bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-23. COMBINATION OF ADC MRI AND FET PET FOR THE ASSESSMENT OF QUANTITATIVE TUMOR PRESENCE IN DIFFUSE GLIOMA
Authors: Tjin Joe Kaithlyn Tjong , Wesseling Pieter , Barkhof Frederik , Beliën Jeroen , Yaqub Maqsood , Pouwels Petra , Verhaak Roel , Reijneveld Jaap , Rozemuller Annemieke , Vandertop Peter , Boellaard Ronald , Barthel Floris , Hamer Philip de Witt , van de Giessen Elsmarieke , Verburg Niels 
Published: November 2023

Abstract

BACKGROUND
Local treatment of diffuse glioma is currently guided by standard MRI sequences. Yet, the combination of apparent diffusion coefficient (ADC) and O-(2-[18F]-fluoroethyl)-L-tyrosine positron emission tomography (FET PET) (ADC/FET) detects qualitatively assessed histopathological tumor presence more accurately than standard MRI imaging. It is unknown whether this is similar for quantitatively assessment of tumor presence. This study compares the diagnostic accuracy of standard imaging and ADC/FET for detecting quantitatively assessment of tumor presence in diffuse glioma.

METHODS
Fifteen patients with newly diagnosed diffuse glioma were retrospectively included. Pre-operative MRI and FET PET imaging were available. Multiregional image-guided stereotactic biopsies were acquired before craniotomy. Quantitative imaging data from regions-of-interests (ROIs) centered at the biopsy locations was obtained. For each biopsy sample, semi-automatic cellularity and proliferation index, as well as DNA methylation-based tumor purity, was determined. Linear mixed models were used to evaluate which modalities were significant independent predictors for each tumor presence marker, and fitness was assessed using Bayesian Information Criterium.

RESULTS
125 biopsies were analyzed (75 from eight high grade and 50 from seven low grade gliomas). Cellularity was associated with T1 weighted MRI (p = 0.033), ADC (p = 0.016) and ADC/FET (p &lt; .001), with ADC/FET as best fit. ADC/FET was the only modality associated with proliferation index (p = 0.003) and with tumor purity (p &lt; .001).

CONCLUSION
Combined ADC and FET PET is stronger associated with cellularity, proliferation index and tumor purity than standard MRI imaging. These findings may pave the way towards ADC/FET-guided local treatment of diffuse gliomas.
NIMG-26. DIFFERENTIATION OF IDH-WILDTYPE GLIOBLASTOMA AND TUMEFACTIVE MULTIPLE SCLEROSIS USING PRE-OPERATIVE BRAIN MRI AND DEEP LEARNING: RIGOROUS AND UNBIASED EXPERIMENTAL DESIGN AND ANALYTICAL APPROACH
Authors: Conte Gian Marco , Decker Paul , Kosel Matthew , Kollmeyer Thomas , McCarthy Christina , Sagen Jessica , Nikanpour Yalda , Fereidan-Esfahani Mahboubeh , Erickson Bradley , Jenkins Robert , Lachance Daniel , Tobin W Oliver , Eckel-Passow Jeanette 
Published: November 2023

Abstract
Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Misdiagnosis of brain lesions may expose patients to unnecessary anxiety, surgery, or radiotherapy. Noninvasive methods to help with accurate diagnosis of tumor and non-tumor etiologies are needed. tMS subjects were identified by the Mayo Clinic Center for Multiple Sclerosis and Autoimmune Neurology (CMSAN). GBM subjects were matched to tMS by age at diagnosis, index MRI date, and sex. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. To identify potential experimental design bias, MRI technology and clinical characteristics were compared across groups. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 600 epochs, batch size 16, learning rate 10-3, cross entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential epoch cross entropy loss &lt; 0.02. Five-fold cross validation (CV) was utilized to obtain an unbiased estimate of predictive performance (overfitting) and sensitivity of the 3D-DenseNet121 algorithm was evaluated. 224 subjects (114 GBM, 110 tMS) were analyzed: 40% male, median age 46. After one round of CV, the average unbiased estimate of sensitivity and specificity for predicting GBM from the final classification model was 0.86 and 0.73, respectively. To assess the reproducibility of the deep learning algorithm (3D-DenseNet121), the exact same CV folds were rerun resulting in average sensitivity and specificity of 0.82 and 0.75, respectively. Obtaining unbiased estimates of model performance using internal validation is critical before evaluating a final model on an external dataset. Understanding sensitivity of a deep learning algorithm is important for model validity. Differentiation of GBM and tMS appears reasonable with MRI; external validation is in progress.
NIMG-28. DIFFUSION DIFFERENCES IN LOW TO HIGH GRADE GLIOMA TRANSFORMATION
Authors: Flatley Michael , Duenweg Savannah , Lowman Allison , Bobholz Samuel , Stebbins Margaret , Winiarz Aleksandra , Nath Biprojit , Kyereme Fitzgerald , Connelly Jennifer , Coss Dylan , Krucoff Max , Banerjee Anjishnu , LaViolette Peter 
Published: November 2023

