bluuebunny/crossref_metadata_2025 huggingface dataset:
NIMG-12. DSC-MRI PERFUSION IMAGING IN UNTREATED BRAIN METASTASES USING THE CONSENSUS ACQUISITION PROTOCOL: A MULTI-SITE BENCHMARK STUDY
Authors: Kohn Sarah , Prah Melissa , Kong Min , Phung Daniel , Urcuyo Javier , Ye Jason , Attenello Frank , Mendoza Jesse , Shiroishi Mark , Hu Leland , Schmainda Kathleen
Published: November 2022

INTRODUCTION

DSC-MRI perfusion methods are commonly used to evaluate both primary and metastatic brain cancer with the creation of maps of relative cerebral blood volume (rCBV). Recently, guidelines were established to ensure a standard DSC-MRI acquisition protocol that reduces inter-site variability. The purpose of this study was to initiate the determination of rCBV benchmark values using the DSC-MRI consensus protocol in treatment-naïve brain metastases.

METHODS

Patients from three sites with treatment-naïve, contrast-enhancing brain metastases on MRI were considered for inclusion in this retrospective study. The MRIs included pre- and post-contrast T1w(T1+C) images obtained after administration of gadolinium-based contrast agent (GBCA) (0.1 mmol/kg), which serves as the recommended preload for the DSC-MRI data collection. A 2nd GBCA dose (0.1 mmol/kg) was administered 40-60sec after the collection of baseline GRE-EPI images using recommended settings (FA=60o, TE/TR=30ms/1100-1250ms) for 120s. Calibrated pre/post T1w difference maps (dT1) were used for delineation of enhancing lesion, and standardized (calibrated) rCBV (sRCBV) created. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and treatment-naive glioblastoma (GBM) from a previous study. Pairwise comparisons were performed using the Mann-Whitney nonparametric test.

RESULTS

N=52 patients with primary histology of lung (n=27); breast (n=6); skin (n=7); gastrointestinal (GI: n=3) and genitourinary (GU: n=9) cancers were included in comparison to GBM (n=31). The mean sRCBV for all metastases (1.77+/-1.05) is significantly lower (p=0.0003) than for GBM (2.67+/-1.34) but with both statistically greater (p< 0.0001) than NAWM (0.706+/-0.163). Individually, lung (1.47+/- 0.61), breast (2.275+/-0.87), skin (2.10+/-1.22), GI (1.91+/- 0.64) and GU (2.01+/-0.63) mean sRCBV are statistically greater than for NAWM.

CONCLUSION

Using the consensus DSC-MRI acquisition protocol confirms use of sRCBV to identify biologically active, treatment-naive brain metastases setting benchmark values for future applications.
NIMG-16. COMPARISON OF A STIR- AND T1-WEIGHTED-BASED RADIOMICS MODEL TO DIFFERENTIATE BETWEEN PLEXIFORM NEUROFIBROMAS AND MALIGNANT PERIPHERAL NERVE SHEATH TUMORS IN NEUROFIBROMATOSIS TYPE 1 (NF1)
Authors: Ly Ina , Liu Tianyu , Cai Wenli , Michaels Olivia , Kwon Daniel , Bredella Miriam , Jordan Justin , Borcherding Dana , Boswell Demetrius , Burgess Crystal , Chi Ping , de Blank Peter , Dombi Eva , Hirbe Angela , Korf Bruce , Lee Shernine , Mautner Victor , Melecio-Vázquez Mairim , Mulder Zachary , Pollard Kai , Pratilas Christine , Salamon Johannes , Srihari Divya , Steensma Matthew , Widemann Brigitte , Blakeley Jaishri , Plotkin Scott
Published: November 2022

BACKGROUND

Plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) are best visualized on short TI inversion recovery (STIR) sequences on MRI. However, STIR sequences are not routinely acquired in the clinical setting. T1-weighted pre-contrast (T1W) sequences are more standardly obtained but provide insufficient contrast for tumor identification. We developed a radiomics model based on STIR and T1W sequences to differentiate between NF1-associated PNF and MPNST.

METHODS

Using a 3D quantitative imaging analysis software (3DQI), 68 MPNST and 79 PNF from 134 participants at nine centers were segmented on STIR sequences (if available) or T2 fat-saturated or T1-weighted fat-saturated post-contrast sequences. Tumor regions of interest were co-registered to T1W sequences. Standard pre-processing included N4 bias field correction, intensity normalization (mean 120 SI, SD 80 SI), and resampling (1 mm3 voxel resolution). 107 radiomic features were extracted using PyRadiomics. To classify tumors as PNF or MPNST, we applied the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top five selected features. The data were divided into a training/validation and test set (7:3 ratio). Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using AUC, sensitivity, specificity, accuracy, and 95% CI.

