bluuebunny/crossref_metadata_2025_split huggingface dataset:
NCOG-63. PEDIATRIC NEURO-ONCOLOGY SURVIVOR IDENTITY: A PROJECT REACH STUDY
Authors: Cacciotti Chantel , Medeiros-Nancarrow Cheryl , Ullrich Nicole , Recklitis Christopher , Cooney Tabitha 
Published: December 2020

Abstract

BACKGROUND
The degree to which adults treated for pediatric CNS tumors identify with cancer and survivorship, and factors influencing their identification, have been understudied.

METHODS
Self-reported data was collected from Project REACH, a locally-treated childhood cancer survivor cohort. Data included demographic (age, gender, race/ethnicity, marriage, education, employment, living situation, insurance), clinical (diagnosis, treatment), neurocognitive (CCSS NCQ), quality-of-life (QOL; SF-12, FACT-G) and survivorship identity variables. Participants were ≥ 18 years old, ≥ 2 years from diagnosis, and ≥ 1 year from therapy completion.

RESULTS
132 participants; 59 males, and 118 non-Hispanic whites were included. Mean age at diagnosis and survey was 9.6 (range 0-22) and 26.7 (range 18-46) years, respectively. Treatment was diverse; 34% of participants received surgery only, 34% received surgery, chemotherapy, and radiation, and 32% received other treatment combinations. Most participants (67%) reported their cancer had “moderate,” “great,” or “total” effect on their current identity, and most (66%) thought of their diagnosis ≥ 1-2 times per month. A large proportion of survivors (83%) reported identifying as a “survivor.” Demographic and clinical variables were largely unelated to perceived effects of cancer, except for current age and QOL; 87% of survivors older than 30 reported moderate to great/total effect of cancer on their identity vs 51% of survivors age 18-21 (p=0.031). Participants reporting great/total effect on their identity had lower mental and physical health scores (SF-12) than those who reported no impact (48.5 vs 52.8, p=0.04; 42.6 vs 47.2, p =0.02 respectively).

CONCLUSION
The majority of participants report a significant impact from their childhood tumor diagnosis on their adult identity, and frequent diagnosis-related thoughts. Older survivors and those with poor QOL report greater effects on their identity. Interventions are needed to promote opportunities to make meaning of the pediatric CNS tumor experience as a way of achieving better quality of life.
NIMG-01. MRI VIRTUAL BIOPSY AND TREATMENT OF PRIMARY OR BRAIN METASTATIC TUMORS WITH TARGETED NANOBIOCONJUGATES
Authors: Ljubimov Vladimir , Patil Rameshwar , Holler Eggehard , Ljubimova Julia , Black Keith 
Published: December 2020

Abstract
Differential diagnosis of brain magnetic resonance imaging (MRI) enhancement(s) remains a significant problem, which may be difficult to resolve without biopsy that can be often dangerous or even impossible. Such MRI enhancement(s) can result from metastasis of primary tumors such as lung or breast, radiation necrosis, infections or a new primary brain tumor (glioma, meningioma). Neurological symptoms are often the same on initial presentation. METHODS: We present a noninvasive tumor-specific imaging approach “MRI virtual biopsy” by engineering new nano imaging agents (NIA) on polymalic acid polymer (PMLA) scaffold containing linear-Gd-DOTA or star-Gd-DOTA moieties for differential diagnosis of brain tumors. The strategy is that after non-invasive MRI diagnosis recognizing the brain lesion on the basis of its molecular signatures, the primary cancer or brain metastasis (BM) is suppressed by tumor-specific molecular inhibitor(s), which is structurally similar to the used NIA. Anti-TfR antibody or Angiopep-2 (AP-2) peptide used to cross blood-brain barrier (BBB) by receptor-mediated transcytosis, and targeting antibody against EFGR- or HER2-overexpressing tumors were covalently attached to PMLA to recognize the tissues of interest. Delivery of contrast agents across BBB was studied by optical imaging. MRI signals in healthy brain and tumors were quantified using 9.4-Tesla MRI system. RESULTS: High specific signal values prevailed for 3 hours for NIA, in comparison with clinical Gd (MultiHance) that declined rapidly. In newly developed double tumor xenogeneic mouse models of brain metastasis this method allowed differential diagnosis of HER2- and EGFR-expressing brain tumors. After MRI diagnosis, breast and lung cancer brain metastases were successfully treated with similar tumor-targeted nanoconjugates carrying molecular inhibitors of EGFR or HER2 instead of imaging contrast agent. The treatment resulted in significant increase of animal survival and markedly reduced immunostaining for several cancer stem cell markers. Support: NIH grants: R01 CA188743, R01 CA206220, R01 CA209921
NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING
Authors: Muragaki Yoshihiro , Matsui Yutaka , Maruyama Takashi , Nitta Masayuki , Saito Taiichi , Tsuzuki Shunsuke , Tamura Manabu , Fukuya Yasukazu , Asano Hidetsugu , Kusuda Kaori , Kawamata Takakazu , Masamune Ken 
Published: December 2020

