bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-33. CHANGES IN MAGNETIC RESONANCE IMAGING AFTER PROTON BOOST THERAPY FOR GLIOBLASTOMA WITH AND WITHOUT TUMOR TREATING FIELDS THERAPY
Authors: Goett Hanna , Jensen Alexandra , Struffert Tobias , Uhl Eberhard , Stein Marco 
Published: December 2020

Abstract

BACKGROUND
Tumor treating fields (TTFields) are an approved glioblastoma (GBM) treatment modality that demonstrated a significant improved median overall survival in newly diagnosed GBM patients. Data about morphologic changes in serial magnetic resonance imaging (MRI) for patients with a combination therapy of TTFields and proton boost therapy does not exist.

METHODS
Twenty-two patients were included in this study. All patients were treated with initial tumor resection followed by combined chemo- and radiation therapy. Radiation therapy was performed with 50.0 Gy photons and a proton boost with 10 Gy equivalent (Gy(RBE)). 11 patients were additionally treated with TTFields.

RESULTS
A new increase in contrast enhancement and/or a progress in the T2 FLAIR hyperintensity was observed in 54.5% (N=12) at 3 months and in 31.8% (N=7) at 6 months. No differences were observed between patients with and without TTFields therapy at 3 months [63.6% (N=7) vs. 45.5% (N=5); P=0.392] and at 6 months [27.3 (N=3) vs. 36.3% (N=4); P=0.647). By the RANO criteria a progressive disease (PD) was observed in 6 patients (27.3%) at 3 months and in 7 patients (31.8%) at 6 months. Pseudoprogression (PP) was observed in in 36.4% (N=8) at months and in 27.3% (N=6) at 6 months. Neither for PD at 3 months [36.4% (N=4) vs. 18.2% (N=2); P=0.338] or at 6 months [36.4% (N=4) vs. 27.3% (N=3); P=0.647), nor for PP at 3 months [45.5% (N=5) vs. 27.2% (N=3); P=0.375] or at 6 months [18.2% (N=2) vs. 36.4% (N=4); P=0.338] differences for patients with and without TTFields therapy were found.

CONCLUSION
Increased contrast enhancement and/or increased T2 FLAIR MRI hyperintensity after proton boost therapy are common. Furthermore, the rates for new contrast enhancement, PD, and PP after photon therapy with and without additional TTFields therapy are comparable.
NIMG-36. AUTOMATIC STRATIFICATION OF ENHANCING AND NON-ENHANCING GLIOMAS INTO GENETIC SUBTYPES USING DEEP NEURAL NETWORKS AND DIFFUSION-WEIGHTED IMAGING
Authors: Cluceru Julia , Alcaide-Leon Paula , Interian Yannet , Pedoia Valentina , Phillips Joanna , Nair Devika , Luks Tracy , Villanueva-Meyer Javier , Lupo Janine 
Published: December 2020

Abstract

INTRODUCTION
Current WHO guidelines emphasize classification of diffuse gliomas by genetic alterations into three subgroups: 1) IDH-wildtype; 2) IDH-mutant, 1p/19q-codeleted; and 3) IDH-mutant, 1p/19q-non-codeleted. Non-invasive genetic characterization can benefit patients with inoperable lesions or who are administered molecularly-targeted therapy before surgery. Prior studies that use anatomical images and convolutional neural networks (CNNs) to distinguish either IDH-mutant from IDH-wildtype tumors, or 1p/19q-codeleted from non-codeleted tumors have resulted in misclassification of nonenhancing IDH-wildtype and enhancing IDH-mutant tumors. This study investigated the benefit of a priori separation of enhancing from nonenhancing lesions and the inclusion of ADC maps from diffusion MRI to genetic subgroup classification.