RESULTS

For the STIR-based model, AUC was 0.856 (95% CI 0.727-0.984), sensitivity 0.6, specificity 0.833, and accuracy 0.727 in the test set. For the T1W-based model, AUC was 0.867 (95% CI 0.743-0.990), sensitivity 0.8, specificity 0.79, and accuracy 0.794 in the test set.

CONCLUSIONS

Our radiomics models demonstrate high and comparable performance to distinguish between PNF and MPNST on STIR and T1W sequences. Our inclusion of multicenter MRIs enhances model generalizability. These models can potentially be integrated into the radiologic workflow to help clinicians in the early identification of MPNST or pre-malignant atypical neurofibromas on clinical MRIs.
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

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< 10-5) between low- and high-risk patients, (HR= 0.47, 95% CI:0.34-0.65; P< 0.005). Average PMI values were significantly greater in high-risk patients (P< 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

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

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

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 > 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

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< 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< 0.001), shortened progression-free survival (HR 1.78, 95% CI 1.30-2.43, p< 0.001), subtotal resection (OR 6.96, 95% CI 4-51-10.73, p< 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

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

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 > 1.556) indicating a high probability of vascular tumor, blue regions (sRCBV< 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 >1.0) or the most vascular tumor (sRCBV > 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 > 1.0) of 8.33cc was 10.69 and 15.35 months (p=.0453; p=.0317). Unmethylated subjects stratified with FTBv (rCBV > 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 > 1.0) or FTBv (rCBV > 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

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

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

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< 0.001), heart (+35%, p< 0.05), lung (+42%, p< 0.01) and bone (+26%, p< 0.05) of SB28 mice at day 6. TSPO-PET increases were lower at day 18 (tumor: +52%, p< 0.05; all organs < +15%, n.s.). The tumor TSPO-PET signal was strongly coupled with TSPO-PET signal in peripheral organs (all R≥ 0.88, all p< 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< 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.
NIMG-52. UTILITY OF ARTERIAL SPIN LABELING (ASL) AND DYNAMIC SUSCEPTIBILITY CONTRAST (DSC) PERFUSION MRI IMAGING IN DISTINGUISHING PSEUDOPROGRESSION IN GLIOBLASTOMA
Authors: Lewis Clifton , Lucas Lee , Pozivilko David , Weng Frank , Livingston Brian , Lu-Emerson Christine
Published: November 2022

Pseudoprogression is a radiographic phenomenon in which increase in post contrast enhancement can mimic tumor progression while actually representing treatment effect. This occurs in up to 30% of glioblastoma (GBM) patients, with higher rate in O6-methylguanine-DNA methyltransferase (MGMT) methylated patient, and may lead to premature discontinuation of therapy. Perfusion imaging, with either DSC and/or ASL can sometimes help differentiate between progression (Pr) versus pseudoprogression (PsPr) noninvasively. Between 2009-2019, we conducted a retrospective study of GBM patients diagnosed between 2009-2019 at Maine Medical Center who received standard of care (surgery/radiation/temozolomide), subsequently displayed changes concerning for progression (n=23) or pseudoprogression (n=3) within 12 months from completion of radiation, and who had a second resection for pathological assessment. Perfusion values were analyzed by assessing DSC ratios and grading ASL signal on MRI. 7/25 patients were MGMT methylated in the Pr group compared to 1/3 patients in the PsPr group. Both Pr and PsPr patients had similar mean DSC ratios (2.098 Pr and 2.200 PsPr, p = NS). Mean ASL grade was 2.4 for Pr patients versus 1.3 for PsPr patients, p = NS. ASL grades of 2 or higher had a sensitivity of 62% (95% CI [36, 83%]) and specificity of 33% (95% CI [6, 79%]) in distinguishing Pr from PsPr patients. DSC ratios of 1 or greater had a sensitivity of 96% (95% CI [80, 99%]) and specificity of 0% (95% CI [0, 66%]) for Pr versus PsPr patients. When both DSC ratios of 1 or greater and ASL grade of 2 or higher were combined, sensitivity remained high at 91% (95% CI [62, 98%]) with specificity of 0% (95% CI [0, 66%]). Despite the limited number of PsPr patients, results support the use of both DSC and ASL measurements to guide the determination of progression versus pseudoprogression in treated glioblastoma patients.
NIMG-54. LOW FRACTIONAL TUMOR BURDEN VOLUME OBTAINED FROM PERFUSION MRI REVEALS SIGNIFICANT SURVIVAL BENEFIT IN MGMT UNMETHYLATED GLIOBLASTOMA
Authors: Prah Melissa , Connelly Jennifer , Schmainda Kathleen
Published: November 2022

