bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract

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 &gt;.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 &gt;.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 &lt; 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

Abstract

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

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract

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 &gt; 65 years was predictive of decreased OS (p &lt; 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.
NIMG-81. EFFECT OF RE-TREATMENT WITH TEMOZOLOMIDE IN GRADE 2/3 IDH MUTANT GLIOMAS AT FIRST PROGRESSION
Authors: Yogendran Lalanthica , Abbas Salma , Scharf Zachary , Patel Sohil , Schiff David 
Published: November 2022

Abstract

BACKGROUND
Patients with WHO grade 2 and 3 IDHmt gliomas commonly receive temozolomide, with or without radiation therapy, as initial therapy. At progression, temozolomide is sometimes reinstated despite a paucity of data on effectiveness.

METHODS
We reviewed imaging outcomes of patients with grade 2/3 IDHmt gliomas re-treated with temozolomide at first progression between 2007 and 2019.

RESULTS
15 subjects included six grade 2 and five grade 3 oligodendrogliomas, one grade 2 and three grade 3 astrocytomas. Median time between completion of first temozolomide course and initiation of re-treatment was 47 months (range, 8-126 months). Best treatment response after first progression by RANO criteria was stable disease in 10 and progressive disease in 5 patients. Median progression free survival with temozolomide re-treatment was 27.4 months (95% CI: [13.8, 41.2 months] and median overall survival 47.8 months (95% CI: [31.5, 125.6 months]). In 14 patients who had pre- and post- first tumor progression rate measurements, the mean rate of tumor growth by bidimensional product was 0.29 cm2/month (95% CI: [0.17, 0.42 cm2/month], p&lt; 0.001) prior to first tumor progression versus 0.47 cm2/month (95 CI: [0.08, 0.85 cm2/month], p=0.021) during re-treatment with monotherapy temozolomide (prior vs after, p=0.371). The volumetric mean rate of tumor growth was 1.12 cc/month (95% CI: [0.31, 1.93 cc/month], p=0.010) prior to first tumor progression versus 1.29 cc/month (95% CI: [0.15, 2.43 cc/month], p=0.030) during temozolomide re-treatment (prior vs. after, p=0.802).

CONCLUSION
Among 15 patients with progressive IDHmt glioma re-treated with temozolomide, there was no radiographic response by RANO criteria nor a decrease in tumor growth rate during the re-treatment period compared to immediate pre-re-treatment period. These findings suggest previously treated, progressive IDHmt gliomas are generally resistant to temozolomide, underscoring the need for novel therapies.