METHODS
3D T2-weighted, T2-FLAIR, and post-contrast T1-weighted images were acquired preoperatively from 254 patients with newly-diagnosed gliomas. IDH1R132H mutations[VJ1] [CJ2], 1p19q-codeletions, ATRX alterations, and p53 mutations were assessed from the resected tissue to determine subtype stratification: IDH-wildtype (n=95), IDH-mutant, 1p/19q-codeleted (n=62), and IDH-mutant, non-codeleted (n=97). 3-channel input images were constructed for each patient using T2-FLAIR, T1-post-contrast, and either T2-weighted or ADC images. Three VGG-16 CNNs pre-trained on ImageNet were re-trained for: 1) lesions without enhancement, 2) enhancing lesions, and 3) all lesions together[VJ3].

RESULTS
A network trained on only enhancing lesions predicted the IDH-wildtype subtype with the highest class accuracy (ADC 94%, T2-weighted 100%) compared to using all lesions combined (ADC 90%, T2-weighted 90%). Models trained using non-enhancing lesions and ADC yielded the highest accuracy classifying 1p/19q-codeleted/non-codeleted subgroups (87%/90% for the non-enhancing network vs 83%/81% for combined network).

CONCLUSIONS
Our results support a strategy that first considers whether a lesion is enhancing when predicting molecular subgroup and includes ADC if the lesion is non-enhancing. Analysis is underway to test this model framework on independent TCIA data.
NIMG-37. DELAYED PSEUDOPROGRESSION IN TWO PATIENTS UNDERGOING TTFIELDS TREATMENT FOR NEWLY DIAGNOSED GLIOBLASTOMA
Authors: Lowman Allison , Hurrell Sarah , Bobholz Samuel , Connelly Jennifer , Cochran Elizabeth , Mueller Wade , McGarry Sean , Brehler Michael , LaViolette Peter 
Published: December 2020

Abstract

PURPOSE
Tumor treatment fields (TTFields) are approved by the FDA for newly diagnosed as well as recurrent glioblastoma (GBM). TTFields have been shown to extend survival by 4.9 months in newly diagnosed GBM, and a landmark overall survival rate of 13% at 5 years. However, the specific effects remain widely unknown, which has prevented widespread clinical use of this treatment.

METHODS
This case study examines two glioblastoma patients, IDH-1 wildtype, MGMT unmethylated, that received TTFields (Optune) in addition to maintenance temozolomide (TMZ) following radiation (RT). Both cases were followed using standard MR imaging. Second resections were performed due to radiographic progression of contrast enhancement.

RESULTS
Although imaging was concerning for tumor progression, pathology showed only treatment effect, ultimately leading to the diagnosis of pseudoprogression. Both patients fell outside the normal expected timeline for chemo-radiation induced pseudoprogression. Based on the pathology, both patients resumed treatment with TMZ/TTFields. One patient expired at 25 months and one is still alive.

CONCLUSIONS
Pathologic confirmation was essential for guiding further treatment and allowed patients to continue treatment that was effective despite contrary indications on imaging. These findings suggest that pathological confirmation should be strongly considered in patients receiving TMZ/TTFields who develop radiographic progression, potentially with a less invasive biopsy procedure. Future studies should look to characterize the underlying mechanism of interaction between TTFields/TMZ and quantify the prevalence, associated risk factors and whether there is a genotype more susceptible. Both patients reported here had O(6)-methylguanine-DNA methyltransferase (MGMT) unmethylated GBM, and while about 60% of glioblastomas are diagnosed likewise, it is possible that MGMT methylation status plays a role in TTFields response. Better characterization of this phenomenon will improve treatment guidance, potentially reducing unnecessary surgeries and the discontinuance of successful therapies.
NIMG-38. NON-INVASIVE PREDICTION OF MGMT PROMOTER METHYLATION USING COMBINED FET PET/MRI RADIOMICS
Authors: Lohmann Philipp , Meissner Anna-Katharina , Werner Jan-Michael , Stoffels Gabriele , Kocher Martin , Bauer Elena , Fink Gereon , Shah Nadim , Langen Karl-Josef , Galldiks Norbert 
Published: December 2020

Abstract

BACKGROUND
Recently, the Response Assessment in Neuro-Oncology (RANO) Working Group emphasized the additional diagnostic value of amino acid PET in addition to MRI. However, the number of studies using amino acid PET/MRI radiomics is still low. We investigated the potential of combined O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET/MRI radiomics for the non-invasive prediction of the O6-methylguanine-DNA methyl-transferase (MGMT) promoter methylation status in glioma patients.