Abstract

PURPOSE
Apparent diffusion coefficient (ADC) maps offer tremendous insight into cellularity and local tissue environment. Proliferation of tumor cells results in increased local cellularity and cell density, thus impairing the free diffusion of water molecules. Therefore, hypointense ADC signals are thought to reflect regions of increased cellularity, and are useful imaging markers for distinguishing neoplastic cellular processes. In this study, we tested the hypothesis that changes in ADC intensity over the course of disease progression differ in progressing gliomas within the tumor-delineated region.

METHODS
Sixty-three glioma patients from our brain bank were separated into two groups. Group 1 included patients with Grade 1-3 gliomas at surgical diagnosis and progressed to a Grade 4 glioma at autopsy. The second group of patients were those with an initial and final Grade 4 glioma diagnosis. Contrast-enhancing tumor regions on MRI sequences were annotated with an established machine-learning algorithm for each patient on their initial and final MRIs. Average ADC intensity within the contrast enhancing regions was extracted and change in signal intensity between the first and last MRI was calculated for each patient. Finally, the mean intensity difference between Group 2 and Group 1 were evaluated using a linear mixed model.

RESULTS
We report a negative mean difference between the Group 2 and Group 1s change in ADC intensity over time (p=0.03).

CONCLUSIONS
These findings suggest changes in cellularity, identified by ADC, are greater in patients who progress from low-to-high grade gliomas. With these results, it may be concluded that change in ADC hypointensity, as a proxy for cellularity, may be a defining imaging characteristic to identify progressing gliomas.
NIMG-29. ADVANCED MRI BIOMARKERS PREDICT EARLY RESPONSE TO TUMOR TREATING FIELDS IN GLIOBLASTOMA
Authors: Schmainda Kathleen , Prah Melissa , Connelly Jennifer 
Published: November 2023

Abstract

BACKGROUND
Tumor-treating fields (TTFields) is a novel US FDA-approved antimitotic treatment modality for patients with newly diagnosed and recurrent glioblastoma, shown to improve both OS and PFS (Stupp et al 2015). Yet, how to best monitor response to TTfields remains unclear. Conventional (anatomic) MRI is unreliable, often showing initial early radiographic progression followed by tumor regression and clinical benefit, thereby delaying confirmation of response. To address this limitation, we evaluate the utility of quantitative (physiologic) MRI biomarkers.

METHODS
Patients with newly diagnosed or recurrent IDH wildtype glioblastoma who underwent MRI with DSC-MRI perfusion imaging at 1-2, 3-4 and 5-6 months after TTField treatment were retrospectively identified for this study. Delta T1 (dT1) images were created from standardized (calibrated) pre- and post-contrast T1-weighted images, from which an enhancing lesion volume was objectively determined. Standardized and leakage-corrected relative cerebral blood volume (sRCBV) images were created from the DSC-MRI data. Together, dT1 and sRCBV were used to generate class maps of fractional tumor burden (FTB), which identify highly-vascular and avascular (treatment effect) regions within contrast-enhancement. Kaplan-Meier survival analysis was performed to determine if positive versus negative changes in these metrics, at each time-point relative to pre-treatment values, are predictive of survival.

RESULTS
20 patients with newly diagnosed (n=17) or recurrent (n=4) IDH wildtype glioblastoma with a mean TTField compliance of 74.9% were identified. At 1-2 months no metric predicted a survival difference, consistent with the known 4-week delay in tumor stabilization following TTField therapy. At months 3-4 and 5-6 decreasing dT1-determined enhancing volumes showed a survival benefit (p=0.0081, p=0.03). Likewise, a decreasing percentage of highly-vascular tissue, determined from FTB class maps, demonstrated a survival benefit (p=0.0013).

