bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-51. THE CENTRALLY RESTRICTED DIFFUSION SIGN FOR DIFFERENTIATION BETWEEN TREATMENT-RELATED LESIONS AND TUMOR PROGRESSION IN GLIOMA PATIENTS: A VALIDATION STUDY
Authors: Alcaide-Leon Paula , Cluceru Julia , Luks Tracy , Lupo Janine , Villanueva-Meyer Javier 
Published: November 2019

Abstract

BACKGROUND
The aim of the study was to compare the diagnostic accuracy of different diffusion patterns for differentiation between treatment-related lesions and true tumor progression in patients with suspected glioma recurrence.

METHODS
A retrospective analysis of a prospective cohort was conducted. The images of 240 consecutive patients who underwent surgery for suspected glioma recurrence were reviewed for potential inclusion. Exclusion criteria were lack of a ring-enhancing lesion and presence of susceptibility artifact. Final diagnosis was established upon review of histopathology reports. Lesions showing treatment-related changes and less than 25% of viable tumor were considered treatment-related lesions. More than 25% of viable tumor was considered recurrent tumor. A neuroradiologist, blinded to the diagnosis, evaluated the diffusion patterns on preoperative MRI. ROC curve analysis was performed.

RESULTS
One hundred and ten patients were included (26 with treatment-related lesions and 84 with tumor progression). Of 110, 35 showed no reduced diffusion. Fifty patients had reduced diffusion in the solid lesion component (84%, recurrent tumor and 16%, treatment-related lesions). Twenty-five patients showed reduced diffusion within the central necrosis (48%, recurrent tumor and 52%, treatment-related lesions). Most cases with reduced diffusion in the central necrotic region showed mixed pathology with concurrent treatment effect and viable tumor. The AUC for the combined diffusion pattern (reduced diffusion in the solid lesion component vs in the necrotic region) was 0.68 (95%CI=0.55–0.81). The AUC for the traditional diffusion approach (reduced diffusion in the solid lesion component vs no reduced diffusion) was 0.59 (95%CI=0.49–070). No significant differences were found in AUC (p=0.3).

CONCLUSION
Although the existence of centrally reduced diffusion seems to be associated with the presence of treatment effect, it does not significantly increase the diagnostic accuracy of the traditional diffusion evaluation approach. This is most likely related to the high incidence of concurrent recurrent tumor and treatment effect.
NIMG-56. A MULTIMODAL 7 TELSA MRI INVESTIGATION OF LONG-TERM EFFECTS OF RADIOTHERAPY ON THE ADOLESCENT BRAIN & COGNITION
Authors: Morrison Melanie , Avadiappan Sivakami , Yuan Justin , Stoller Schuyler , Jakary Angela , Mueller Sabine , Molinaro Annette , Banerjee Anu , Hess Christopher , Braunstein Steve , Lupo Janine 
Published: November 2019

Abstract

INTRODUCTION
Radiation therapy (RT) remains an integral role in the treatment of adolescent brain tumors, despite evidence of its long-term effects including cognitive impairment, vascular injury and reduced white matter integrity. While prior studies have related vascular injury to cognitive decline and associated more severe cognitive impairment with a whole-brain versus a focal RT approach, the relationship between underlying imaging, clinical, and treatment parameters has yet to be explored in this population. In this study we used multimodal 7 Tesla MR imaging to probe RT-induced changes in the brain and identified risk factors for clinical outcome.

METHODS
Twenty-three patients (age 6–25 years) with non-supratentorial tumors treated with RT as children and 4 nonirradiated control patients (ages 13–16 years) were scanned on a 7T MRI system; eight patients underwent serial imaging 0.9–3.7 years following the first scan. Simultaneous MR-veniography and angiography, and 90-direction, dual-shell multi-band diffusion MRI were used to assess the relationships among cerebral microbleed (CMB) development, changes in arterial radii, and whole-brain white matter connectivity. A computerized cognitive battery (Cogstate) evaluated multiple domains of cognitive function. Multiple univariate and multivariate regression models with multiple comparison corrections identified risk factors.

RESULTS
Cognitive status measured via executive function and working memory tasks revealed the strongest associations with type of RT and imaging parameters. Specific risk factors for worse outcome included whole-brain RT, RT at a younger age, and time since RT. On imaging this corresponded to increased CMB burden, decreased arterial volume, and reduced global structural connectivity; intrasubject serial imaging followed these trends.