Abstract

INTRODUCTION
It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.

METHODS
A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients.

RESULTS
The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE

PERSPECTIVE
A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.
NIMG-12. 18F-FLUORETHYLTYROSINE POSITRON EMISSION TOMOGRAPHY VERSUS MAGNETIC RESONANCE SPECTROSCOPY IN PREOPERATIVE DETECTION OF NEWLY DIAGNOSED GLIOMAS BY USING MACHINE LEARNING
Authors: Lazaridis Lazaros , Kebir Sied , Weber Manuel , Schmidt Teresa , Keyvani Kathy , Pierscianek Daniela , Stuschke Martin , Antoch Gearald , Forsting Michael , Sure Ulrich , Kleinschnitz Christoph , Herrmann Ken , Umutlu Lale , Deuschl Cornelius , Glas Martin 
Published: December 2020

Abstract

BACKGROUND
Advanced imaging techniques entered the field of neurooncology. In this analysis we compare the diagnostic potential of 18F-fluorethyltyrosine (FET) positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) in their potential to preoperatively predict certain glioma subtypes.

AIMS
Goal of this analysis ist the evaluation of FET PET and MRS regarding the preoperative prediction of glioma subtypes.

METHODS
We analyzed 33 patients with histopathologically confirmed newly diagnosed glioma. The patients received FET PET and MRS during one single preoperative diagnostic session. According to the molecular portfolio patients were subdivided in IDH wildtype glioblastoma patients (GBM), IDH wildtype WHO grade II/III glioma patients (Astro_IDHwt), IDH mutant WHO grade II/III glioma patients without 1p/19q codeletion (Astro_IDHmut) and with 1p/19q codeletion (ODG). Mean and maximum tumor-to-brain ratio (TBRmean and TBRmax), N-acetylaspartate, choline and creatine peaks were correlated with postoperative tumor diagnosis. To gain generalizable implications we subdivided the study cohort into a development and validation subcohort. A support vector machine model was fitted to the development subcohort and evaluated on the validation subcohort. Receiver operating characteristic curve served to assess model performance.

RESULTS
GBM patients had highest TBRmax and TBRmean values (mean: 3.5 and 3.8) and the ODG patients showed the second highest TBRmax and TBRmean values (mean: 2.6 and 3). The distribution of MRS markers exhibited to clear trend. The performance of glioma subtyping was comparatively low for the TBR values (AUC: 0.68) and even lower for the MRS markers (AUC: 0.60). These results are in line with preliminary investigations performed by our institute for the comparison of 11C-methionine PET with MRS in preoperative glioma subtyping.

CONCLUSIONS
FET PET and MRS bear limited potential in glioma subgrouping. However, FET PET appears to be slightly superior. Investigation in a larger cohort is required to draw definite conclusions.
NIMG-13. GENETIC, CELLULAR, AND CONNECTOMIC CHARACTERIZATION OF THE ADULT HUMAN BRAIN REGIONS COMMONLY PLAGUED BY GLIOMA
Authors: Mandal Ayan , Romero-Garcia Rafael , Hart Michael , Suckling John 
Published: December 2020