CONCLUSIONS
Quantitative MRI metrics, including dT1-derived enhancing volumes and FTB/sRCBV-defined regions of high vascularity, demonstrate an ability to provide early indicators of response to TTField treatment.
NIMG-37. FUNCTIONAL MRI AND DTI TRACTOGRAPHY AS ADJUNCTS TO IMPROVE THE PREDICTIVE VALUE OF TMS FOR CORTICAL LANGUAGE SITES
Authors: Muir Matthew , Prinsloo Sarah , Noll Kyle , Kumar Vinodh , Traylor Jeffrey , Prabhu Sujit 
Published: November 2023

Abstract
Surgeons must maximize the extent of resection while minimizing postoperative neurological morbidity in patients with eloquent gliomas. Transcranial magnetic stimulation (TMS) has emerged as a novel non-invasive technique to map the functional language cortex. However, multiple studies have documented the plethora of false positives that decrease the predictive value of TMS for intraoperative functional sites. Here we hypothesize that diffusion tensor imaging (DTI) tractography and functional magnetic resonance imaging (fMRI) can serve as adjuncts to identify true positive TMS preoperatively and increase the positive predictive value (PPV). We used a distortion correction algorithm to overlay the postoperative MRI with the preoperative surgical plan and correlated the resection versus preservation of certain structures with functional outcomes measured by pre to postoperative change in the Western Aphasia Battery (WAB) score in a cohort of 61 patients with language eloquent gliomas. We found that the resection of TMS points alone does not correlate with aphasic surgical deficits. We then seeded DTI tractography with TMS points and investigated the effect of progressively increasing fractional anisotropic (FA) thresholds. We found that the resection of TMS points with connecting white matter tracts identified at the 50%, 75%, and 85% FA thresholds strongly correlated with surgical deficits. (r=.38, p=.002; r=.61, p&lt; .001, r=.54, p&lt; .001) The PPV increased with increasing FA thresholds. The highest PPV was 50% at the 75% and 85% FA thresholds while the highest negative predictive value was 98% at the 75% FA threshold. We then combined fMRI with TMS and DTI tractography by only using fMRI-colocalized TMS points for tractography seeding. The PPV increased to 63% but the correlation coefficient with aphasic deficits stayed at .61. This study shows that adjunctive modalities can significantly increase the predictive value of TMS for preoperatively identifying true positive cortical sites of language function.
NIMG-46. MULTI-PARAMETRIC MRI RADIOMIC ANALYSIS USING DEEP LEARNING PREDICTS PERITUMORAL GLIOBLASTOMA INFILTRATION AND SUBSEQUENT RECURRENCE
Authors: Kwak Sunwoo , Akbari Hamed , Garcia Jose , Mohan Suyash , Davatzikos Christos 
Published: November 2023

Abstract

PURPOSE
Glioblastoma (GBM) is the most aggressive and infiltrative brain tumor with a very poor prognosis which hasn’t significantly improved in over 20 years. The almost 100% recurrence rate is due to cancer infiltration beyond the tumor margins currently being targeted by standard of care. Prior studies used traditional supervised machine learning have shown great promise in predicting tumor infiltration beyond these margins. We hypothesize that deep learning methods can further improve such predictive maps and guide intensive, yet targeted and personalized therapies.

METHODS
MRIs from a cohort of 109 de novo GBM patients were collected from Hospital of the University of Pennsylvania. All the patients incorporated in this study had pre-operative multi-parametric MRI scans including T1, T1Gd, T2, T2-FLAIR, ADC, and underwent surgical resection followed by standard chemoradiation therapy and had pathologically confirmed recurrence. A novel, automated deep learning method, informed by results of prior studies using support vector machines, was constructed to train a patch-based ensemble CNN to identify regions of peri-tumoral cancer infiltration. Leave-one-out was used to evaluate the predictive value of this method, against pathology-proven subsequent recurrence.

RESULTS
Probability maps, representing the likelihood of tumor infiltration and eventual recurrence, were binarized using threshold of 50% cutoff, compared with actual recurrence on post-recurrence MRI scans. The average cross-validated accuracy was 92%, specificity was 93%, patient-based sensitivity was 78%, and odds-ratio among all patients was 12.95 (Estimated “hot spots” were 12.95 times more likely to present recurrence in the future).

