bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-23. AI-BASED CONNECTED COMPONENT MARKERS OF BRAIN PERITUMORAL MICROENVIRONMENT USING WATER RESTRICTION INFORMATION
Authors: Samani Zahra Riahi , Parker Drew , Akbari Hamed , Wolf Ronald L , Brem Steven , Bakas Spyridon , Verma Ragini 
Published: November 2022

Abstract

PURPOSE
Glioblastoma is the most aggressive adult brain tumor, with heterogeneous neoplastic cell peritumoral infiltration. Characterization of this infiltrative peritumoral heterogeneity is an unmet clinical need that could contribute to strengthening our understanding of this disease. We propose novel AI-based markers of infiltration using deep-learning (DL) based on water restriction caused by infiltration, identified by diffusion tensor imaging (DTI). These markers could contribute to precision/personalized medicine, towards influencing clinical decision-making, including planning for biopsies, surgery, and radiation. METHOD: We automatically extracted peritumoral patches from free water volume fraction maps (FW-VF) of a retrospective cohort of 44 brain metastases and 66 glioblastomata patients and labelled them as high- and low- free water, respectively. An AI/DL model was then trained on these patches to distinguish differences in water restriction. Our trained AI/DL model was then applied on FW-VF of 264 hold-out glioblastoma patients (survival:0.43-76.9 months, age:21-88, 104 females) to generate a peritumoral microenvironment index (PMI) map quantifying infiltrative heterogeneity. Connected components (CCs) of high PMI values were calculated and their descriptive characteristics of size, number, shape, directionality, and spatial location, were extracted as AI-based markers. Gaussian mixture model clustering was then applied on these markers to determine if their representative infiltrative peritumoral heterogeneity can capture overall survival differences, by partitioning the patients into three clusters: low, moderate, and high risk.

RESULTS
The log-rank test yielded significant differences (p&lt; 10-5) between low- and high-risk patients, (HR= 0.47, 95% CI:0.34-0.65; P&lt; 0.005). Average PMI values were significantly greater in high-risk patients (P&lt; 0.05).

CONCLUSION
We introduced novel AI-based markers of infiltration in the peritumoral microenvironment, using information of water restriction extracted from DTI. Our proposed markers can capture overall survival differences, based on the patterns of infiltration using DTI-based characterization of the water restriction, that show promise as clinically relevant prognostic biomarkers.
NIMG-28. USE OF NEUROIMAGING TECHNIQUES IN GLIOMA PATIENTS – RESULTS OF AN INTERNATIONAL SURVEY ON BEHALF OF THE EORTC BRAIN TUMOR GROUP
Authors: Lohmann Philipp , Smits Marion , Razis Evangelia , Kocher Martin , Langen Karl-Josef , de Vos Filip , Bendzsus Martin , Franceschi Enrico , Grosu Anca-Ligia , Compter Inge , Galanaud Damien , de Larraya Jaime Gallego Pérez , Gempt Jens , Hau Peter , Andratschke Nicolaus , Tonn Joerg-Christian , Zadeh Gelareh , Weller Michael , Preusser Matthias , Galldiks Norbert 
Published: November 2022

Abstract

BACKGROUND
Multimodal imaging offers the potential to provide valuable diagnostic information in brain tumor patients. Considering the increasing number and availability of advanced neuroimaging techniques, selecting, and applying the best modality may be therefore difficult. The present survey was carried out to evaluate the preferred use of various neuroimaging applications in patients with glioma.

METHODS
An online survey with 31 questions was distributed to 262 centers associated with the EORTC located in 34 countries. Subsequently, this survey was promoted by EANO and SNO via social media and newsletters.