INTRODUCTION

Standard imaging remains inadequate for the determination of GBM response within the first 3 months following upfront chemoradiation therapy (CRT). Since advanced diffusion and DSC perfusion (pMRI) MRI are currently not included in RANO criteria, greater reliance is placed on MGMT methylation status, with methylated tumors demonstrating survival benefit. While pMRI has demonstrated usefulness following CRT, a newer pMRI biomarker termed Fractional Tumor Burden (FTB) provides both spatial and quantifiable information. The purpose of this study was to evaluate whether advanced MRI including FTB informs a survival benefit beyond MGMT promoter methylation status in newly diagnosed GBM (nGBM).

METHODS

Consented subjects with nGBM, and known MGMT status who completed standard upfront therapy and had MRI within 6 weeks of CRT completion, were included. Known IDH-mutant tumors were excluded. DSC was collected with the consensus protocol and leakage-corrected relative cerebral blood volume (rCBV) generated. Apparent diffusion coefficient (ADC) was calculated from diffusion MRI (b-value: 0,1000 s/mm2). FTB-defined tumor volumes were calculated from rCBV and delta T1w enhancement. Kaplan-Meier 24-month overall survival (OS) was evaluated for MGMT status and stratified by cohort mean for rCBV (1.3 a.u.) and ADC (1300 x10-6mm2/s), and empirically for FTB (5 ml) (significance: P >.05).

RESULTS

A total of 43 subjects were included (male/female=21/22; age=57(23-74)). MRI was acquired an average of 29 (12-42) days following CRT. Significant OS differences were confirmed for MGMT methylation (OS=19.08 vs. 10.18 mo; P=.0129). No significant survival differences were found (P >.05) when stratified by rCBV or ADC. While FTB did not provide further OS distinctions in methylated GBM, those with unmethylated MGMT had significant OS benefit when FTB volume was < 5 ml (OS=16.15 vs 8.39 mo; P=.0054).

CONCLUSION

FTB further informs OS in MGMT promoter unmethylated GBM, where those with lower volume had survival approaching that of methylated subjects.
NIMG-58. IMMUNOPET OF 89ZR-DFO-CD69 AB VISUALIZES T-CELL ACTIVATION AND PREDICTS SURVIVAL FOLLOWING IMMUNOTHERAPY IN MURINE GBM MODEL
Authors: Nisnboym Michal , Vincze Sarah , Raphael Itay , Sneiderman Chaim , Xiong Zujian , Li Bo , Day Kathryn , Latoche Joseph , Nedrow Jessie , Anderson Carolyn , Pearce Thomas , Pollack Ian , Lieberman Frank , Drappatz Jan , Edwards Wilson , Kohanbash Gary
Published: November 2022

INTRODUCTION

Glioblastoma (GBM) is the most malignant brain tumor in adults, with a dismal prognosis despite aggressive therapy. Immunotherapy is currently being evaluated as a treatment modality for recurrent GBM. MRI is not adequate for response assessment to immunotherapy even after using refined response assessment criteria. Thus, there is a need for the development of neuroimaging techniques for response assessment. T-cells are key mediators of cancer immunotherapy responses and upregulation of CD69 is a marker of T-cell activation. Our aim is to use PET/CT imaging to non-invasively quantify CD69 in vivo, following immunotherapy, and correlate the expression to survival.

METHODS

CD69 was evaluated by flow cytometry and immunofluorescence staining on human and mouse in vitro activated T-cells and on dissociated tumors from GL261 glioma-bearing mice treated with anti-PD1/anti-CTLA4 immunotherapy (ICI). Single-cell RNA sequencing (ScRNAseq) datasets from recurrent GBM patients receiving (n=20) or not receiving (n=22) ICI were examined for CD69 expression on tumor infiltrating lymphocyte (TIL) populations. PET/CT was performed on mice (n=30) receiving radiolabeled anti-CD69 antibody (89Zr-DFO-anti-CD69) to evaluate response to ICI therapy. Standard uptake values (SUV) were compared between ICI and controls and in relationship to survival.