CONCLUSION
7T-MRI was highly sensitive to CMBs with cumulative incidence rates greatly exceeding prior 3T studies in this population. This work demonstrates the value of multimodal 7T-MRI in providing metrics that reflect cognitive deficits arising from RT and identifying patients who would benefit the most from cognitive rehabilitation.
NIMG-57. SERIAL HYPERPOLARIZED 13C PYRUVATE AND 1H METABOLIC IMAGING IN RECURRENT HIGH-GRADE GLIOMA
Authors: Villanueva-Meyer Javier , Autry Adam , Gordon Jeremy , Chen Hsin-Yu , Lafontaine Marisa , Chang Susan , Xu Duan , Larson Peder , Vigneron Daniel , Li Yan 
Published: November 2019

Abstract

INTRODUCTION
Hyperpolarized (HP) 13C MRI is a novel metabolic imaging that allows for real time in vivo probing of pathway-specific metabolic processes relevant to gliomas. The first human brain application reports were published recently. In this study, we characterized longitudinal dynamic and static brain metabolism changes from serial HP 13C and 1H metabolic imaging in a patient with multiply recurrent glioma.

METHODS
Eight dynamic HP 13C imaging, 1H MRI and MRSI examinations were acquired over a period of 15 months in a patient with multiply recurrent glioma that underwent malignant transformation. The patient was imaged after initial tumor debulking, after chemoradiotherapy, at first recurrence, after re-irradiation and anti-angiogentic therapy, and at second recurrence. Ratios of pyruvate-to-lactate and pyruvate-to-bicarbonate were calculated from HP 13C imaging and choline-to-NAA index (CNI) was derived from 1H MRSI. RESULTS/

DISCUSSION
Successful therapies are usually associated with a drop in pyruvate-to-lactate conversion, mediated by different mechanisms from various treatments. We identified three key findings over repeat HP 13C MRIs: 1) a marked response to therapy with a reduction in pyruvate-to-lactate conversion corresponding to associated imaging response to therapy at the primary site of disease. Following chemoradiotherapy, the volume of T2 hyperintensity decreased from 28.21cc to 15.29 cc, and the volume of metabolic abnormality (CNI >3) decreased from 27.84 cc to 10.36 cc. 2) increasing pyruvate-to-lactate conversion in a region of recurrence before morphologic signal abnormality, and 3) a reduction in pyruvate-to-lactate conversion in a recurrent lesion following anti-angiogenic therapy.

CONCLUSION
Here we show that one can reliably image glioma patients using HP 13C MRI alongside standard of care morphologic and physiologic MRI including diffusion, perfusion, and 1H MRSI. Serial HP 13C MRI can be performed in the neuro-oncologic clinical setting and pyruvate-to-lactate metabolism may be useful to monitor treatment response.
NIMG-59. ADVERSE EFFECTS OF IMAGE TILING FOR AUTOMATIC DEEP LEARNING GLIOMA SEGMENTATION IN MRI
Authors: Anthony Reina G , Thakur Siddhesh , Panchumarthy Ravi , Bakas Spyridon 
Published: November 2019

Abstract

BACKGROUND
Application of deep learning to neuro-oncology has shown promising clinically relevant results for tumor classification, localization, and segmentation. Hardware limitations, typically memory size of graphics cards, prevent magnetic resonance imaging (MRI) volumes from being processed as a whole, and hence they are divided into smaller, overlapping tiles. Deep learning algorithms (e.g., U-Net) can then be trained and applied for predictions on such tiles, followed by their combination/stitching as the final prediction for the whole volume. We investigate the hypothesis that image tiling options, such as tile placing, size, overlap, and stitching, introduce variations with adverse effects on predictions, both in terms of inconsistency and accuracy.

METHODS
We utilized the publicly available BraTS 2018 dataset of 285 baseline pre-operative MRI glioma scans, with corresponding expert tumor boundary annotations. We implemented a 3D U-Net to predict boundaries of the whole tumor extent, by virtue of the abnormal hyper-intense signal of T2-FLAIR scans.

RESULTS
Simply flipping the tile horizontally, or translating it by one voxel, produces different predictions. Use of small tiles (64x64x64 voxels) yields substantially more false positive predictions than when using larger tile size (i.e., 128x128x128 voxels). Overlapping tiles produce conflicting predictions, leading to ambiguous interpretations upon their stitching. In areas of overlapping tiles, rounding followed by averaging the overlapping predictions produce superior results to the inverse sequence. All these are particularly noticeable in the margins of the abnormal signal and in areas of large contrast variation.

