bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-34. CXCR4-TARGETED PET IMAGING OF CENTRAL NERVOUS SYSTEM LYMPHOMA AND GLIOMA USING [68GA]GA-PENTIXAFOR
Authors: Werner Jan-Michael , Tomasich Erwin , Berghoff Anna S , Wöhrer Adelheid , Albert Nathalie L , Mayerhoefer Marius E , Galldiks Norbert , Traub-Weidinger Tatjana , Hacker Marcus , Preusser Matthias 
Published: November 2024

Abstract

BACKGROUND
Initial results suggest that PET imaging of the chemokine receptor CXCR4 is of considerable interest for differential diagnosis and theranostic approaches. Here, we summarized findings of CXCR4 PET imaging in an institutional series of lymphoma of the central nervous system (CNSL) and glioma.

METHODS
Twenty-three patients with CNSL and 16 patients with glioma (IDH-wildtype, n=9) underwent PET imaging using the CXCR4-directed tracer [68Ga]Ga-Pentixafor. In 18 patients, serial PET imaging was performed (range, 2-14 scans), resulting in a total of 104 PET scans. For evaluation, PET scans were classified as [68Ga]Ga-Pentixafor-positive and -negative visually, and the maximum standardized uptake value (SUVmax) was obtained from [68Ga]Ga-Pentixafor-PET.

RESULTS
Twenty-four of all 39 patients (62%) had at least one [68Ga]Ga-Pentixafor-positive PET (CNSL, 57%; glioma, 69%). [68Ga]Ga-Pentixafor-positive PET scans were more common in patients with IDH-wildtype gliomas (89%) than in IDH-mutant gliomas (43%). In patients with [68Ga]Ga-Pentixafor-positive PET, the average SUVmax was 6.4±4.0 in CNSL, and 3.4±1.0 in gliomas (P=0.155). Besides visualization of tumors, [68Ga]Ga-Pentixafor uptake was observed in two patients with radionecrosis. In four CNSL patients who had undergone methotrexate/cytarabine-based first-line therapy, serial PET revealed a significant reduction of SUVmax after chemotherapy completion (average decrease of SUVmax from 4.5±3.8 to 0.6±0.7; P=0.044). In three of those four patients, tumor progression has not been reached (range of time after the follow-up PET, 4-55 months); one patient deceased 44 months after the follow-up PET. In contrast, another patient with an increase in SUVmax from 6.9 to 11.0 after first-line treatment died one month after the follow-up PET.

DISCUSSION
[68Ga]Ga-Pentixafor PET seems to be of value for non-invasively visualizing and quantifying CXCR4 receptor binding. In CNSL, changes in [68Ga]Ga-Pentixafor uptake following therapy may indicate potential for response assessment. CXCR4-targeting radioligand therapies might be more promising in CNSL than in glioma.
NIMG-39. THE “RESECTABILITY INDEX” AS A NOVEL OBJECTIVE PREDICTOR OF SURVIVAL FOR PATIENTS WITH ADULT-TYPE DIFFUSE GLIOMAS
Authors: Nichols Noah , Seenarie Nitish , Yong Raymund 
Published: November 2024

