peritumoral regions with high likelihood of recurrence by decrypting the visually imperceptible heterogeneity of peritumoral tissue. Model performance improved from training on a larger/diverse dataset and combining results of multiple AI methods. Independent validation confirmed the model’s ability to generalize to unseen data. We believe this will serve to advance AI-based biomarkers for predicting future recurrence and facilitate development of multi-modal targeted therapies in this era of precision neuro-oncology.
NIMG-48. MEASURABLE DISEASE AS BASELINE CRITERION FOR RESPONSE ASSESSMENT IN GLIOBLASTOMA TRIALS – A COMPARATIVE STUDY OF MRI-BASED (RANO 2.0) VERSUS PET-BASED (PET RANO 1.0) ASSESSMENT
Authors: Johanna Müller Katharina , Forbrig Robert , Reis Jonas , Wiegand Lilian , Barci Enio , Kunte Sophie , Kaiser Lena , Schönecker Stephan , Schichor Christian , Harter Patrick , Thon Niklas , von Baumgarten Louisa , Preusser Matthias , Albert Nathalie Lisa 
Published: November 2024

Abstract
Response assessment in glioblastoma relies on measuring contrast enhancement on MRI according to RANO 2.0 criteria, and ‘measurable disease’ often serves as inclusion criterion for clinical trials. Recently, criteria based on amino acid PET have been proposed for response assessment in clinical trials (PET RANO 1.0). Here, we compare ‘measurable disease’ as defined by PET RANO 1.0 with the standard MRI-based RANO 2.0 criteria in glioblastoma patients. In this retrospective single-center study, we identified patients with newly diagnosed IDH-wildtype glioblastoma who underwent [18F]FET PET and MRI after resection or biopsy and before chemoradiotherapy. Two independent investigators analyzed ‘measurable disease’ according to PET RANO 1.0 versus MRI-based RANO 2.0 criteria. Additionally, lesion size and uptake intensity, including maximal and mean target-to-background ratio, were assessed. We evaluated 136 patients with 76 cases after biopsy and 60 after microsurgical resection. Using RANO 2.0, 35/76 patients (46.1%) had measurable disease in the biopsy group, and 25/60 (41.6%) in the resected group (median sum of maximum cross-sectional diameters: 39.7mm and 28.0mm, respectively). Utilizing PET RANO 1.0, a significantly higher proportion (overall 123/136, 90.4%; 66/76 (86.8%) after biopsy; 57/60 (95.0%) after resection) had measurable disease (p&lt;0.001). None of the 10 patients in the biopsy group nor of the 3 patients in the resected group without measurable disease on PET had measurable disease on MRI. Contrariwise, 32/41 patients (73.2%) in the biopsy group and 30/35 (85.7%) in the resected group without measurable disease on MRI exhibited measurable disease on PET. PET RANO 1.0 identifies a significantly higher number of patients with ‘measurable disease’ compared to conventional MRI-based criteria in the perioperative period of glioblastoma. PET-based assessment may thus serve as a novel baseline parameter for evaluating treatment response in glioblastoma trials, that may allow the enrollment of larger patient populations. Further validation in prospective trials is warranted.
NIMG-51. PROSPECTIVE CASE CONTROL STUDY OF HYPOXIA FMISO PET/MRI AS A DIAGNOSTIC FEATURE OF GLIOBLASTOMA IMMUNOTHERAPY FAILURE
Authors: Barajas Jr Ramon , Mallak Nadine , Choudhary Gagandeep , Szidonya Laszlo , Stoller Jared , Thurston Brian , Raslan Ahmed , Dogan Aclan , Randy Woltjer , Wood Matthew , Sanusi Olabisi , Walker Josh , Rooney Bill , Fu Rochelle , Muldoon Leslie , Holland Samantha , Neuwelt Edward , Nehmeh Sadek , Ivanidze Jana , Ambady Prakash 
Published: November 2024

Abstract

BACKGROUND
Hypoxia is a prognostic biological feature of glioblastoma. 18F-fluoromisonidazole (FMISO) PET quantifies tumor hypoxia. Efficacy of checkpoint immunotherapy is being investigated, but specific response assessment criteria remain unknown. We hypothesized that tumor hypoxia is a defining feature of immunotherapy failure in patients with glioblastoma.