Abstract
For decades, it has been known that gliomas follow a nonrandom spatial distribution, appearing more often in some brain regions (e.g. the insula) compared to others (e.g. the occipital lobe). A better understanding of the glioma localization patterns could lend clues to the origins of these types of tumors, and consequently inform treatment targets. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by functional hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrate that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with putative cells-of-origin for glioma, neural stem cells and oligodendrocyte precursor cells, exhibited a high glioma frequency. Leveraging a human brain atlas of post-mortem gene expression, we found that gliomas were localized to brain regions enriched with expression of genes associated with chromatin organization and synaptic signaling. A set of glioma proto-oncogenes was enriched among the transcriptomic correlates of glioma distribution. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. These results add to previous literature reporting the vulnerability of hub regions to neurological disease, as well as provide support for cancer stem cell theories of glioma. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of brain dysfunction.
NIMG-17. VALIDATION OF DIFFUSION MRI AS AN IMAGING BIOMARKER FOR BEVACIZUMAB THERAPY IN RECURRENT GLIOBLASTOMA IN A RANDOMIZED PHASE III TRIAL OF BEVACIZUMAB WITH OR WITHOUT VB-111 (GLOBE)
Authors: Ellingson Benjamin , Cloughesy Timothy , Wang Chencai , Patel Kunal , Raymond Catalina , Brenner Andrew , de Groot John , Butowski Nicholas , Zach Leor , Campian Jian , Schlossman Jacob , Rizvi Shan , Cohen Yael , Lowenton-Spier Noa , Minei Tamar Rachmilewitz , Fain-Shmueli Shifra , Wen Patrick 
Published: December 2020

Abstract

BACKGROUND
Evidence from independent single center as well as multicenter phase II trials have suggested diffusion MRI is a strong predictive imaging biomarker for survival benefit in recurrent glioblastoma (rGBM) treated with anti-VEGF monotherapy, but not systemic chemotherapies or combination therapies with anti-VEGF agents. The current study builds on this body of evidence by evaluating these diffusion MR phenotypes in a large randomized phase III clinical trial.

METHODS
Patients with rGBM were enrolled in a phase III randomized (1:1), controlled trial (NCT02511405) to compare the efficacy and safety of bevacizumab (BV) versus bevacizumab in combination with ofranergene obadenovec (BV+VB-111), an anti-cancer viral therapy. In 170 patients with diffusion MRI available, pre-treatment enhancing tumor volume and ADC histogram analysis were used to phenotype patients as having high (&gt;1.24 um2/ms) or low (&lt; 1.24 um2/ms) ADCL, the mean value of the lower peak in a double Gaussian model of the ADC histogram within the contrast enhancing tumor.

RESULTS
Baseline tumor volume (P=0.3460) and ADCL (P=0.2143) did not differ between treatment arms. Univariate analysis showed that patients with high ADCL had a significant survival advantage when pooling all patients (P=0.0006), as well as when examining the BV (P=0.0159) and BV+VB-111 individually (P=0.0262). Multivariable Cox regression accounting for treatment arm, age, baseline tumor volume and ADCL identified continuous measures of tumor volume (P&lt; 0.0001; HR=1.0212) and ADCL phenotypes (P=0.0012; HR=0.5574) as independent predictors of OS.

CONCLUSION
Baseline diffusion MRI and tumor volume are independent imaging biomarkers of OS in rGBM treated with BV or BV+VB-111. Since ADCL was predictive of OS in combination BV+VB-111, results support the working hypothesis that co-administration of VB-111 with BV may block any VB-111 anti-tumor effect, whereas VB-111 monotherapy or priming may result in higher efficacy of VB-111.
NIMG-22. INTEGRATION OF A RADIOMIC SIGNATURE, CLINICAL VARIABLES AND PLASMA CELL-FREE DNA IN ADULT PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA PREDICTS PATIENT SURVIVAL AND IMPROVES DISEASE STRATIFICATION
Authors: Fathi Kazerooni Anahita , Bashyam Vishnu , Akbari Hamed , Sako Chiharu , Mamourian Elizabeth , Till Jacob , Abdalla Aseel , Yee Stephanie , Binder Zev , Nabavizadeh Seyed Ali , Carpenter Erica , Davatzikos Christos , Bagley Stephen 
Published: December 2020

Abstract

PURPOSE
We have previously demonstrated the potential role of liquid biopsy, specifically plasma cell-free DNA (cfDNA), as a non-invasive biomarker for prognostication in patients with glioblastoma. In separate prior studies, we have also developed MRI-based radiomic signatures to predict survival outcomes in glioblastoma. In this study, for the first time, we evaluated the potential of combining radiomic signatures, epidemiological and clinical variables, and plasma cfDNA quantification for upfront prediction of overall survival (OS) in patients with newly diagnosed glioblastoma.