RESULTS

We confirmed CD69 upregulation upon T-cell activation in vitro. Ex vivo, CD69 expression significantly increased on TILs early after ICI treatment compared to control (63.46% vs 24.37% CD69+/TILs, respectively; p=0.017). ScRNAseq demonstrated significant elevated CD69 expression in almost all TIL populations tested in recurrent GBM patients treated with ICI compared with a control group. ImmunoPET demonstrated significantly higher anti-CD69 tracer uptake in ICI-treated mice compared with controls. Most importantly, we observed a strong positive correlation between survival and immunoPET SUV (r=0.9425, p=0.016) in the ICI-treatment group, but not within the control group.

CONCLUSIONS

Our study demonstrates the potential incorporation of CD69 ImmunoPET as response assessment to ICI for GBM patients.
NIMG-61. IMPROVED GENERALIZABILITY OF RADIOPATHOMIC PROBABILISTIC MAPPING OF TREATMENT-INDUCED EFFECTS WITH PHYSIOLOGIC MR IMAGING AND DEEP LEARNING IN PATIENTS WITH RECURRENT GLIOBLASTOMA
Authors: Ellison Jacob , Tran Nate , Molinaro Annette , Pedoia Valentina , Phillips Joanna J , Shai Anny , Nair Devika , Lafontaine Marisa , Jakary Angela , Luks Tracy , Villanueva-Meyer Javier , Chang Susan M , Berger Mitchel S , Hervey-Jumper Shawn L , Aghi Manish , Lupo Janine
Published: November 2022

Although physiologic (diffusion-weighted and perfusion-weighted) MRI has shown promise in identifying regions of recurrent tumor (rTumor) in patients with glioblastoma suspected of progression, distinguishing treatment-induced effects (TxE) from rTumor on anatomical MRI remains a challenge. Whereas prior larger-scale machine learning (ML)-based studies mostly utilize anatomical imaging alone and/or perform lesion-level predictions, this study aimed to develop a non-invasive, radiopathomic tool for regional probabilistic mapping of TxE using 208 tissue-samples (55 pathologically-confirmed TxE, 153 recurrent glioblastoma) acquired from 107 patients with known spatial coordinates on pre-surgical MRI. We tested the hypothesis that applying a deep-learning (DL) model that included physiological MRI can: 1) more accurately identify areas of TxE that mimic rTumor on anatomical MRI and 2) better generalize to an independent test set than ML-models or a DL-model that uses anatomical MRI alone. An 80/20 split for training/validation was used after 1/3 of the patients were withheld for testing. Oversampling of TxE samples was employed to address class imbalance and an equal proportion of TxE samples was maintained across all datasets. Three ML-models, their ensemble, and a deep 4D-convolutional-neural-network were trained based on normalized anatomical (post-contrast T1, T2-FLAIR), diffusion-weighted (ADC, FA), and DSC perfusion-weighted (PeakHeight, %recovery) images cropped to 10mm-cubic patches centered on the coordinates from where tissue was obtained. Although Random Forest and voting-ensembled ML-models using all imaging and the anatomical DL-model had the best validation performance (AUC=0.81-0.82), these models did not generalize (test AUC=0.58-0.59). The DL-model including physiologic images had slightly lower validation AUC (0.78) but the best overall test AUC (0.795), indicating superior generalizability. Elevated blood volume (nPeakHeight) was the most important feature. Our DL-model’s interpretability was also demonstrated by disrupting class separation after shuffling voxels within each input patch. These results suggest that using deep-learning with physiologic MRI can improve intratumoral classification of TxE from rTumor.
NIMG-63. PRE-SURGICAL RADIO-PATHOMIC MAPS OF TUMOR CELLULARITY PREDICT EARLY RECURRENCE IN GLIOBLASTOMA PATIENTS
Authors: Winiarz Aleksandra , Bobholz Samuel , Lowman Allison , Duenweg Savannah , Kyereme Fitzgerald , Coss Dylan , Cochran Elizabeth , Connelly Jennifer , LaViolette Peter
Published: November 2022

PURPOSE

Glioblastoma time to recurrence following initial surgery is difficult to predict as it differs widely between patients. This is important as those who recur early have a poorer prognosis and shorter survival time. This study aimed to test the hypothesis that cell density, as defined by a predictive radio-pathomic mapping model, would indicate a more aggressive tumor, and thus carry a lower time to progression.