CONCLUSIONS
Although tiling is a workaround for hardware limitations, it introduces variations detrimental to accuracy. Tiling of neuro-oncology scans for computational analysis using deep learning leads to non-generalizable, non-reproducible results, thereby affecting the performance and potential clinical translatability of such algorithms. Careful considerations and standardization recommendations should be established and appropriately documented for performing such analyses, in order to avoid misinterpretation of results.
NIMG-61. USING MACHINE LEARNING TO BUILD RADIOMICS MODELS THAT DISTINGUISH REGIONS OF GLIOBLASTOMA RECURRENCE VS TUMOR PROGRESSION ON MRI
Authors: Yoon Hyunsoo , Hawkins-Daarud Andrea , Save Akshay , Singleton Kyle , Clark-Swanson Kamala , Wang Lujia , Bendok Bernard , Mrugala Maciej , Wu Teresa , Bruce Jeffrey , Hu Leland , Li Jing , Canoll Peter D , Swanson Kristin 
Published: November 2019

Abstract
Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.
NIMG-62. ATYPICAL IMAGING CHARACTERISTICS OF PRIMARY CNS LYMPHOMA AT INITIAL PRESENTATION LEADS TO FREQUENT MISDIAGNOSIS AND DELAYS IN DIAGNOSIS
Authors: Barbaro Marissa , Pan Peter , Thakur Kiran , Welch Mary 
Published: November 2019

Abstract

BACKGROUND
Primary CNS lymphoma (PCNSL) has protean appearances on magnetic resonance imaging (MRI) that may lead to delays in diagnosis.

METHODS
We retrospectively reviewed histologically-confirmed PCNSL at Columbia University Irving Medical Center (CUIMC, 2010–2019), and characterize imaging features on pre-treatment MRI scans.

RESULTS
64 patients were analyzed. 61 of 64 (95%) presented with enhancement. 35 of 64 (55%) were multiply enhancing, and 26 of 64 (41%) were singly enhancing (of which 2 were dural-based). 3 of 64 (5%) were non-enhancing. 42 of 59 (71%) had diffusion restriction. 36 of 49 (73%) lacked susceptibility. 40 of 64 (63%) were periventricular. 28 of 64 (43%) had callosal involvement. In 14 of 54 (26%), lymphoma was either not included in the differential or specifically noted less likely in radiographic report – this radiographically misdiagnosed group was significantly more likely to be either non-enhancing or non-periventricular (p=0.026). Furthermore, radiographic misdiagnosis was associated with an increased risk of a more than 14-day delay from the initial MRI to the initial invasive study, either lumbar puncture or brain biopsy (p=0.04). Presentation with a single enhancing lesion, on the other hand, was associated with significantly faster time to diagnosis – median 4.6 days (IQR 3) vs 21.6 days (IQR 4.5) from initial MRI (p=0.04).

CONCLUSION
In PCNSL, imaging characteristics influence outcomes. While a classic single enhancing lesion is associated with rapid diagnosis, non-enhancing and non-periventricular disease are the most likely to be misdiagnosed and require a heightened index of suspicion to avoid delays to diagnosis.
NIMG-68. FEDERATED LEARNING IN NEURO-ONCOLOGY FOR MULTI-INSTITUTIONAL COLLABORATIONS WITHOUT SHARING PATIENT DATA
Authors: Sheller Micah , Edwards Brandon , Anthony Reina G , Martin Jason , Bakas Spyridon 
Published: November 2019

Abstract

BACKGROUND
Training deep learning algorithms requires large amounts of data, which is a significant challenge in the medical domain, and particularly in neuro-oncology, where ample data can only be found in multi-institutional collaborations. The current paradigm for multi-institutional collaborations is based on pooled datasets that has always faced privacy, legal, technical, and data-ownership concerns. In this study we evaluate the hypothesis that federated learning can provide a method to overcome these concerns and facilitate a shift in the paradigm of multi-institutional collaborations without sharing patient data. We attempt to investigate this hypothesis in a feasibility study of automatically delineating the glioblastoma extent in T2-FLAIR scans.