METHODS
Seventy-one patients with newly diagnosed glioma (predominantly WHO grade III and IV glioma, 82%) underwent a hybrid FET PET/MRI scan. Forty-six patients (65%) had a methylated MGMT promoter. The tumor and tumor subregions were manually segmented on conventional MRI. In total, 199 standardized features were obtained from FET PET, contrast-enhanced T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) MRI. After feature extraction and data normalization, patients were randomly assigned to a training and a test dataset for final model evaluation in a ratio of 70/30, with a balanced distribution of the MGMT promoter methylation status. Feature selection was performed by recursive feature elimination using random forest regressors. For the final model generation, the number of features was limited to seven to avoid data overfitting. Different algorithms for model generation were compared, and the model performance in the training data was assessed by 5-fold cross-validation. Finally, the best performing models were applied to the test dataset to evaluate the robustness of the models.

RESULTS
In the test dataset, the best radiomics signatures obtained from MRI or FET PET alone achieved diagnostic accuracies for the prediction of the MGMT promoter methylation of 64% and 70%, respectively. In contrast, the highest diagnostic accuracy of 83% was obtained by combining FET PET and MRI features.

CONCLUSION
Combined FET PET/MRI radiomics allows the non-invasive prediction of the MGMT promoter methylation status in patients with gliomas, providing more diagnostic information than either modality alone.
NIMG-44. INTEGRATING AUTOMATED LESION SEGMENTATIONS FROM SINGLE-IMAGES INTO ROUTINE CLINICAL WORKFLOW FOR VOLUMETRIC RESPONSE ASSESSMENT
Authors: Villanueva-Meyer Javier , Damasceno Pablo , LaFontaine Marisa , Hawkins James , Luks Tracy , Crane Jason , Lupo Janine 
Published: December 2020

Abstract

INTRODUCTION
Volume calculations have not been adopted into glioma response assessment due to lengthy times for manual definition and unreliable measures provided by automated algorithms. Relatively new artificial intelligence approaches such as convolutional neural networks have significantly improved lesion segmentation with performance accuracies &gt;90%. However, their adoption into routine practice remains limited due to poor generalizability and failure rates approaching 25% when incorporated into clinical workflow. The latter can be attributed to 1) the requirement of four different types of anatomic images (T2, T2-FLAIR, T1 pre- and post-contrast); 2) cumbersome preprocessing including alignment, reformatting, and skull removal; and 3) the lack of a well-integrated clinical deployment system. The goal of this study was to demonstrate how simple modifications to a robust network coupled with an integrated workflow can provide reliable measures of tumor volume for real-time use in the reading room.

METHODS
Leveraging NVIDIA’s Clara-Train software and a molecularly diverse dataset of 400 labeled images for training, we modified a top-performing ensembled 2D-U-Net to require a single image-volume input (T2-FLAIR or post-contrast T1 for the T2-hyperintense or contrast-enhancing lesions) and deployed the results in the clinic to provide quantitative volumetrics. Inference was performed on a mix of image orientations without any reformatting or skull-stripping.