Abstract
Extent of resection (EOR) is a strong prognostic factor for patients with adult-type diffuse gliomas. Whole brain tractography (WBT) allows visualization of tumors in relation to functional subcortical white matter tracts connecting eloquent cortex. We developed a preoperative metric, the resectability index (RI), to estimate tumor resectability based on pre-resection volume (PRV) and unresectable tumor volume (UTV). We explore if RI predicts progression-free survival (PFS) and overall survival (OS). We retrospectively reviewed the charts of consecutive patients with adult-type diffuse gliomas resected from 2013-2020. Only patients with pre-operative DTI MRIs were included. UTV was the total overlapping volume of tumor with functionally significant tracts (corticospinal, arcuate, optic, fornix) and deep structures (basal ganglia, thalamus, brainstem). RI was (PRV-UTV)/PRV. Seventy-nine patients were included with an average age of 56 years at time of surgery. Glioblastoma was the most common histology (68%). Tumors were located in the left hemisphere (LH) (48%), right hemisphere (RH) (39%), and bilateral hemispheres or midline (BHM) (13%). EOR for LH, RH, and BHM tumors was 0.8, 0.91, and 0.29, respectively (P = 0.1^7). RI for LH, RH, and BHM tumors was 0.86, 0.912, and 0.44, respectively (P = 0.00021). Cox regression was used to model PFS and OS based on age, preoperative KPS, WHO grade, EOR, and RI. Age, recurrent status, bilaterality, residual tumor volume (RTV) and UTV predicted PFS (CI = 1.01 – 1.06, P = 0.002). For OS, UTV and RI were more strongly predictive than RTV or EOR. In a Cox regression model consisting only of preoperative variables, RI was independently predictive of OS. RI is a novel metric with potential efficacy for preoperative survival prognostication in patients with diffuse gliomas. RI may enable neurosurgeons to provide HGG patients with more accurate information about expected benefits of surgical intervention than is currently possible. RI will also enable more valid comparisons of HGG patient cohorts treated at different institutions.
NIMG-43. INFARCTION RADIOLOGICALLY MIMICKING INTRA-AXIAL BRAIN TUMORS
Authors: Kim Kyung-Min 
Published: November 2024

Abstract
There are several reports of brain lesions that radiologically mimicking the brain tumors. The abscess is representative lesion of resembling the intra-axial tumors in brain MRI. However, cerebral infarction is not a common lesion that resembles a brain tumor and can be easily distinguished from a brain tumor in MRI. Here, we represented the two cases of pathologically diagnosed infarction that radiologically suspected gliomas. A 67-year-old man presented with left side weakness and memory impairment. Brain MRI revealed the rim enhancing lesion radiologically suspected glioma. Navigation guided biopsy was performed and final pathological diagnosis was subacute infarction. A-41-year-old man represented the right side weakness. MRI showed the enhancing nodular lesion with perilesional edema. Surgical excision was performed and pathological diagnosis was infarction. Although advancement of brain imaging techniques, it is difficult to accurately discriminate the intracerebral lesions utilizing the MRI. Further comprehensive study should be executed for more precise differentiation of infarction and brain tumors.
NIMG-49. IMAGING PREDICTIVE FACTORS OF BRAIN ADHESION IN SUPRATENTORIAL MENINGIOMA
Authors: Tanaka Shingo , Tamai Sho , Kinoshita Masashi , Nakada Mitsutoshi 
Published: November 2024

Abstract

OBJECTIVES
80 % of meningioma are benign and are classified as WHO grade 1. However, meningioma frequently adheres to surrounding brain and make tumor detach difficult during surgery. This study retrospectively analyzed the adhesion of meningioma to the brain based on preoperative MR images and operative findings.

METHODS
We retrospectively reviewed 238 tumor planes of 60 supratentorial meningioma cases with WHO grade 1 (parasagittal: 15, convexity: 20, falcine: 7, sphenoidal ridge 13 and frontal base 5) underwent surgical treatment. The tumor adhesion to the surrounding brain was defined during surgery as the rupture of arachnoid membrane or leaving tumor capsule due to tight adhesion. The location of MRI image was identified during surgery and verified reference to operation video postoperatively. We assessed following items, tumor diameter, T2 hyper rim, T1 hypo rim, FLAIR hypo rim and brain edema of surrounding tumor. We performed univariate analysis for each item with and without adhesion, and extracted predictors for tumor adhesion by logistic regression analysis.