METHODS

18 confirmed glioblastoma patients were included in this study. All patients underwent surgery followed by chemo-radiation, consistent with standard of care. Inclusion criteria also included radiographic recurrence, and autopsy confirmation of recurrent glioblastoma. Three magnetic resonance imaging (MRI) timepoints were investigated: pre- and post-surgery, and tumor recurrence defined by a radiologist. Patients were classified into two groups, early recurrence, as defined by tumor progression in the first 6 months post-surgery MRI (n=9, average 116 days to recurrence), and late recurrence, which included everyone else (n=9, average 283 days to recurrence). Contrast enhancement and FLAIR hyperintensity regions of interest were annotated from the patients’ T1+C and FLAIR scans. Radio-pathomic maps of predicted tumor cellularity were generated from a previously published machine learning model trained to identify tumor pathology using aligned autopsy tissue samples as ground truth to clinical MRI scans. The T1, T1+C, FLAIR, and apparent diffusion coefficient (ADC) images were used as input. Tumor cellularity values were then averaged across the T1+C and FLAIR ROIs.

RESULTS

Both pre- and post-surgical cell density within contrast enhancement was significantly greater in patients with early recurrence compared to those who recurred later (p ≤0.05).

CONCLUSIONS

Our results suggest that radio-pathomic maps of cell density can identify early-recurrence in patients prior to treatment. This may help with treatment planning for radiologists, surgeons, and neuro-oncologists which may include more aggressive surgery and more frequent monitoring.
NIMG-72. VALIDATION OF CONDITIONAL AND SUPER-RESOLUTION GENERATIVE ADVERSARIAL NETWORKS FOR IMPUTATION OF MISSING BRAIN MRI SEQUENCES
Authors: Conte Gian Marco , Tobin W Oliver , Moassefi Mana , Faghani Shahriar , Decker Paul , Kosel Matthew , Nikanpour Yalda , Zhang Kuan , Lachance Daniel Honore , Jenkins Robert , Erickson Bradley , Eckel-Passow Jeanette
Published: November 2022

Missing MRI sequences are common in radiology due to differences in protocols, time constraints, and image artifacts. Missing sequences are a nuisance for subsequent algorithms, such as automated segmentation algorithms requiring multiple MRI sequences (e.g., HD-GLIO, which requires T2, T1, post-contrast T1 (T1c), and FLAIR). Accurate tumor segmentation is a fundamental step in extracting radiomics analysis to use, for example, to predict the IDH status of glioma or their overall survival. We validated a conditional (pix2pix) and super-resolution GAN (SOUP-GAN) for imputing MRI sequences on 285 adult glioma with complete MRI sequences available. We simulated missing T1 sequences and subsequently imputed them from T1c using pix2pix and from sagittal T1 using SOUP-GAN. Using HD-GLIO, we obtained the true segmentations from the complete MRI sequences. HD-GLIO was also used after replacing the original T1 sequence with the one imputed with pix2pix and SOUP-GAN. Thus, there were three sets of segmentations for each subject. We compared T2 and T1c radiomics features extracted with Pyradiomics using segmentations obtained with complete data with features extracted from segmentation obtained with the imputed T1 sequences using intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Median (interquartile range) ICC for shape features extracted from T2 and T1c were 0.82(0.76-0.92) using pix2pix and 0.89(0.86-0.94) using SOUP-GAN. Median ICC for first-order-statistics features were 0.90(0.83-0.93) for T2 and 0.90(0.88-0.92) for T1c using pix2pix, and 0.61(0.22-0.82) for T2 and 0.42(0.34-0.57) for T1c using SOUP-GAN. Median ICC for texture features were 0.86(0.82-0.87) for T2 and 0.87(0.79-0.90) for T1c using pix2pix, and 0.69(0.47-0.81) for T2 and 0.76(0.65-0.83) for T1c using SOUP-GAN. We show that T2 and T1c shape features (e.g., volumetric analysis) can be adequately extracted from segmentations obtained with an algorithm requiring multiple MRI sequences using T1 sequences imputed with GANs; however, the effect of imputation differed for first-order-statistics and texture features.
NIMG-78. DEVELOPMENT OF A PRECLINICAL GLIOBLASTOMA MURINE MODEL FOR THE ASSESSMENT OF RADIATION NECROSIS USING DIFFUSION KURTOSIS IMAGING
Authors: Stephenson Connor , McDonald Daniel G , Yazdani Milad , Mehrotra Shikhar , Welsh Cynthia A , Infinger Libby Kosnik , Vandergrift William A , Varma Abhay K , Patel Sunil J , Giglio Pierre , Zukas Alicia , Cachia David , Lindhorst Scott M , Das Arabinda
Published: November 2022