RESULTS
Training on only 115 of our 400 datasets, we achieved Dice Coefficients of 90% and 81% overlap of our auto-segmented T2 and contrast-enhancing lesions with manual labels in our 25-patient validation cohort (11 enhancing), compared to 91% and 83% overlap with the original model that required four anatomic images to segment each lesion. Radiologists can view segmentations directly from PACS as contours or overlays and provide numerical feedback for model refinement. The workflow has been applied on 50 cases to date without any failures and can be easily shared for deployment on any clinical PACS.
NIMG-48. USE OF MOBILE LIPIDS AS METABOLIC MARKERS FOR THE ASSESSMENT OF TREATMENT-INDUCED NECROSIS IN A RECURRENT GLIOBLASTOMA PATIENT TREATED WITH A NEW OSCILLATING MAGNETIC FIELD GENERATING DEVICE
Authors: Pichumani Kumar , Ijare Omkar , Sharpe Martyn , Nguyen Lisa , Helekar Santosh , Baskin David 
Published: December 2020

Abstract
The FDA has approved tumor treating fields (Optune) to treat glioblastoma (GBM) and unresectable mesothelioma. Optune uses scalp electrodes to pass a strong alternating electric field through the head, slowing GBM growth. We have recently developed and tested a novel device that uses an oscillating magnetic field (OMF) generated by rotating magnets to treat GBM. Our first GBM patient had a recurrent GBM with leptomeningeal spread. A custom-made Oncomagnetic device consisting of a multi-sport helmet with three OMF generators called oncoscillators, was worn by the patient at home. Changes in the brain and GBM tumor metabolism were examined using in vivo1H MRS on a 7T Siemens MRI scanner. 1H MRS data of the “tumor” and “non-tumor” regions during OMF treatment was obtained using sLASER pulse sequences. Both non–tumor and tumor regions showed the most common brain metabolites – myo-inositol, creatine, choline, glutamine/glutamate, and N-acetyl aspartate (NAA). 30 days after initiation of treatment, choline, creatine and NAA levels in the shrunken tumor region had collapsed. The tumor region displayed broad proton signals centered at 1.30 ppm and 0.88 ppm. These peaks have been observed following successful brain tumor radiation therapy and have been attributed to the presence of mobile lipids due to tumor necrosis. Our in vitro experiments suggest OMF-induced damage to mitochondria and elevated levels of reactive oxygen species, leading to cancer cell death via apoptosis and necrosis. We propose that loss of choline and elevation of lipid levels may be used as a metabolite ‘fingerprint’ to noninvasively assess the Oncomagnetic treatment response. This powerful new treatment holds great promise for treatment of patients with GBM, and produces rapid changes in brain chemistry during treatment.
NIMG-51. CONVENTIONAL MRI RADIOMIC FEATURES IMPROVE PROGNOSTICATION AND ARE PREDICTIVE OF H3 K27M STATUS IN DIPG
Authors: Tinkle Christopher , Hsu Chih-Yang , Simpson Edward , Chiang Jason , Li Xiaoyu , Armstrong Jesse , Soike Michael , Young Michael , Lu Zhaohua , Gajjar Amar , Merchant Thomas , Broniscer Alberto , Baker Suzanne , Patay Zoltan , Lucas John 
Published: December 2020

Abstract

BACKGROUND
Genomic profiling of DIPG suggests distinct and clinically relevant molecular subgroups based on the presence and isoform of histone H3 K27M mutation. We evaluated the impact of radiomic features on the classification and prognostication of 81 histologically confirmed and centrally reviewed DIPG.

METHODS
We utilized a combination of manual and automatic segmentation (DeepMedic) to define tumor volume and Pyradiomics for computation of radiomic features. Imaging feature stability was assessed by calculating concordance correlation coefficient (CCC) for each radiomic parameter from two separate pretreatment MRIs. Bootstrapped least absolute shrinkage and selection operator (LASSO) was used for feature selection. Classification and prognostication models, incorporating H3 status and clinical variables, were developed using random forest, Cox proportional hazards, and deep learning algorithms and assessed for goodness of fit using the c-index.