RESULTS
We confirmed 127 tumor planes without adhesion and 111 tumor planes adhered to surrounding brain. The cutoff value obtained from the ROC curve for tumor adhesion and tumor diameter was 2.3 cm. In univariate analysis, tumor diameter ≧ 2.3 cm, T2 hyper rim, T1 hypo rim, FLAIR hypo rim and brain edema of surrounding tumor were significantly associated with adhesion. In logistic regression analysis, tumor diameter ≧ 2.3 cm (odds ratio (OR) 2.7, P = 0.02), T2 hyper rim (OR 0.04, P &lt; 0.001), and brain edema of surrounding tumor (OR 25, P &lt; 0.001) were significantly independent predictive factors for tumor adhesion.

CONCLUSION
Meningioma adhesion to surrounding brain might be predicted by tumor diameter ≧2.3 cm, T2 hyper rim, and brain edema of surrounding tumor.
NIMG-50. MULTIPARAMETRIC MRI DISTINGUISHES NEURONAL AND IMMUNE SIGNATURES IN INVASIVE HIGH-GRADE GLIOMA RIM THROUGH GRAPH NETWORK ANALYSIS
Authors: Flick Matthew , Stevens Victoria , Plaisier Christopher , Barajas Ramon , Tran Nhan , Kannan Kasthuri , Hu Leland 
Published: November 2024

Abstract

BACKGROUND
Standard of care surgery for high grade glioma (HGG) employs Magnetic Resonance Imaging to identify the contrast enhancing (CE) core for resection, leaving a non-enhancing (NE) invasive rim of tumor which contributes to recurrence. Multi-parametric MRI (mpMRI) has recently been used to map regional biological heterogeneity of neuronal and immune signatures in HGG, and continues to offer a promising strategy for non-invasive surveillance of tumor progression. Here, we present a graph network analysis relating mpMRI to biological signatures in the NE tumor rim and show the utility of communities of imaging features for discriminating tumor phenotypes.

METHODS
53 mpMRI features across 101 spatially-localized RNA-sequenced biopsy samples from 64 patients with IDH-wt HGG were represented as nodes within a Neo4j graph network. Modularity optimization was used to identify communities of densely connected nodes. Communities of nodes representing high proportions of NE samples (one-tailed test) were kept for further analysis. Gene set enrichment analysis (GSEA) on samples within selected imaging communities was analyzed for biological process enrichment using clusterProfiler. Single sample GSEA (ssGSEA) using ssGSEA2 was used to annotate neuronal and immune cell states across samples within selected communities. mpMRI feature selection within each community identified the most relevant imaging features for isolating samples classified into neuronal and immune cell states.

RESULTS
11 imaging communities gathered a high proportion of NE samples. Three NE communities showed significant enrichment of neuronal signatures, while one showed significant enrichment of immune pathways. One neuronal-predominant community showed unique upregulation of 14 pathways related to neuronal migration/astrocyte projection.

CONCLUSION
Network imaging communities can identify clusters of NE glioma samples showing contrasting neuronal and immune signatures, including distinct upregulation of migration and projection pathways. Mapping these imaging feature communities to specific biological processes implicated in glioma progression may aid treatment planning and prognostication for glioma patients.
NIMG-58. MACHINE LEARNING-GENERATED WHOLE-BRAIN RADIO-PATHOMIC TUMOR PROBABILITY MAPS WITHIN CONTRAST-ENHANCEMENT STRATIFY SURVIVAL IN GLIOBLASTOMA PATIENTS UNDERGOING GROSS-TOTAL RESECTION
Authors: Aaronson Daniel , Bobholz Samuel , Duenweg Savannah , Kyereme Fitzgerald , Lowman Allison , Nath Biprojit , Winiarz Aleksandra , Connelly Jennifer , Mrachek E Kelly , Krucoff Max , Banerjee Anjishnu , LaViolette Peter 
Published: November 2024

Abstract

BACKGROUND
Even for those undergoing gross total resection (GTR), patients with WHO grade 4 glioblastoma (GBM) have poor overall survival (OS). Stratifying those who will have better OS vs. those that will not remains challenging, with conventional imaging and radiomic models falling short. This study utilizes machine learning (ML)-generated radio-pathomic models based on autopsy tissue as ground-truth to enhance prognostication through tumor probability maps (TPM) within preoperative contrast-enhancing (CE) regions of GBM.