CONCLUSIONS
This study demonstrates that Multi-parametric MRI pattern analysis using CNN based network can successfully predict tumor infiltration in peritumoral region for Glioblastoma patients. These suggest that new intensive, yet precisely targeted treatments can be developed, guided by AI-driven predictive maps of infiltration.
NIMG-50. TOTAL TUMOR CELLULARITY AND CELLULAR GROWTH RATES ESTIMATED USING A RADIO-PATHOMIC AI MODEL PREDICTS PROGNOSIS AND RESPONSE TO CYTOTOXIC THERAPY IN RECURRENT GBM
Authors: Oshima Sonoko , Yao Jingwen , Bobholz Samuel , Nagaraj Raksha , Raymond Catalina , Teraishi Ashley , Guenther Anna-Marie , Kim Asher , Sanvito Francesco , Cho Nicholas , Eldred Blaine , Connelly Jennifer , Pope Whitney , Nghiemphu Phioanh , Lai Albert , Salamon Noriko , Cloughesy Timothy , LaViolette Peter , Ellingson Benjamin 
Published: November 2023

Abstract

PURPOSE
A radio-pathomic machine learning model trained with postmortem tissue samples has recently been developed to estimate tumor cellularity from standard anatomic MR images (Bobholz SA et al. AJNR 2022). The aim of this study was to use this externally developed, machine learning-based model to assess the impact of tumor cellularity and changes in cellularity on survival in recurrent glioblastoma (rGBM) treated with standard cytotoxic therapies.

METHODS
We retrospectively evaluated 22 cases of rGBM treated with chemotherapy. Radio-pathomic maps of predicted tumor cellularity were generated using the radio-pathomic model using pre- and post-contrast T1-weighted MR images, T2-weighted FLAIR images and apparent diffusion coefficient calculated from diffusion-weighted imaging. Total cellularity was estimated by integrating the cellularity measures within a contrast-enhancing tumor volume of interest. Cellularity growth rates (CGRs) were computed using three pre-treatment scans and available post-treatment scans. Tumor cellularity and CGRs were assessed in terms of their impact on overall survival (OS) using log-rank analysis.

RESULTS
Patients with baseline tumor cellularity &lt; 8.89x108 cells and those with pre-treatment CGR &lt; 7.55x106 cells/day showed significantly longer OS than those with higher cellularity or CGRs (P = 0.001 and 0.008, respectively). Patients with cellularity &lt; 8.90x108 cells at the 1st follow-up (one month after treatment), those with cellularity at 2nd follow-up &lt; 8.89x108 cells (two months after treatment), post-treatment CGR &lt; 1.51x107 cells/day, and change in CGR after treatment &lt; +5.91x106 cells/day showed significantly longer OS than those with higher values (P = 0.03, &lt; 0.001, &lt; 0.001, = 0.02, respectively).

CONCLUSION
These preliminary results suggest the potential value of estimating total cellularity within the enhancing tumor as a biomarker for prognosis prediction and cytotoxic therapy effect evaluation in rGBM. Additional analyses with more cases and treatments are warranted.
NIMG-51. RADIOGRAPHIC IMPROVEMENT IN A PATIENT WITH OLIGODENDROGLIOMA UNDERGOING TREATMENT FOR ACUTE MYELOID LEUKEMIA
Authors: Davila Mauricio Perez , Mendez Lourdes , Baehring Joachim 
Published: November 2023

Abstract
INTRODUCTION: The standard chemotherapy for treating oligodendroglioma consists of a combination of procarbazine, lomustine, and vincristine (PCV). The combination of hypomethylating agents, like azacitidine, and BCL2 inhibitors like venetoclax have not been formally studied in the treatment of glial tumors. The combination of these two drugs is commonly used to treat acute myeloid leukemia, with IDH mutant disease being a particularly sensitive subtype. CASE REPORT: A 48-year-old man was diagnosed with a left frontal, grade II oligodendroglioma following multiple episodes of speech arrest and right-hand numbness. He was subsequently treated with six cycles of PCV with a minor response. After slow progression over eight years, the tumor started displaying contrast enhancement. While planning for radiation therapy, he presented to the Emergency Department with severe fatigue and shortness of breath. Workup led to a diagnosis of pure erythroid leukemia, with molecular and cytogenetic studies suggesting a neoplasm related to prior exposure to cytotoxic chemotherapy. He was treated with three cycles of venetoclax and azacitidine, but unfortunately, the erythroleukemia progressed after a transient response to this treatment. On the other hand, however, a re-staging MRI brain showed a remarkable reduction of his tumor size of approximately 40% per RANO criteria. The patient was off steroids and remained neurologically asymptomatic during this time. Discussion: The use of azacitidine for the treatment of IDH mutant gliomas has been reported in the literature, with mixed results that might suggest at least some benefit in a subtype of patients. Other studies have also suggested that the BCL2 gene is associated with treatment resistance and tumor recurrence in gliomas. Our case presents an interesting observation that combining a hypomethylating agent with a BCL2 inhibitor might convey clinical and radiographic benefits for patients with relapsed IDH mutant gliomas.
NIMG-68. TEMPORAL CHANGES IN TUMOR COMPOSITION DIFFER ACROSS TUMOR GRADE IN GLIOMA
Authors: Duenweg Savannah , Bobholz Samuel , Lowman Allison , Stebbins Margaret , Winiarz Aleksandra , Flatley Michael , Nath Biprojit , Kyereme Fitzgerald , Connelly Jennifer , Coss Dylan , Krucoff Max , Banerjee Anjishnu , LaViolette Peter 
Published: November 2023