METHODS
A prospective case control study was undertaken in patients with newly diagnosed IDH wildtype glioblastoma (NCT03649880). Cases were concurrently enrolled in a phase II study (NCT03347617) of pembrolizumab immunotherapy (200 mg IV every 3 weeks) combined with Stupp protocol temozolomide-based chemoradiotherapy following maximal safe resection. Controls did not receive pembrolizumab. FMISO PET/MRI (3.7 MBq/kg, 90 min uptake) was performed at time of mRANO presumed progression. Hypoxic volume (HV) was defined by pixels with FMISO uptake ≥1.2x that of mean cerebellar uptake. MRI-Gd enhancing volume was segmented using a semi-automated approach. Hypoxic fraction (HF) was defined as the HV-to-MRI enhancing volume ratio. Disease progression or pseudoprogression (Psp) was confirmed through surgery or follow-up MRI. T-test and receiver operator characteristic (ROC) analysis assessed differences and diagnostic performance.

RESULTS
18 patients (14 cases [11 progression and 3 Psp] and 4 controls [3 progression and 1 Psp]) provided evaluable disease. For all patients, HF was significantly elevated with progression (1.44 ± 0.8; N=14) compared to Psp (0.31 ± 0.3; N= 4; P=0.01). HV was not different (P=0.10). No difference in HV or HF was observed between Cases and Controls (P&gt; 0.42). Use of HF yielded an AUROC of 0.96 with an optimal cut off of 0.74, to differentiate progression from Psp.

CONCLUSION
At mRANO presumed progression HF with FMISO PET/MRI accurately distinguishes tumor progression from neuroinflammatory Psp, irrespective of prior pembrolizumab immunotherapy. The incorporation of FMISO PET/MRI in larger prospective therapeutic clinical trials in patients with glioblastoma is underway and likely to improve response assessment criteria.
NIMG-60. SUBTYPING GLIOBLASTOMA FROM JOINT CLUSTERING OF IMAGING AND GENOMIC DATA
Authors: Guo Jun , Fathi-Kazerooni Anahita , Toorens Erik , Akbari Hamed , Yu Fanyang , Matsumoto Yuji , Sako Chiharu , Mamourian Elizabeth , Shinohara Russell , Koumenis Constantinos , Verginadis Ioannis , Bagley Stephen , Morrissette Jennifer , Binder Zev , Brem Steven , Mohan Suyash , Lustig Robert , O’Rourke Donald , Ganguly Tapan , Bakas Spyridon , Aboian Mariam , Nasrallah MacLean , Davatzikos Christos 
Published: November 2024

Abstract

PURPOSE
To comprehensively unravel the heterogeneity of glioblastoma, we developed a subtyping method using integrated imaging and genomic data.

METHODS
We assembled a retrospective cohort of 571 IDH-wildtype glioblastoma patients, obtaining pre-operative multi-parametric MRI (T1, T1-GD, T2, T2-FLAIR, DSC, DTI) scans (available for 462 patients) and targeted next-generation sequencing (NGS) data (available for 355 patients). We extracted 971 radiomic features from these MRI scans, selecting 12 significant dimensions through L21-norm minimization guided by 13 key driver genes from the five most frequently altered pathways in glioblastoma compared to healthy controls (RB1, P53, MAPK, PI3K, and RTK). Subtypes were identified using a joint clustering approach that integrated radiomics and genomics.