RESULTS
Eighty of 386 imaging features demonstrated stability (CCC, p&lt; 0.001) between pretreatment scans. LASSO identified 26 prognostic imaging features and 38 and 57 imaging features predictive of the presence of H3 K27M mutation and H3 K27M isoforms, respectively. Using five-fold cross validation, the accuracy of distinguishing H3 K27M mutant and WT tumors was 85% and 77% for H3.3 K27M, H3.1 K27M, and WT tumors. C-index for prognostication was 0.77 for Cox, 0.55 for random forest, and 0.72 for deep learning. All models were more predictive than the Jansen survival prediction model.

CONCLUSIONS
Stable, predictive radiomic features may be a surrogate for H3 status and enhance current prognostication of DIPG. Model validation in cohorts of prospectively treated patients with DIPG is ongoing.
NIMG-54. DIFFUSE TUMOR GROWTH PATTERN IS ASSOCIATED WITH WORSE OUTCOME ONLY IN IDH WILDTYPE BUT NOT IN IDH MUTANT GLIOMAS WHO II AND III
Authors: Weller Jonathan , Katzendobler Sophie , Gramatzki Dorothee , Wirsching Hans-Georg , Pangalu Athina , Biczok Annamaria , Ruf Viktoria , Thon Niklas , Weller Michael , Tonn Joerg-Christian , Suchorska Bogdana 
Published: December 2020

Abstract

BACKGROUND
Magnetic resonance imaging (MRI) based characterization has previously shown heterogeneity in tumor appearance according to IDH mutation status. We have recently investigated the relevance of contrast enhancement to be dependent on IDH mutation status in glioma WHO II and III. Here, we aimed at further characterizing tumor growth patterns and their prognostic value in these tumors.

METHODS
MRI and clinical data of patients with newly diagnosed glioma WHO II and III from two different centers were retrospectively reviewed. Radiological data such as localization, presence of contrast enhancement, T2-volume as well as tumor growth pattern (“diffuse” vs. “circumscript”) were obtained. Progression-free (PFS) and overall survival (OS) were determined and correlated with clinical, radiological and molecular characteristics using univariate and multivariate regression analyses.

RESULTS
390 patients were included, 69% thereof having an IDH mutation. The median T2-volume was 46.0 ml, IDH mutant tumors being larger (50.7 ml) than IDH wildtype tumors (36.0 ml), p = 0.01. A total of 172 tumors were classified as “circumscript” and 218 as “diffuse”; the majority of IDH wildtype tumors (71%) were well delineated compared to 51% in the IDH mutant group (p&lt; 0.0001). Apart from clinical parameters such as younger age, lower KPS, complete resection and delayed treatment, “circumscript” tumor growth pattern was associated with improved survival in the entire group (p = 0.016). When analyzed according to IDH mutation status, “circumscript” tumor growth pattern was significantly associated with OS and PFS (p = 0.006 and p = 0.002) in the IDH wildtype, but not in the IDH mutant group (p = 0.34 and p = 0.81).

CONCLUSION
IDH wildtype tumors present more often with a ”circumscript” growth pattern on initial T2 MRI. However, this “circumscript” growth pattern was associated with improved survival only in the IDH wiltdtype but not in the IDH mutant group.
NIMG-59. EVALUATION OF GLCM FEATURES BASED CLASSIFIERS IN DIFFERENTIATING TUMOR GRADES ON MULTI-MODAL MR IMAGES
Authors: Datta Sushmita , Riascos-Castaneda Roy , Zhu Jay-Jiguang 
Published: December 2020

Abstract

BACKGROUND
Accurate classification of tumor grade is necessary for understanding tumor development critical in patient management. Radiomic features are gaining popularity in classifying the tumors with the application of various classifiers. We evaluate five classifiers using gray-level co-occurrence matrix (GLCM) features to classify low-grade gliomas (LGG) and high-grade gliomas (HGG).