RESULTS
A total of 77 responses, predominantly from radiation oncologists (36%) and neurooncologists (31%), were evaluated. Most responses came from university hospitals and research institutions (68%), mainly from Europe (89%). Almost half of these centers (48%) examined more than 100 glioma patients per year. All institutions had access to MRI, 94% to CT, 67% to PET or PET/CT, and 27% to hybrid PET/MRI. A total of 56% of institutions used RANO criteria for the evaluation of imaging findings. In addition to structural MRI, most institutions routinely performed advanced MRI, followed by CT, PET, and hybrid PET/CT (74%, 42%, 32%, and 23%, respectively). Regarding PET, 64% of centers used the amino acid tracer O-(2-[18F]fluoroethyl)-L-tyrosine, followed by 2-[18F]-fluoro-2-deoxy-D-glucose (45%). Twenty-five percent of centers performed intraoperative MRI, and 10% intraoperative CT. In addition to structural MRI, the preferred additional imaging methods were perfusion-weighted MRI, diffusion-weighted MRI, amino acid PET, and proton MR spectroscopy (83%, 75%, 60% and 56%, respectively).

CONCLUSION
The results of this international survey provide insights into the use of neuroimaging techniques in neuro-oncology centers. The availability, use, and assessment of neuroimaging in glioma patients varies from country to country. Our results highlight the importance of global activities towards further standardization of neuroimaging in brain tumor patients, such as the RANO working groups.
NIMG-31. CLINICALLY RELIABLE DEEP LEARNING MODEL FOR DIFFERENTIATION OF GLIOBLASTOMA FROM SINGLE BRAIN METASTASIS: ESTIMATING UNCERTAINTY WITH DEEP ENSEMBLES
Authors: Park Yae Won , Ahn Sung Soo , Lee Seung-Koo 
Published: November 2022

Abstract

OBJECTIVES
To develop a clinically reliable deep learning model to differentiate glioblastoma (GBM) from solitary brain metastasis by providing predictive uncertainty estimates.

METHODS
A total of 465 patients (300 GBM, 165 single brain metastasis) were enrolled in the institutional training set. Deep ensembles model based on Densenet121 (2D CNN) were trained on multiparametric MRI. The models were validated in the external validation set consisting of 143 patients (101 GBM, 42 single brain metastasis). The classification performance was estimated, and entropy values for each input were evaluated for uncertainty measurement. Based on entropy values, the institutional training and external validation set were split to high and low uncertainty groups. To evaluate uncertainty on out-of-distribution data from unseen classes, 319 patients with meningiomas (from two external institutions) were separately tested in the deep ensemble model.

RESULTS
Deep ensembles showed an AUC, accuracy, sensitivity, and specificity of 0.82, 78.3%, 54.8%, and 88.1% on external validation in differentiating GBM from solitary brain metastasis. Deep ensembles showed higher performance in the low uncertainty group (AUC, accuracy, sensitivity, and specificity of 0.78, 89.0%, 62.5%, and 94.2%, respectively) than the high uncertainty group (AUC, accuracy, sensitivity, and specificity of 0.64, 65.5%, 51.4%, and 77.1%) according to entropy values on external validation. On out-of-distribution dataset of meningiomas, 292 (91.4%) patients were classified as high uncertainty group by deep ensembles whereas only 202 (63.4%) patients were classified into high uncertainty group by the single DenseNet121.

CONCLUSIONS
Not only can deep ensembles provide predictive uncertainty in differentiating GBM from single brain metastasis, but also avoid overconfident predictions in unseen classes and consequently offer reliable clinical decision making.
NIMG-38. 2-HYDROXYGLUTARATE MAGNETIC RESONANCE SPECTROSCOPY IN ADULT BRAINSTEM GLIOMA PATIENTS
Authors: Iwahashi Hirofumi , Nagashima Hiroaki , Tanaka Kazuhiro , Uno Takiko , Hashiguchi Mitsuru , Maeyama Masahiro , Somiya Yuichiro , Komatsu Masato , Hirose Takanori , Itoh Tomoo , Sasayama Takashi 
Published: November 2022

Abstract

INTRODUCTION
The onco-metabolite, 2-hydroxyglutarate (2HG), is non-invasive biomarker detected by magnetic resonance spectroscopy (MRS), indicating isocitrate dehydrogenase (IDH) mutant glioma. Especially in patients with brainstem lesions where biopsies are at high risk of causing neurological deficit, 2HG-MRS may be useful. Here, we examined the utility of 2HG-MRS for diagnosing IDH mutant adult diffuse brainstem glioma and the ability of prognosis prediction.