METHODS
Quantitative radiomic features were extracted from multiparametric MRI (T1, T1Gd, T2, T2-FLAIR) scans of a discovery cohort of 505 and an independent replication cohort of 50 IDH-wildtype glioblastoma patients. For the independent replication cohort, pre-surgical plasma cfDNA was extracted and quantified. In the first stage, a radiomic signature was created for stratification of patients into categories of short (OS ≤ 6 months) and long (OS ≥ 18 months) survivors using a cross-validated XGBoost method based on the discovery cohort, which was tested independently on the replication cohort. In the second stage, the radiomic signature and clinical variables were integrated to build a second-stage signature using a cross-validated support vector machine (SVM) classifier to stratify the patients into short and long survivor categories. In the third stage, the value of the second-stage signature integrated with cfDNA concentration was assessed through a cross-validated SVM regression method.

RESULTS
The combination of radiomic, clinical, and cfDNA variables resulted in the best overall predictive accuracy, with Pearson’s correlation coefficient of 0.59 (p&lt; 0.0001) between actual and predicted OS.

CONCLUSION
In this study, we evaluated the value of combining plasma cfDNA, radiomic, and clinical variables for predicting OS, and showed that it could act as an effective non-invasive prognostic and patient stratification tool in patients with newly diagnosed glioblastoma.
NIMG-23. COMPARISON OF A RADIO-PATHOMIC MODEL VERSUS A RADIOLOGY-ONLY TUMOR SEGMENTATION MODEL FOR THE DETECTION OF INFILTRATIVE TUMOR IN GLIOMA PATIENTS
Authors: Bobholz Samuel , Lowman Allison , Connelly Jennifer , Cochran Elizabeth , Mueller Wade , McGarry Sean , Brehler Michael , Gliszinski Cassandra , Wilczynski Anna , LaViolette Peter 
Published: December 2020

Abstract
This study used large format autopsy tissue samples to compare radio-pathomic maps of brain cancer to a current tumor segmentation algorithm. We hypothesized that an MRI-based machine learning model trained with actual histology rather than radiologist annotations cellularity would 1) improve delineation between tumor and treatment effect, and 2) detect abnormal pathology beyond the contrast-enhancing tumor region. Seventeen patients with pathologically confirmed glioma were included in this study. At autopsy, 43 tissue samples were collected from 17 subjects from whole brain slices sectioned to align with the last axial MRI prior to death. Cellularity was calculated using a color deconvolution on 40X digitized H&E stained slides from the tissue samples. In-house custom software was used to align tissue samples and cellularity information to the FLAIR image using manually defined control points. The DeepMedic algorithm was trained to segment tumors using the BraTs 2017 dataset, and then applied to our patients in order to create automated tumor probability maps. An MRI-based ensemble algorithm using a 5x5 voxel searchlight (input: T1, T1C, FLAIR, ADC) was used to predict cellularity at each voxel, using tissue samples from 14 subjects as ground truth. Both models were applied to 3 withheld test subjects in order to compare tumor probability and cellularity predictions to the pathological ground truth. The mutual information between tumor probability and actual cellularity was 1X10-15, relatively low compared to the rad-path predicted cellularity (=0.16), despite the tumor prediction model accurately highlighting regions of contrast enhancement. Additionally, the radio-pathomic ensemble model correctly identify regions of hypercellularity beyond the tumor segmentation model as well as regions of within the segmented tumor area. This study demonstrates the utility of training machine learning models with pathological ground truth rather than radiologist annotations for predicting localized tumor information, particularly in the post-treatment stage.
NIMG-26. DIAGNOSIS OF PSEUDOPROGRESSION FOLLOWING RADIOTHERAPY PLUS LOMUSTINE-TEMOZOLOMIDE CHEMOTHERAPY IN NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS USING FET PET
Authors: Werner Jan-Michael , Ceccon Garry , Weller Johannes , Schaub Christina , Tscherpel Caroline , Lohmann Philipp , Bauer Elena , Schäfer Niklas , Stoffels Gabriele , Kabbasch Christoph , Baues Christian , Marnitz Simone , Fink Gereon R , Langen Karl-Josef , Herrlinger Ulrich , Galldiks Norbert 
Published: December 2020

Abstract

BACKGROUND
The CeTeG/NOA-09 trial demonstrated a significant survival benefit of radiotherapy plus lomustine-temozolomide chemotherapy in glioblastoma patients with methylated O6-methylguanine-DNA-methyltransferase (MGMT) promoter compared to standard temozolomide chemoradiation (1). However, data on pseudoprogression following treatment according to the CeTeG/NOA-09 trial is lacking. Due to the limited specificity of conventional MRI we evaluated the value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of pseudoprogression from actual tumor progression.