Abstract
Gliomas are the most common primary adult brain tumor in the United States and their biological heterogeneity makes it difficult to assess and treat tumor progression over time. This study used conventional MRI tumor segmentations to determine longitudinal growth differences between low grade (≤ G2), G3, and G4 gliomas. A total of 60 patients with at least three scans collected before death were organized into low-grade (n = 9), G3 (n = 13), and G4 (n = 38) gliomas at initial diagnosis. T1, T1C, FLAIR, and T2 images from every timepoint across all patients were used as input for tumor segmentation, using BraTS, to segment contrast enhancement (CE), FLAIR hyperintensity (FH), and the necrotic core (NC). Volumes within each segmentation and the time between each subsequent MRI was calculated, where the first post-surgical MRI was considered time 0. Linear mixed models were fitted for each segmentation volume, where Time, Group (i.e., G2-4) and their interaction (Group*Time) were fixed effects and subject was a random effect. We found that Time and Group*Time interaction were significant predictors of FH growth (p &lt; 0.001 and 0.04, respectively); however, no group-level differences were observed in FH volumes over time. Group was the only significant predictor of CE growth (p = 0.006). Specifically, G4 growth was greater than G2 and had a trending increase than G3 (p = 0.003 and 0.08, respectively). Group, Time, and Group*Time were all significant predictors of NC growth (p = 0.04, 0.001, and 0.01, respectively). G4 NC growth was significantly greater than both G2 and G3 (both p &lt; 0.05). No difference any of these tumor segmentations growth between G2 and G3 was detected. These results further show the heterogeneity in glioma growth over time and the necessity to assess how treatment effects longitudinal tumor composition progression and overall survival.
NCMP-01. TREATMENT STRATEGIES IN PATIENTS WITH RENAL CELL CANCER AND BRAIN METASTASES
Authors: Derks Sophie , van der Meer Edgar , Joosse Arjen , de Jonge Maja , Slagter Cleo , Schouten Joost , Hoop Esther Oomen-de , Smits Marion , van den Bent Martin , Jongen Joost , van der Veldt Astrid 
Published: November 2023

Abstract

BACKGROUND
Brain metastases (BM) significantly impact quality of life and survival in patients with metastatic renal cell cancer (mRCC). Based on the International Metastatic RCC Database Consortium (IMDC) risk classification, treatment strategies for patients with mRCC vary from systemic treatment with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (VEGFR-TKIs) and/or immune checkpoint inhibitors (ICIs) to active surveillance (AS). Here, we describe the impact of treatment strategies on survival in patients with RCC and BM.

METHODS
We conducted a single-center, retrospective cohort study at Erasmus MC Cancer Institute (Rotterdam, Netherlands). All consecutive patients with RCC BM between 2011-2022 were included. Primary outcome was the BM-free survival, i.e. the time between diagnosis of extracranial metastases (ECM) and BM. Secondary outcome was overall survival (OS).

RESULTS
46 patients with RCC BM were included: 25 (54.3%) had received prior systemic treatment for ECM, and 21 (45.7%) were treatment naive at diagnosis of BM. IMDC scores (favorable 12% resp. 0%, intermediate 48% resp. 64%, poor 24% resp. 0%, unknown 16% resp. 36%) were not significantly different (p=0.07) between these groups. In 39 (84.8%) of 46 patients, BM were metachronous ( &gt;1 month) after ECM. In these patients (n=39), the median BM-free survival was 22.3 (IQR 5.7-46.7) months and was significantly longer (p=0.02) in previously treated patients (29.0 [IQR 12.6-57.0] months) as compared to treatment naive patients (6.8 [IQR 1.0-7.0] months). However, median OS since ECM diagnosis (39.1 [IQR 16.5-111.5] months) was not significantly different (p=0.62) between previously treated and treatment naive patients.

CONCLUSION
Patients with RCC who received systemic treatment for ECM prior to BM diagnosis, had a longer BM-free survival as compared to patients without previous systemic treatment. These results emphasize the need for careful evaluation of AS in patients with mRCC, even in patients with a favorable prognostic profile.