METHODS
Three tesla -MRS (3T-MRS) was performed on patients with radiographically confirmed brainstem tumor. A single-voxel was set in the lesion with reference to the T2 / FLAIR image, and analyzed according to the 2HG-tailored MRS protocol (Philips, Achieva, PRESS, TE 35 ms). All patients underwent biopsy using an intraoperative navigation system (Brain LABTM) or stereotactic biopsy system (Komai’s CT-stereotactic frame) before initial treatment. IDH and H3K27M mutation status were confirmed by immunohistochemistry and DNA sequence. We examined the relationship between 2HG values and patient survival period.

RESULTS
Ten patients (7 male and 3 female, median age 33.5) underwent 2HG-MRS and biopsy. Four had H3K27M mutation and 4 had IDH mutation (1 R132H, 2 R132S, and 1 R132G). Two had neither H3K27M nor IDH mutation. H3-K27 and IDH mutations were mutually exclusive. All tumor located at pons mainly. There was no significant radiological difference between H3K27M and IDH mutant in conventional MRI sequence. Pearson's chi-square test demonstrated that 2HG concentrations &gt; 1.5 mM were 100% in sensitivity and 83.3% in specificity for IDH mutant glioma (p = 0.0098). The median overall survival was 130.5 months in IDH mutant glioma (n=4) and 25 months in IDH wild-type glioma (n=6), respectively.

CONCLUSIONS
3T-MRS was able to detect 2HG in adult brainstem glioma. A noninvasive 2HG-MRS study may be helpful in the diagnosis and predicting prognosis of adult diffuse brainstem glioma patient.
NIMG-40. RAPID EARLY PROGRESSION (REP) OF GLIOBLASTOMA IS AN INDEPENDENT NEGATIVE PROGNOSTIC FACTOR: RESULTS FROM A SYSTEMATIC REVIEW AND META-ANALYSIS
Authors: Waqar Mueez , Roncaroli Federico , Lehrer Eric , Palmer Joshua , Villanueva-Meyer Javier , Braunstein Steve , Hall Emma , Aznar Marianne , De Hamer Philip Witt , D'Urso Pietro , Trifiletti Daniel , Quiñones-Hinojosa Alfredo , Wesseling Pieter , Borst Gerben 
Published: November 2022

Abstract

INTRODUCTION
In patients with newly-diagnosed glioblastoma, rapid early progression (REP) refers to tumour regrowth between surgery and postoperative chemoradiotherapy. This systematic review and meta-analysis appraised published data on REP to better characterise and understand it.

METHODS
Systematic searches of MEDLINE, EMBASE and the Cochrane database from inception to 21/10/21. Studies describing the incidence of REP – tumour growth between the postoperative MRI scan and pre-radiotherapy MRI scan in newly-diagnosed glioblastoma, were included. The primary outcome was REP incidence.

RESULTS
From 1590 search results, 9 studies were included with 716 patients. The median age was 56.9 years (IQR 54.0-58.8 years). There was a male predominance with a median male-to-female ratio of 1.4 (IQR 1.1-1.5). The median number of days between MRI scans was 34 days (IQR 18-45 days). The mean incidence rate of REP was 45.9% (range 19.3%-72.0%) and significantly lower in studies employing functional imaging to define REP (p&lt; 0.001). REP/non-REP groups were comparable with respect to age (p=0.99), gender (p=0.33) and time between scans (p=0.81). REP was associated with shortened overall survival (HR 1.78, 95% CI 1.30-2.43, p&lt; 0.001), shortened progression-free survival (HR 1.78, 95% CI 1.30-2.43, p&lt; 0.001), subtotal resection (OR 6.96, 95% CI 4-51-10.73, p&lt; 0.001) and IDH wildtype versus mutant tumours (OR 0.20, 95% CI 0.02-0.38, p=0.03). MGMT promoter methylation was not associated with REP (OR 1.29, 95% CI 0.72-2.28, p=0.39).

