bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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 (>1.24 um2/ms) or low (< 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< 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

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< 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

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

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

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 < 05). Overall survival (OS) was longer in patients with ≤ 20% increase (vs. > 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. > 40%) in normalized choline (P = .013). From mid-RT to post-RT, ≤ 40% decrease (vs. > 40%) in normalized choline/NAA (P < .001) or choline/creatine (P = .014), or ≤ 40% increase (vs. > 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.
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

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

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

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

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

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 >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

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

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< 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

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< 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

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

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 > 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.