RESULTS
Our method identified three glioblastoma subtypes with distinct risk levels: high-risk (subtype 1), medium-risk (subtype 2), and low-risk (subtype 3), each characterized by their differential overall survival outcomes (Kaplan-Meier analysis, p &lt; 0.05). The intensities of axial diffusivity in enhancing tumor regions and radial diffusivity in non-enhancing cores increased across three subtypes. All subtypes shared a common tendency for more frequent mutation co-occurrence pattern in [TP53,RB1] but exhibited unique molecular characteristics. For example, subtype 1 displayed a relatively high frequency of co-occurring mutations in [TP53,KDR] and [NOTCH2,MDM4]; subtype 2 had six co-occurring pairs [TP53,KDR], [PTPN11,NF1], [RB1,FUBP1], [PTEN,CIC], [PTEN,KRAS], and [NOTCH2,CDKN2A]; subtype 3 showed six co-occurring pairs [PTPN11,NF1], [PDGFRA,NF1], [NTRK1,NOTCH2], [MET,ATRX], [KRAS,KDR], and [FGFR2,DNMT3A] plus four significant patterns of mutual exclusivity in [TP53,PTPN11], [TP53,EGFR], [EGFR,RB1], and [EGFR,NF1].

CONCLUSION
Our study discovered distinct glioblastoma subtypes, revealing complex patterns and structures across multiple data modalities without relying on prior clinical assumptions. This is promising to help enhance the precision of diagnosis and treatment of glioblastoma.
NIMG-71. T2 RELAXIVITY AND DIFFUSION TENSOR METRICS IN THE PREDICTION OF PROGRESSION-FREE SURVIVAL IN NON-RECURRENT HIGH GRADE GLIOMA
Authors: Rulseh Aaron , Zajickova Heidrun , Vymazal Josef 
Published: November 2024

Abstract
High grade glioma (HGG) is the most common primary brain tumour; we aimed to evaluate T2 relaxivity and diffusion tensor imaging (DTI) metrics in the prediction of progression-free survival. Seventy-two non-recurrent HGG subjects (IDH: MUT=10, Unknown=20) followed at our institution after surgery and combined chemo-radiotherapy were included. All data used were prior to progression as per RANO criteria; only the first 2 years of follow up was included in the analyses. T2 relaxation rates (1/T2) were calculated voxel-wise assuming mono-exponential decay. Diffusion data were corrected from two opposite phase encode acquisitions and DTI metrics evaluated included FA, MD, p, q, and L1. Whole- and half-brain (ipsilateral/contralateral) masks were generated by brain extraction and registration; data extracted from these masks were analyzed using python 3.8 with the exception of numerous histogram features extracted using fsl tools. Radiomics features were also extracted using the pyradiomics package. The lofo-importance package was used for feature selection. A multi-level approach was used to reduce the initial 32,000 MRI features to a final number of 443; from these, the 50 highest-ranked features were used further. Within-subject feature engineering was then performed including 1–4 step forward and backward lag, lag difference, as well as area, maximum, mean and cumulative sum. Non-MRI features included available demographic, histological and treatment-related data. A LightGBM classification model was used for the prediction of progression within 90 days; the data were grouped by subject, such that each subject was present only in training or testing datasets. Hyperparameters were tuned using randomized search cross validation, and the final model using 5-fold cross validation reached a mean ROC-AUC of 0.89. Our results show the potential of quantitative MRI features derived from qualitatively unremarkable images within the progress-free interval to identify subjects at risk of imminent progression.
NIMG-85. RADIATION DOSE IS ASSOCIATED WITH CHANGE IN MAGNETIC RESONANCE FINGERPRINTING PROPERTIES OF ANATOMICALLY DISTINCT REGIONS OF INTEREST IN GLIOBLASTOMA MULTIFORME PATIENTS
Authors: Lee Joan , Arsenault Theodore , Gongala Sree , Hodges Tiffany , Ma Dan , Newton Herbert , Badve Chaitra , Vempati Prashant 
Published: November 2024