CONCLUSIONS
REP occurs in almost half of patients with newly-diagnosed glioblastoma and has a strongly negative prognostic effect. Future studies should investigate its biology and effective treatment strategies.
NIMG-41. NON-INVASIVE TRACKING OF T-CELL RECRUITMENT TO THE TUMOR MICROENVIRONMENT IN A MURINE GLIOMA MODEL BY HIGH FIELD CELLULAR MRI
Authors: Hunger Jessica , Schregel Katharina , Fischer Manuel , Agardy Dennis , Sturm Volker , Karimian-Jazi Kianush , Bunse Theresa , Heiland Sabine , Wick Wolfgang , Bendszus Martin , Platten Michael , Breckwoldt Michael 
Published: November 2022

Abstract
Gliomas are characterized by increased T cell exhaustion and poor T cell infiltration into the tumor as well as an overall highly immunosuppressive tumor microenvironment (TME). Response rates in preclinical glioma models and patients to promising new therapeutic approaches in the field of immunotherapies - such as checkpoint blockade, vaccines and adoptive therapy with chimeric antigen receptor (CAR) or T cell receptor (TCR)-transgenic T cells - remain heterogeneous. This demonstrates the need for non-invasive tracking of T cell recruitment to the TME in order to monitor T cell activating immunotherapies, adapt therapeutic strategies and predict treatment outcome. Iron oxide nanoparticles (NP) can be visualized non-invasively by magnetic resonance imaging (MRI) and dedicated MRI sequences such as T2* mapping. Using isolated murine T cells cultures we show that labeling of T cells with iron oxide NP as contrast agent is feasible and does not impact T cell viability and functionality as assessed by cytokine secretion and antigen-specific killing activity in vitro. We demonstrate that adoptively transferred T cells can be visualized intratumorally in a murine glioma model by high field MRI at 9.4 Tesla with high sensitivity and that T cells can be tracked non-invasively in a time course of over one week. Ongoing work assesses preclinical efficacy of adoptive T cell therapy targeting well characterized model antigens expressed in experimental gliomas using longitudinal MRI to visualize spatial and temporal T-cell dynamics in the TME. Correlative methods include immunohistochemistry, flow cytometry, tissue clearing and ultramicroscopy. We hypothesize that T cell distribution and numbers may predict therapeutic efficacy and correlate with treatment outcome in experimental gliomas.
NIMG-44. DSC-MRI FRACTIONAL TUMOR BURDEN VOLUME PREDICTS OVERALL SURVIVAL IN UNMETHYLATED NEWLY DIAGNOSED HIGH GRADE GLIOMA
Authors: Armstrong Stephanie , Prah Melissa , Connelly Jennifer , Krucoff Max , Mueller Wade , Schmainda Kathleen 
Published: November 2022

Abstract

OBJECTIVE
Image maps of Fractional Tumor Burden (FTB), derived from dynamic susceptibility contrast (DSC) perfusion MRI, provide clinically meaningful information for glioma treatment management. The goal of this study was to determine if presurgical FTB is predictive of overall survival (OS) in high-grade glioma (HGG) classified according to WHO 2016 criteria.

METHODS
Standardized relative cerebral blood volume (sRCBV) maps were created and co-registered with T1+C images using Horos (Version 4.0.0) and Imaging Biometrics software (Version 21.05) (Elm Grove, WI). Enhancing tumor volumes were determined from calibrated pre/post T1 difference (dT1) maps. Tissue-validated sRCBV thresholds were used to create FTB class maps within enhancing lesion with red regions (sRCBV &gt; 1.556) indicating a high probability of vascular tumor, blue regions (sRCBV&lt; 1.0) indicating avascular (non-tumor) tissue and yellow representing tissue with a lower probability of vascular tumor. FTB volume fractions (FTBv) for all vascular tumor (sRCBV &gt;1.0) or the most vascular tumor (sRCBV &gt; 1.566) were determined. Subjects were separated by MGMT methylation status. GraphPad Prism statistical software (Version 9.3.1) was used for Kaplan Meier survival analysis at 12 and 24 months with patients stratified by median FTBv.