METHODS
We identified a retrospective cohort of 165 glioblastoma patients with available clinically acquired pre-operative multi-parametric structural MRI (mpMRI) scans (i.e., T1, T1Gd, T2, T2-FLAIR), with corresponding expert tumor boundary annotations, from 10 independent institutions. We implemented a 3D deep learning algorithm (3D-UNet) to predict the boundaries of the whole tumor extent, by virtue of the abnormal hyper-intense signal of T2-FLAIR scans. We compare the performance of this 3D-UNet model resulting from federated learning with the performance of the same 3D-UNet model generated by sharing data to a single location where centralized/traditional training occurs.

RESULTS
Our quantitative results on federated learning (Dice:85.2%) across individual contributions from the 10 institutions demonstrate final model quality reaching 99% of the model quality achieved by sharing data (Dice:86.2%).

CONCLUSIONS
Translation and adoption of federated learning in a clinical configuration for multi-institutional collaborations is expected to have a catalytic impact towards precision and personalized medicine. The performance of computer-aided analytics and assistive diagnostics is expected to see a precipitous rise, as new models are trained on datasets of unprecedented size through such data-private collaborations.
NIMG-71. DETECTION OF CYSTIC GLIOBLASTOMA FROM MAGNETIC RESONANCE IMAGING USING DEEP LEARNING TECHNIQUES
Authors: Ranjbar Sara , Curtin Lee , Whitmire Paula , Hu Leland , Swanson Kristin 
Published: November 2019

Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor with an average survival of 15 months with standard of care treatment. GBM patients typically present with necrosis surrounded by enhancement on T1-weighted post gadolinium magnetic resonance imaging (T1gd MRI), however some patients present with a significant cystic component. Cysts are caused by different underlying biological mechanisms to necrosis and are important to identify for future clinical investigations. These cystic components can be manually identified through MRI but this process can be time consuming for large patient cohorts. Over the last two decades, our lab has collected serial MRI data of brain tumor patients. With over 70,000 images now in the database and that number increasing daily, it is clear that we have a unique resource for clinical investigation and a need to automate this process. To this end, the aim of this work was to develop and assess the performance of a convolution neural network (CNN) model for automatic detection of cystic GBMs. In this retrospective IRB-approved work, we collected pretreatment MRIs of a patient cohort consisting of 85 patients with a significant cystic component at presentation along with 400 non-cystic GBM, both identified manually through MRI. Image slices with a view of the cystic component were used as positive samples for training. Data were randomly split into training, validation, and test sets using a 70:15:15 ratio. The proportion of positive to negative cases was comparable between sets. Prior to training, we used image augmentation techniques to compensate for the class imbalance in our data. Our results showed that deep learning networks can automatically detect cystic GBMs on MRIs with high accuracy and thus illustrates the potential use of this technique in clinically relevant settings.
NCMP-12. DISSECTING THE ANTI-TUMOR IMMUNE RESPONSE IN GLIOBLASTOMA
Authors: Chen Dan 
Published: November 2019

Abstract
Glioblastoma multiforme (GBM) is the deadliest form of brain cancer, for which there is currently no effective therapy. In clinical trials with GBM patients, immunotherapy led to an increase in the number of immune cells infiltrating the tumor, yet surprisingly not to a clinical benefit. These trials reveal the need to study how the immune system is impacted by GBM and which immune cells are the key players in the anti-tumor response. Using a GBM mouse model to guide our studies, we have found anti-CTLA4 treatment improved survival, but the efficacy of anti-CTLA4 was lost in mice that were depleted of CD4+ T cells but no other immune cells, indicating ‘helper’ CD4+ T cells were essential for anti-tumor immunity and long-term survival in GBM. Thus, it is imperative to explore the types of CD4+ T cells in these tumors helping to controlling GBM and response to anti-CTLA4. We also find some specific myeloid population is disappeared after CD4 depletion. We need to understand how the function of CD4 T cells impacts other immune cells in the context of GBM. Furthermore, the future goal of this work is to extend to characterize the immune compartment of clinical GBM, which will provide important implications for how to effectively improve current immunotherapy to overcome GBM.
NCMP-13. BRAIN MRI FINDINGS IN PATIENTS TREATED WITH CHIMERIC ANTIGEN RECEPTOR T-CELLS (CAR-T) DURING SEVERE NEUROTOXICITY
Authors: Mokhtari Sepideh , Arrington John , Asquith Justin , Bachmeier Christina , Locke Frederick , Lazaryan Aleksandr 
Published: November 2019

Abstract

INTRODUCTION
Severe neurotoxicity occurs in ~30% of Diffuse Large B Cell Lymphoma (DLBCL) patients treated with CAR-T cell therapy. Brain MRI is used to assess and manage patients during neurotoxicity. Brain MRI can help understand which brain locations are affected during neurotoxicity. However, in majority of patients brain MRI is normal. It is unclear if the MRI findings are time dependent. Here we review our institutional experience with MRI findings.