Abstract
Magnetic Resonance Fingerprinting (MRF) is an investigational imaging technique that provides spatially-resolved quantification of tissue properties, in contrast to qualitatively weighted conventional MR imaging. Intentional sequential variation of acquisition parameters throughout the MRF acquisition produces a unique quantitative signal timecourse for each tissue, which generates a spatial “fingerprint” for individual voxels. This study applies MRF to quantitatively correlate radiation dose with MR signal intensity changes, specifically T1, T2, and M0 maps across regions of interest (ROIs) that are involved in cognitive speed and processing. Three patients who underwent radiation therapy for resected glioblastoma multiforme participated in this study. MRF scans were conducted at pre-radiotherapy (pre-RT), mid-radiotherapy (mid-RT), and post-radiotherapy (post-RT) timepoints. ROIs (bilateral hippocampi, amygdalae, dorsolateral prefrontal cortices, and corpus callosum) were contoured and radiation dose to the ROI contours at mid-RT and post-RT timepoints were obtained using MIM treatment planning software (MIM Software, Beachwood, OH). Spearman’s rank correlation test was conducted in MATLAB (MATLAB R2024a, Natick, MA) for mean radiation dose to ROIs against MRF T1 relaxation time, T2 relaxation time, and M0 proton density. Mean radiation dose exhibited statistically significant correlation with absolute changes from pre-RT to post-RT T1 relaxation time, T2 relaxation time, and M0 proton density values across the ROIs. Mid-RT scans showed intermediate changes, suggesting a progressive alteration in tissue characteristics with accumulating radiation dose. The hippocampus and amygdala received lower radiation doses than other ROIs and exhibited less pronounced changes in T1, T2, and M0 values. These results demonstrate a novel application of MRF in quantitative characterization of imaging changes in anatomically distinct ROIs. The correlation between radiation dose and absolute changes in MRF signal values from pre-RT to post-RT MRF scans reflect progressive changes as a result of radiation exposure.
NIMG-88. EVALUATION OF DIFFUSION AND PERFUSION MRI FEATURES FOR DIFFERENTIATING RECURRENT BRAIN METASTASES AND RADIATION NECROSIS
Authors: Shao Guowen , Sanvito Francesco , Kim Won , Ellingson Benjamin , Yao Jingwen 
Published: November 2024

Abstract

BACKGROUND
Brain metastases (BM) are a frequent and serious complication of cancer. Differentiating between BM and radiation-induced necrosis (RN) after radiotherapy remains challenging. Diffusion-weighted imaging (DWI) derived apparent diffusion coefficient (ADC) maps and perfusion-weighted imaging (PWI) derived relative cerebral blood volume (rCBV) have the potential to distinguish between BM and RN. Despite the potential of these imaging modalities, previous studies have been limited by the absence of histopathological confirmation. In this study, we utilized a patient cohort that underwent stereotactic biopsy as part of their Laser Interstitial Thermal Therapy (LITT) following suspicious radiographic progression. This allowed us to evaluate the efficacy of ADC and rCBV imaging features with histopathological validation.

METHODS
In this study, 29 patients underwent MRI scans (3 with 1.5T, 26 with 3T) prior to LITT. Pathology confirmed 11 patients with BM and 18 with RN. Regions of interest (ROIs) in the contrast-enhanced lesion and T2 hyperintensity lesion were identified using post-contrast T1-weighted images and fluid-attenuated inversion recovery (FLAIR) images. Mean values and histogram-based features (standard deviation, 10%, 90%) of lesion volume, ADC, and rCBV within tumor ROIs were compared between the BM and RN using the Wilcoxon rank-sum test.

RESULTS
Our findings indicated that the mean values of ADC in the enhancing and non-enhancing lesion ROIs did not significantly differ between BM and RN patients. However, in the contrast-enhanced lesion regions, we observed a significant difference in the standard deviation of rCBV (p&lt;0.05), and the 90th percentile value of rCBV also suggested potential differences between BM and RN (p=0.069). In the T2 hyperintensity lesion regions, lesion volume (p&lt;0.05) and the mean of rCBV (p&lt;0.05) show significant differences between BM and RN.

CONCLUSION
This study demonstrates the potential of using rCBV to aid in the clinical diagnosis of BM. Additionally, our findings indicate that T2 hyperintensity regions, beyond just contrast-enhancing lesions, may provide valuable information for differential diagnosis.