RESULTS
Forty-one subjects (23 males, 18 females) with a median age of 59.45 years were included. Median 12-month and overall survival of unmethylated groups stratified by FTBv (rCBV &gt; 1.0) of 8.33cc was 10.69 and 15.35 months (p=.0453; p=.0317). Unmethylated subjects stratified with FTBv (rCBV &gt; 1.566) of 7.13cc had a median survival of 10.69 and 15.20 with short- and long-term survival distinguished at 12 months (p=0.0033), 24 months (p=0.0002) and overall (p=0.0002). No significant difference in survival was found between the methylated subgroups with FTBv (rCBV &gt; 1.0) or FTBv (rCBV &gt; 1.566) stratified analyses.

CONCLUSION
These results demonstrate that pre-surgical FTBv is predictive of OS in newly diagnosed unmethylated high-grade glioma.
NIMG-47. A MULTIMODAL DEEP LEARNING MODEL FOR PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA PATIENTS
Authors: Faghani Shahriar , Moassefi Mana , Conte Gian Marco , Erickson Bradley 
Published: November 2022

Abstract
GBMs are highly aggressive tumors, evidenced by their rapid growth. Despite maximal treatment, median overall survival ranges between 16 and 20 months. Several studies use patients' diagnosis time(pre-surgical) imaging to predict their survival, but having a model that can predict the days till death for the patients who received the first standard step treatments (Stupp protocol) and now showing secondary progression signs is highly needed. In this study, we aimed to predict the overall survival of Glioblastoma(GBM) patients by developing a multimodal deep learning model using post-operative magnetic resonance imaging(MRI) and patients' age. We included GBM patients who met the following inclusion criteria: having maximum surgical resection followed by concurrent chemoradiation and showing secondary progression signs in follow-up imaging while being on adjuvant temozolomide treatment. We obtained contrast-enhanced T1(CET1) and T2-weighted(T2W) MRIs at progression time and patients' age and death dates. We calculated the days of survival from the day of the MRI. Dataset was split into train, validation, and test set at the patient level. CET1 and T2W MRIs were skull-stripped and coregistered for each patient. To mitigate data imbalance, we used the deep imbalance regression method for training a 3-D Densenet121 model with the concatenated CET1 and T2W as inputs. One hundred features were extracted from the optimized model and concatenated with the normalized age of each patient; the resulting output vectors were passed through the second dense neural network for final prediction. We included 110 patients who met the inclusion criteria. With the overall survival ranging from 16 to 4500 days, a mean of 582.4 and a standard deviation of 694.3, we achieved the test set's root mean square error of 261 days. Further refinement with more data, including clinical data such as comorbidities and external validation, is required prior to adoption in clinical practice.
NIMG-48. MULTI-ECHO SPIN-AND-GRADIENT ECHO (SAGE) PERFUSION MRI TO EVALUATE BRAIN TUMOR MICROSTRUCTURE AND MICROVASCULATURE
Authors: Sanvito Francesco , Raymond Catalina , Cho Nicholas S , Hagiwara Akifumi , Orpilla Joey , Salamon Noriko , Cloughesy Timothy , Liau Linda M , Everson Richard G , Nghiemphu Phioanh L , Lai Albert , Prins Robert , Ellingson Benjamin 
Published: November 2022

Abstract

BACKGROUND
In a classic DSC perfusion sequence, T1 and T2* leakage effects compete in determining the post-bolus signal, and are influenced by tissue geometry (i.e. cell density, cell size). Different DSC sequences can be variously influenced by these effects, depending on the different degree of T1 weighting and the pre-bolus administration. Multi-echo spin-and-gradient echo (SAGE) perfusion MRI enables to disentangle T2* and T1 components that contribute to the classic DSC curve, and to simultaneously compute DSC and DCE perfusion metrics.