METHODS
Data from the UCSF-PDGM-v2 and UPenn-GBM databases were analyzed, focusing on patients who underwent GTR with WHO 4 IDH-wildtype glioblastoma (n = 528). Preoperative tumor cellularity within CE was quantified using ML-generated TPMs, with a median threshold of 0.7242. Kaplan-Meier survival curves were employed to compare OS between patients with low (≤ 0.7242) and high (&gt; 0.7242) TPM values in CE regions. Statistical significance was evaluated using log-rank tests.

RESULTS
Kaplan-Meier curves demonstrated significant stratification in survival outcomes based on TPM values. Patients with lower TPM values in CE regions had a median OS of 567 days, compared to 457 days for those with higher TPM values (log-rank p-value: &lt; 0.001). These findings suggest that lower tumor cellularity in CE regions as assessed by TPMs correlates with improved survival, offering a more nuanced stratification than current MRI-based methods.

CONCLUSION
This study underscores the potential of TPM metrics as a powerful prognostic tool in GBM. The significant correlation between lower TPM values in CE regions and better median OS indicates that TPM can effectively stratify patients, aiding in clinical decision-making and personalized treatment planning. Future research is necessary to validate these findings in larger, diverse patient populations and to explore the biological mechanisms underlying these survival differences.
NIMG-63. LEVERAGING LLMS FOR ACCURATE DIFFERENTIATION OF RADIATION NECROSIS AND TUMOR PROGRESSION IN BRAIN MRI REPORTS: A STUDY ON AUTOMATED SCORING AND CLINICAL IMPLICATIONS
Authors: Liu Eulanca , Goldfinger Shir , Delery William , Jank Erika , Lamb James , Tenn Stephen , Hegde John , Kaprealian Tania , Steinberg Michael , Savjani Ricky 
Published: November 2024

Abstract

BACKGROUND
Accurate differentiation between radiation necrosis and tumor progression or recurrence in patients treated with stereotactic radiosurgery for brain metastases is critical for guiding clinical management. This study leverages the advanced natural language processing capabilities of Meta Llama3, an artificial intelligence (AI) large language model (LLM), combined with prompt engineering, to rapidly categorize brain magnetic resonance imaging (MRI) radiology reports. Our objective was to develop an automated scoring system to classify concern for radiation necrosis, tumor progression, equivocal findings, or stable exams.

METHODS
Using a comprehensive dataset of reports annotated by expert radiologists, we ran inference on a 70-billion parameter Llama3 model (temperature 0.2, top_p 0.9) with specific prompts designed to capture the nuanced language and diagnostic criteria related to radiation necrosis or tumor progression.

RESULTS
The first pass was performed on a training dataset of 107 reports and did not predefine the clinical conditions. This demonstrated 43.4% accuracy in scoring when compared to a human user’s classification of each report. Agreement between the human reader and Llama3 was assessed using the Gwet agreement coefficient, AC1=0.411 (99%CI 0.396-0.426). Multiple iterations of targeted prompt engineering were then employed to narrow the definition of radiation necrosis and tumor progression, with specific examples and nuanced language used to achieve a higher degree of accuracy at 72.0%, AC1=0.719 (99%CI 0.717-0.722).

DISCUSSION/FUTURE DIRECTIONS
This surpasses recent demonstration of lower human-LLM agreement in radiographic score assignment, with further room for calibration on a dataset of several thousand reports. Next, we will correlate automated interpretations with actual clinical management decisions for radiation necrosis (e.g., initiation of steroids, bevacizumab, Laser Interstitial Thermal Therapy, and/or repeat imaging). This automated scoring system holds significant potential for LLMs in clinical applications. Future work will focus on integrating this model into clinical workflows and expanding its capabilities to include longitudinal monitoring of patient outcomes.
NIMG-65. SYNTHETIC MRI GENERATION TO FACILITATE AUTOSEGMENTATION OF PEDIATRIC BRAIN TUMORS IN LIMITED DATA SCENARIOS
Authors: Chrysochoou Dimosthenis , Gandhi Deep , Familiar Ariana , Vossough Arastoo , Storm Phillip , Resnick Adam , Davatzikos Christos , Nabavizadeh Ali , Kazerooni Anahita Fathi 
Published: November 2024