METHOD
We reviewed MRI brain of 38 patients who received CAR-T at Moffitt Cancer Center from October, 2017 to March, 2019.

RESULTS
We identified 8 patients (21%) who had abnormal MRI findings. Five (63%) of these patients showed increased T2 FLAIR changes in bilateral mesial temporal lobes consistent with limbic encephalitis. One patient showed restricted diffusion and abnormal T2 FLAIR and T2 weighted images within the splenium of the corpus callosum plus increased T2 FLAIR changes in bilateral mesial temporal lobe, which resolved with follow up MRI in a month. One patient showed increased FLAIR signal intensity in left lateral cerebellum and cerebellar vermis. Two patients showed multiple new tiny areas of acute ischemic changes post CAR-T. One patient had several small and subtle areas of increased FLAIR signal intensity in bilateral frontal and parietal cortex on day 5 post CAR-T and multiple tiny areas of acute ischemia on day 10 post CAR-T. The median time to performing brain MRI was 8 days (range: 5–14).

CONCLUSIONS
MRI can show T2 FLAIR changes consistent with limbic encephalitis mostly in temporal lobes but also in frontal, parietal lobes, and cerebellum. The MRI changes can happen anytime from day 5 to day 14 post CAR-T infusion. We need larger, controlled studies to have better understanding of MRI findings and whether they are time dependent in relation to when CAR-T was given.
PDCT-05. FINAL RESULTS OF THE VINILO OPEN-LABEL PHASE II RANDOMIZED TRIAL FOR PEDIATRIC LOW-GRADE GLIOMAS (pLGG) COMPARING THE COMBINATION VINBLASTINE-NILOTINIB WITH VINBLASTINE ALONE
Authors: Grill Jacques , Brard Caroline , Picton Sue , Cruz Ofelia , Schouten-vanMetteren A Y N , Marie Sehested Astrid , Gerber Nicolas , Entz-Werle Natacha , Le Deley Marie-Cécile , Dangouloff-Ros Volodia , Varlet Pascale 
Published: November 2019

Abstract
Chemotherapy is the mainstay of non-surgical treatment in pLGG but many patients progress again after the end of the first treatment. Apart from the standard carboplatin-vincristine, new regimens with less toxicity are therefore desirable. Single agent vinblastine is an established regimen. PDGFRA TKI inhibitors have shown efficacy in refractory pLGG as well. The VINILO phase I showed the feasibility of the combination of vinblastine with nilotinib at the expense of a 50% decrease of the dose of vinblastine. The phase II trial therefore compared vinblastine 6 mg/m2 weekly (standard arm) to vinblastine 3mg/m2 weekly plus nilotinib 230mg/m2/day. The primary endpoint was the PFS, analysed on the intention-to-treat population. The target sample size was 120 patients. Accrual was stopped after recruitment of 109 patients (53 and 56 in the vinblastine-arm and the vinblastine+nilotinib arm, respectively) between July 2016 and April 2019. Fifty-four patients had an optic pathway glioma and 45 had NF1 (these patients were allowed to enter the trial as initial therapy). Half of the patients were treated after more than one line of therapy. The planned interim analysis showed that the vinblastine+nilotinib arm was associated with a worse PFS as compared to the standard arm (HR=2.37; 95%CI, 1.26–4.46; p=0.007; with 2-year PFS of 28% versus 49%). Overall, 156 biological adverse events (AE) of grade ≥ 3 have been reported after randomization (94 in vinblastine alone arm, 62 in vinblastine+nilotinib arm) and 84 non-biological adverse events of grade ≥ 3 (50 in vinblastine alone arm, 34 in vinblastine+nilotinib arm). We conclude that the combination of vinblastine plus nilotinib was less effective than vinblastine alone, possibly because of a lower dose intensity of vinblastine in the experimental arm. Vinblastine can serve as a backbone for combinations but lowering its dose may jeopardize the efficacy.