METHODS
We included high resolution multi-modal MR images from pre-operative BraTS 2019 database. The database contains a total of 335 multi-modal MR images (259 HGG and 76 LGG) with manually-corrected segmentations of tumor compartments. Sixty four three-dimensional (3D) voxel-by-voxel GLCM feature images on T2-wighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-weighted (T1) pre- and post-contrast images are computed. A total of 192 features within regions of enhanced tumor (ET), edema (ED), and non-enhanced tumor (NET) are obtained by taking averages of the GLCM features within each region. For classification, we evaluated k-nearest neighbor (kNN), learning vector quantization (LVQ), random forest classifier (RF), classification and regression trees (CART), and support vector machine (SVM) classifiers for differentiating LGG from HGG. The dataset is randomly split into ratio of 80 to 20 for training and validation. The models are trained using repeated 5-fold cross-validation. Best 10 models for each of the classifiers are selected based on accuracy by applying multiple random split.

RESULTS
The average accuracies of 10 best models selected for each of kNN, LVQ, RF, CART, and SVM classifiers are 0.88, 0.92, 1.00, 0.890, and 0.95 on training set, and 0.89, 0.88, 0.88, 0.89, and 0.90 on validation set. The performance of five classifiers on validation set is similar. The accuracy of SVM classifier is slightly higher on validation set even though the RF appears to be the best classifier on training set.

CONCLUSION
Voxel-by-voxel GLCM features help differentiate LGG and HGG with 0.89 of accuracy irrespective of the classifier.
NIMG-63. THE APPLICATION OF MRI AND TREATMENT RESPONSE ASSESSMENT MAPS (TRAMS) FOR MONITORING TREATMENT EFFECTS DURING/FOLLOWING PROTON BEAM THERAPY – INITIAL EXPERIENCE
Authors: Zach Leor , Last David , Lütgendorf-Caucig carola , Furman Orit , Gerasimov Alexandra , Daniels Dianne , Hentschel Herald , Stock Markus , Mardor Yael , Eugen Hug 
Published: December 2020

Abstract

BACKGROUND/OBJECTIVES
We have previously shown that delayed-contrast-MRI-based TRAMs enable efficient separation between tumor and treatment-effects, validated histologically, reaching 100%/93% sensitivity/PPV to active tumor. Proton beam therapy (PBT) is a highly conformal radiotherapy modality with increasing use in CNS tumors. Here, for the first time, we studied the application of standard MRI and TRAMs for monitoring patients during/following PBT.

METHODS
8 patients with gliomas (4 grade II, 3 grade III, 1 grade IV) and 9 with meningiomas (6 grade 1, 2 grade II, 1 grade III) were scanned by standard MRI and TRAMs at baseline, mid-term during PBT (treatment duration 22-30 days), end of treatment and 6/12 months thereafter. Enhancing volumes (EVs) were calculated from contrast-enhanced MRI, while blue/tumor and red/radiation-changes volumes (blueV/redVs) were calculated from the TRAMs.

RESULTS
Three gliomas showed increased (by &gt; 10%) EVs (15.2±8.7%), 3 stable and 2 decreased EVs (14.2±8.5%) during treatment. At the end of treatment 3 showed increased volumes (27.6±10.4%) and 5 decreased volumes (26.4±6.5%). One meningioma showed decreased EVs (11%) while 8 were stable during treatment. At end of treatment one showed increased EVs (22%), 5 were stable and 3 showed increased EVs (13.7±1.7%). The TRAMs showed similar trends but of varied magnitudes. In addition, the TRAMs could indicate radiation changes in two cases with increased enhancement: One glioma showed increased EVs/redVs and decreased blueVs during treatment, consistent with pseudoprogression, confirmed later by significant shrinkage of all volumes, and one meningioma with a similar pattern at 6 months.

CONCLUSIONS
Our initial results show for the first time significant changes in brain tumors volumes, during and at the end of PBT. The addition of TRAMs may suggest a new tool to assess PBT radiation effects. Interpretation of these results, comparison between tumor types/grades, and correlation with treatment planning and additional follow-up will be presented.