Abstract

BACKGROUND
The availability of standard multiparametric MRI sequences, including pre-contrast T1w (T1w-pre), post-contrast T1w (T1w-post), T2w, and FLAIR images, is essential for accurate segmentation of tumor subregions and subsequent response assessment in pediatric brain tumors (PBTs). However, clinical MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across different centers. This study leverages Generative Adversarial Networks (GANs) to synthesize missing FLAIR and T1w-pre images from T2w and T1w-post images, respectively, to facilitate tumor segmentation in PBTs.

METHODS
We developed two GAN models based on the pix2pix framework, trained, validated, and tested on FLAIR-T2w and T1w-post-T1w-pre pairs from a multi-histology and multi-institutional cohort of 476 subjects with PBTs from the Children’s Brain Tumor Network (CBTN). The perceptual quality of the synthesized T1w-pre and FLAIR images was evaluated using the Structural Similarity Index (SSI; ideal value = 1). The robustness of the synthesized sequences to replace missing MRI sequences was evaluated by comparing the segmentation performance (Dice score) of our pre-trained autosegmentation model using all real modalities versus replacing real sequences with their synthesized counterparts.

RESULTS
The T1w-pre and FLAIR synthesis GANs achieved mean SSI values of 0.95 and 0.93, respectively. Median Dice scores for segmentation of whole tumor (WT), enhancing tumor (ET), and tumor core (TC) were calculated for real sequences: 0.90, 0.83, 0.89, replacing real FLAIR with synthetic: 0.86, 0.81, 0.86, and replacing real T1w-pre with synthetic: 0.90, 0.75, 0.89. No statistical significance (p&gt;0.05) was observed in segmentation performance for WT, TC, ET when replacing real T1w-pre with synthetic, and for ET when replacing real with synthetic FLAIR sequences.

CONCLUSION
This method achieves robust segmentation performance in limited data scenarios by synthesizing missing MRI sequences. Future work includes improving FLAIR synthesis and segmentation performance for WT and ET by implementing and comparing other GAN architectures.
NIMG-73. FALSE POSITIVE INTRA-OPERATIVE MAGNETIC RESONANCE IMAGING DURING HIGH GRADE GLIOMA RESECTION
Authors: Kreatsoulas Daniel , George Andrew , Kolawole Samuel , Damante Mark , Cua Santino , Finger Guilherme , Elder Brad 
Published: November 2024