METHODS
Twenty-one patients with newly diagnosed IDH-wildtype glioblastoma and methylated MGMT promoter with conventional MRI findings suspicious for tumor progression according to RANO criteria (2) after radiotherapy completion were investigated using FET PET. The maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) were calculated. Pseudoprogression was diagnosed neuropathologically or clinicoradiologically (i.e, if (i) a stable clinical course, (ii) stable or improved MRI findings, and (iii) no treatment change occurred within the next six months). Diagnostic performance of FET PET parameters was evaluated using receiver-operating-characteristic (ROC) analyses and Fisher’s exact test. Imaging results were also related to progression-free survival (PFS).

RESULTS
Pseudoprogression was identified in 9 of 21 patients (43%). In the majority of patients (n=8; 89%), pseudoprogression was diagnosed clinicoradiologically. The median time between radiotherapy completion and pseudoprogression was nine weeks (range, 5-25 weeks). ROC analysis yielded an optimal cutoff value of 1.95 for TBRmean to differentiate between pseudoprogression and tumor progression (sensitivity, 89%; specificity, 75%; accuracy, 81%; area under the curve, 0.72±0.13; P = 0.008).

CONCLUSIONS
In a considerable number of patients, pseudoprogression may occur following radiotherapy plus off-study lomustine-temozolomide chemotherapy according to the CeTeG/NOA-09 trial. FET PET appears of value for the differentiation of pseudoprogression from tumor progression in this group of patients. Literature: 1. Herrlinger et al., 2019 Lancet; 2. Wen et al., 2010 J Clin Oncol
NIMG-27. THRESHOLD VALUES OF EARLY METABOLIC CHANGES IN 1H MRSI METABOLIC METRICS PREDICTIVE FOR SURVIVAL IN PATIENTS WITH NEWLY DIAGNOSED HIGH-GRADE GLIOMA
Authors: Patel Samir , Wang Michael , Wachowicz Keith , Yahya Atiyah , Murtha Albert , Amanie John , Quon Harvey , Chainey Jonathan , Ghosh Sunita , Roa Wilson 
Published: December 2020

Abstract

BACKGROUND
The purpose of this study was to evaluate the association of specific threshold values for changes in metabolic metrics measured from 1H magnetic resonance spectroscopic imaging (MRSI) to survival of patients with high-grade glioma treated with multimodality therapy.

METHODS
Forty-four patients with newly diagnosed high-grade glioma were prospectively enrolled. Serial MRI and MRS scans were performed at baseline, week 4 of radiation therapy (RT), and two months post-RT to measure tumor choline, creatine, and N-acetylaspartate (NAA) normalized to contralateral normal brain. Serial changes in metabolites were assessed using the Wilcoxon signed-rank test. Cox regression analyses adjusted for patient age, KPS, and delivery of concurrent chemotherapy were used to assess the association of changes in metabolic metrics with survival.

RESULTS
Median follow-up time was 13.4 years, and vital status was available for 95.1% of patients. Mean normalized choline, creatine and choline/creatine decreased significantly after baseline to post-RT in the overall group of patients (all P &lt; 05). Overall survival (OS) was longer in patients with ≤ 20% increase (vs. &gt; 20%) in normalized choline (P = .024) or choline/NAA (P = .024) from baseline to week 4 of RT. During this period, progression-free survival (PFS) was longer in with patients ≤ 40% increase (vs. &gt; 40%) in normalized choline (P = .013). From mid-RT to post-RT, ≤ 40% decrease (vs. &gt; 40%) in normalized choline/NAA (P &lt; .001) or choline/creatine (P = .014), or ≤ 40% increase (vs. &gt; 40%) in NAA/creatine (P = .030) were associated with reduced median OS.

CONCLUSIONS
Threshold values for serial changes in choline metrics on MRSI associated with OS and PFS were identified. These findings suggest a potential clinical role for MRSI to provide an early assessment of treatment response, and could enable risk-adapted therapy in clinical trial development and clinical practice.