METHODS
We retrospectively selected patients with: diagnosis of primary brain tumor, availability of SAGE-based DSC perfusion and DWI datasets, availability of histopathological images from targeted biopsies located within contrast-enhancing tissue. Post-processing allowed to distinguish the pure T2* component from the pure T1 component, and to perform a DCE analysis on the latter. Quantitative perfusion MRI measurements were then evaluated with respect to underlying histopathology.

RESULTS
Histopathological images were available for 22 targeted biopsies (across 10 patients) meeting the inclusion criteria. The following novel MRI quantitative maps were successfully computed voxelwise: ΔR2* at steady state (reflecting T2* leakage effects), ΔR1 at steady state (reflecting T1 leakage effects), transverse relaxivity at tracer equilibrium (TRATE, reflecting the combination of T2* and T1 leakage effects). In addition, Ve and ktrans were computed from the DCE analysis, and the percentage of signal recovery (PSR) was computed from the second echo of the multi-echo DSC (comparable to a classic single-echo DSC sequence).

CONCLUSIONS
Histopathological validation will assess the usefulness of these novel multi-echo derived quantitative maps for the non-invasive prediction of tumor microstructure. This would be particularly relevant for: 1) differential diagnosis between brain tumors with different cell size and cell density (e.g. lymphoma vs glioblastoma); 2) treatment response assessment (as pre-existing studies proved that cell shrinkage is an early event in treatment response).
NIMG-51. 18KDA TRANSLOCATOR PROTEIN (TSPO) DECLINES IN TUMOR AND IMMUNE CELLS DURING PROGRESSION OF EXPERIMENTAL GLIOBLASTOMA
Authors: Bartos Laura , Kirchleitner Sabrina , Quach Stefanie , Blobner Jens , Wind Karin , Weidner Lorraine , Holzgreve Adrien , Gold Lukas , Kunze Lea , Antons Melissa , Kunte Sebastian , Beumers Philipp , Messerer Denise , Bartenstein Peter , Tonn Joerg-Christian , von Baumgarten Louisa , Riemenschneider Markus , Albert Nathalie , Brendel Matthias 
Published: November 2022

Abstract
Glioblastoma combines a lack of immunogenicity with a highly immunosuppressive tumor microenvironment (TME), including both tumor and immune cells. However, biomarkers that allow monitoring of the immune phenotype are still lacking. Hence, we investigated the 18kDa translocator protein (TSPO) during tumor progression in an experiential glioblastoma mouse model (SB28) mimicking human TME. We used TSPO-PET imaging ([18F]GE-180) and in vivo measures of single cell tracer uptake between days 6 and 18 after inoculation to study alterations and dependence of TSPO in tumor and peripheral organs in SB28 mice (n= 27) in comparison to sham controls (n= 11). CSF1R inhibition was applied to deplete tumor associated microglia/macrophages (TAM) followed by withdrawal to induce immune cell rebound (PLX5622 day -20 to day 6, n= 3). Compared to sham, TSPO-PET signals were distinctly elevated in tumor (+83%, p&lt; 0.001), heart (+35%, p&lt; 0.05), lung (+42%, p&lt; 0.01) and bone (+26%, p&lt; 0.05) of SB28 mice at day 6. TSPO-PET increases were lower at day 18 (tumor: +52%, p&lt; 0.05; all organs &lt; +15%, n.s.). The tumor TSPO-PET signal was strongly coupled with TSPO-PET signal in peripheral organs (all R≥ 0.88, all p&lt; 0.001). Single tumor cells and TAM showed strong early increases of TSPO tracer uptake at day 6 (tumor: 32-fold, TAM: 8.5-fold) and a decline of these increases at day 18 (tumor: 2.4-fold, TAM: 1.7-fold) when compared to sham microglia. Immune cell rebound restored TSPO tracer uptake in TAM (+120%, p&lt; 0.01) but not in tumor cells (-24%, n.s.) when compared to therapy-naïve SB28 mice. TSPO declines in tumor cells and TAM during progression of experimental glioblastoma. TSPO in peripheral organs could serve as a supportive indicator of declining immune response in glioblastoma. CSF1R inhibition and reinitiation yields specific restoring of TSPO in TAM and could serve as immunomodulatory therapy strategy.