Abstract
Intraoperative magnetic resonance imaging (iMRI) allows assessment of extent of resection during surgery for high-grade glioma (HGG). In most cases, contrast-enhancing areas noted on iMRI indicate residual tumor. However, false positives may occur, leading to inappropriate additional resection and patient morbidity. The rate and pattern of false positive findings in iMRI have not been fully described. We analyzed surgical and imaging findings in patients undergoing iMRI during resection of HGG to determine factors linked with potential false positive contrast enhancement. A single-center analysis retrospectively assessed consecutive patients undergoing resection of newly diagnosed high-grade glioma using iMRI guidance. Preoperative, intraoperative and postoperative imaging, as well as pathologic findings were assessed. One hundred seventy-four consecutive patients who underwent surgery from 2016-2023 were included. All patients had preoperative MRI showing contrast-enhancing tumor and all underwent one iMRI during surgery to assess extent of resection. 124/174 cases (71.3%) demonstrated contrast enhancement on iMRI that prompted inspection and additional resection using updated neuronavigation. Enhancement corresponding to residual tumor was identified with or without additional removal in 120/124 cases (96.8%). No residual tumor was found in 4/124 cases (3.2%, false positives). All false positive cases were the result of contrast accumulation in hematomas (2), hemostatic agents (1) or dependent portions of the resection cavity (1). Both hematomas were located in the walls of the resection cavity and confirmed as hematoma, rather than tumor, by separate pathology analysis. False positives were not associated with patient positioning, tumor sub-type, anatomic location of tumor or extent of resection. Gross total resection (GTR) was confirmed in 163/174 (93.7%) cases on postoperative imaging. False-positive iMRI during HGG surgery can mimic residual tumor. This occurs at a low rate due to contrast pooling in the cavity or accumulation within hemostatic agents or hematomas along the cavity wall. Meticulous hemostasis when preparing the patient for iMRI may help prevent false positives and prevent unnecessary additional resection.
NIMG-74. A NOVEL APPROACH FOR PERSONALIZED RADIOTHERAPY TARGET VOLUME DEFINITION USING METABOLIC MRI AND DEEP LEARNING-DRIVEN PREDICTIONS OF GLIOBLASTOMA RECURRENCE
Authors: Tran Nate , Luks Tracy , Ellison Jacob , Li Yan , Jakary Angela , Ngan Tiffany , Liu Bo , Villanueva-Meyer Javier , Molinaro Annette , Clarke Jennifer , Butowski Nicholas , Chang Susan , Braunstein Steve , Lin Hui , Lupo Janine 
Published: November 2024

Abstract

BACKGROUND
Machine-learning models have demonstrated great promise in predicting tumor infiltration beyond anatomical margins in glioblastoma. Yet standard-of-care (SOC) radiation therapy (RT) planning only utilizes a uniform, isotropic 2cm-expansion of the T1-post-contrast-lesion or T2-lesion to generate a clinical target volume (CTV), without considering heterogeneity in tumor infiltration. We hypothesize that using a novel deep-learning approach to predict regions of tumor progression with metabolic and diffusion-weighted MRI will result in personalized CTVs that are more sensitive to detecting normal-appearing infiltrating tumor beyond the T2-lesion, sparing more healthy brain compared to SOC-CTVs.

METHODS
Anatomical, diffusion-weighted, and metabolic MRI from 101 patients with glioblastoma, acquired post-surgery but prior to chemoradiation, were retrospectively used to predict regions of new contrast-enhancement or T2-hyperintensity at confirmed progression. A segmentation-based predictive deep-learning approach with personalized loss-functions and evaluation metrics (“Progression-Coverage-Coefficient”, PCC) that weight Dice coefficient terms by tumor size was employed with a 66/17/18 train/validation/test split of patients and the resulting DL-CTV compared to two SOC-CTVs: 1) the RTOG[T2-FLAIR+2cm]-CTV, 2) the EORTC[T1c+2cm]-CTV.

RESULTS
Our best deep-learning-CTV trained using anatomical, diffusion, and metabolic MRI achieved significantly improved median test specificity (0.87 vs. 0.77; p&lt;0.005) and comparable sensitivity (0.92 vs. 0.95; p=0.1) and PCC (0.81 vs. 0.79; p=0.1) to the SOC RTOG[T2-FLAIR+2cm]-CTV, sparing more normal brain tissue. Compared to the more conservative EORTC[T1c+2cm]-CTV, our approach had significantly higher test sensitivity (0.92 vs. 0.85; p&lt;0.00001) and PCC (0.81 vs. 0.77; p=0.003) in covering the progressed lesion, while maintaining similar specificity (0.87 vs. 0.89; p=0.3). Test performance dropped by 9.6% and 12.4% when removing metabolic and both metabolic and diffusion metrics, respectively (p&lt;0.001).

CONCLUSION
This study demonstrates the benefit of using metabolic and diffusion MRI with deep learning for personalized RT planning by targeting regions of anatomically normal infiltrative disease that are highly likely to progress, while reducing treatment to normal brain.