Glioblastoma (GB) patients often present with either radiation-induced necrosis (RN) or develop tumor recurrence (TR). Radiologically, differentiating between these two disease states is particularly difficult, especially utilizing traditional MRI techniques. Accurately distinguishing these separate states is important, as failure to do so can ultimately lead to unnecessary surgical intervention or early cessation of appropriate radiation therapy. Currently, distinction between these pathologies is made using best clinical judgement by the neuro-oncology team. Therefore, there is a need for developing novel non-invasive techniques that can reliably distinguish between radiation necrosis and tumor recurrence. Primary data was collected utilizing orthotopically transplanted GL261 mouse GB cells in a C57BL/6 mouse. Tumor induction was verified by MRI after two weeks. RN and TR were initiated using an aggressive radiation dose fractionation (12 Gy or 60 Gy). This was completed using a 4 mm radiation cone such that one portion of the tumor received 100% dose of 12 Gy or 60 Gy fraction which is sufficient to cause RN, whereas the tumor edge received only 50% of the dose, allowing for tumor recurrence. Our data demonstrated mice tumor recurrence and radiation necrosis on MRI imaging. Axial T2-weighted MRI and DKI sequence at 2 weeks following implantation demonstrated edema compatible with tumor recurrence. One week later, the T2-weighted MRI and DKI sequences demonstrated continued tumor progression. In a separate mouse with orthotopic glioblastoma implantation with high dose (60 Gy) radiation treatment, T2 imaging and DKI demonstrated areas of tumor and suspected central radiation necrosis. DKI imaging biomarkers was compared by liquid biopsy, H&E staining, and Immunohistochemistry (IHC) analysis of sacrificed mice. Our H&E staining results showed typical pathological features of GB and RN, and TR in each case. This result demonstrated that our proposed model for radiation induction was effective at achieving its goal.
NIMG-80. A RADIOMICS-BASED MODEL PREDICTIVE OF OVERALL SURVIVAL IN GLIOBLASTOMA USING MULTI-INSTITUTIONAL PREOPERATIVE MRI SCANS
Authors: Kowalchuk Roman , Conte Gian Marco , Crompton David , Cabreja Ricardo Domingo , Vega Carlos Perez , Moassefi Mana , Faghani Shahriar , Khosravi Bardia , Vora Sujay , Erickson Bradley , Trifiletti Daniel
Published: November 2022

PURPOSE

Though patients with glioblastoma generally have poor overall survival (OS), OS estimates range significantly. We sought to develop a radiomics-based model predictive of OS in glioblastoma using preoperative MRI scans to identify these differences and guide clinical care.

METHODS

The primary endpoint was OS, determined from the date of surgery to death or last follow-up. T1, FLAIR, T2, and post-contrast T1 (T1c) MRI sequences were obtained for all patients. Using HD-GLIO-AUTO, lesion segmentation yielded three masks corresponding to the regions of contrast enhancement, non-enhancement, and total volume. Radiomics features were extracted from each region from the T2 and T1c sequences independently (i.e. either from T2 or T1c) and in combination. Age at surgery and gender were also considered. The data was split into training (75%) and test (25%) sets, stratifying for age at surgery. Kaplan-Meier analysis and the Coxnet model were used. For Coxnet, Elastic Net was used as the penalty, with optimization of the L1 coefficient using 5-fold cross-validation and evaluation via the concordance index (C-index).

RESULTS

From 3 institutions, 383 patients were included. Median OS was 395 days: 346 patients died, and 37 were right-censored. Age > 65 years was predictive of decreased OS (p < 0.005), but gender was not (p=0.16). For the Coxnet model, the C-index was 0.67 (standard deviation: 0.01) considering T2 and T1c sequences for the total lesion volume, with a C-index of 0.65 for the test set. For the enhancing volume, the model had a C-index of 0.71 for the test set using T1c sequences. Overall, C-indices ranged from 0.62-0.71.

CONCLUSIONS

We present a novel radiomics-based model predictive of OS in glioblastoma across a multi-institutional dataset. Ongoing efforts will incorporate immediate postoperative imaging and IDH and MGMT status into the proposed model to further improve prognostic ability and help guide clinical decision-making.