bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-01. INTEROBSERVER VARIABILITY OF THE REVISED IMAGING SCORECARD FOR LEPTOMENINGEAL METASTASIS: A JOINT EORTC BRAIN TUMOR GROUP AND RANO EFFORT
Authors: Le Rhun Emilie , Devos Patrick , Winklhofer Sebastian , Lmalem Hafida , Brandsma Dieta , Pérez-Larraya Jaime Gállego , Castellano Antonella , Compter Annette , Dhermain Frederic , Franceschi Enrico , Forsyth Peter , Furtner Julia , Galldiks Norbert , Gempt Jens , Glantz Michael , Hattingen Elke , Hempel Johann Martin , Jaeckle Kurt , Kumthekar Priya , Lukacova Slavka , Minniti Giuseppe , O'Brien Barbara , Postma Tjeerd J , Roth Patrick , Rudà Roberta , Schäfer Niklas , Schmidt Nils Ole , Smits Marion , Snijders Tom , Thust Steffi , Tonn Jörg-Christian , van den Bent Martin , van den Hoorn Anouk , Vogin Guillaume , Preusser Matthias , Wen Patrick , Bendszus Martin , Weller Michael 
Published: November 2021

Abstract

BACKGROUND
Validation of the 2016 LANO MRI scorecard for leptomeningeal metastasis failed for multiple reasons. The objective of this joint EORTC Brain Tumor Group and RANO effort was to validate the feasibility of the revised MRI scorecard for assessing response in leptomeningeal metastasis.

METHODS
Coded paired cerebrospinal MRI of 20 patients with leptomeningeal metastases from solid cancers at baseline and follow-up after treatment and instructions for assessment were provided via the EORTC imaging platform. The kappa coefficient (K) was used to evaluate inter-observer pairwise agreement. Statistical analyses were performed using SAS V9.4 software (Cary, NC). The sponsor of the study was the University Hospital Zurich (2018-00192).

RESULTS
Thirty-five raters participated, including 9 neuroradiologists, 17 neurologists, 4 radiation oncologists, 3 neurosurgeons and 2 medical oncologists. Among leptomeningeal metastases-related items at baseline, the best median concordance was noted for hydrocephalus (K=0.63), and the worst median for spinal linear enhancing disease (K=0.46). The median concordance for overall response was moderate (K=0.44). Notably, the interobserver agreement for the presence of parenchymal brain metastases at baseline was minimal (K=0.29). Significant differences were observed when considering the specialty of the raters. Only 394 of 700 ratings (56%) were fully completed. Among 394 fully completed ratings, perfect concordance was noted in 293 ratings (74%) when comparing the overall response according to the guidelines provided in the scorecard and the overall response provided by the raters. The main discordances were noted for partial response according to the rater versus stable disease according to the guidelines (n=44), followed by progression according to the raters versus stable disease according to the guidelines (n=23).

CONCLUSION
Electronic case report forms with "blocking solutions" are probably required to enforce completeness and quality of scoring. These results confirm the necessity of central review and the need for training of MRI assessment in clinical trials.
NIMG-02. 18F-FLUCICLOVINE POSITRON EMISSION TOMOGRAPHY TO DISTINGUISH TUMOR PROGRESSION FROM RADIATION NECROSIS FOLLOWING STEREOTACTIC RADIOSURGERY FOR BRAIN METASTASIS: A QUALITATIVE ANALYSIS
Authors: Smile Timothy , Tom Martin , Obuchowski Nancy , DiFilippo Frank , Jones Stephen , Suh John , Murphy Erin , Yu Jennifer , Mohammadi Alireza , Barnett Gene , Huang Steve , Wu Guiyun , Johnson Scott , Ahluwalia Manmeet , Peereboom David , Stevens Glen , Chao Samuel 
Published: November 2021

Abstract

PURPOSE/OBJECTIVE(S)
To assess the ability of 18F-Fluciclovine PET/CT to distinguish radiation necrosis (RN) from tumor progression (TP) among patients with brain metastases (BM) treated with stereotactic radiosurgery (SRS) in a prospective pilot study.

MATERIALS/METHODS
Adults with post-SRS BM presenting with follow-up brain MRI equivocal for RN versus TP underwent 18F-Fluciclovine PET/CT within 30 days of equivocal MRI. PET images were reconstructed using a point-spread-function algorithm. Three physician reviewers independently performed qualitative analyses of each lesion using a three-point visual score relative to PET-avidity of blood pool and parotid. Quantitative metrics for each lesion were documented. Reference standard was clinical follow-up with brain MRI until tumor board consensus or tissue confirmation. Nonparametric estimates of area under the receiver operating characteristic curve (AUC) for clustered data were estimated, with diagnostic performance based on visual score.

RESULTS
In 15 subjects with 20 lesions, final diagnosis was RN in 16 (80%) lesions and TP in 4 (20%). Visual score significantly correlated with final diagnosis (AUC range 0.836-0.906 [p≤0.037]). A threshold score of 2 (lesion 18F-fluciclovine uptake above blood pool to parotid) and higher produced sensitivities and specificities of 75-100% and 38-56% respectively among the reviewer majority. Conversely, a threshold of 3 (uptake higher than parotid) produced sensitivities and specificities of 50-75% and 100% respectively.

CONCLUSION
In this prospective pilot, basic visual analysis of 18F-Fluciclovine PET/CT provided high sensitivity and specificity in detection of TP in post-SRS BM based on different threshold scores, suggesting room for visual threshold optimization. A low TP event rate limited the ability to estimate sensitivity/specificity and to perform combined qualitative/quantitative analyses. Further study to refine interpretation criteria is ongoing.
NIMG-11. VOLUMETRIC ENDPOINTS IN DIFFUSE INTRINSIC PONTINE GLIOMA (DIPG): COMPARISON TO CROSS-SECTIONAL MEASURES AND CORRELATION WITH OUTCOMES
Authors: Lazow Margot , Nievelstein Martijn , Lane Adam , Bandopadhayay Pratiti , DeWire-Schottmiller Mariko , Fouladi Maryam , Glod John , Greiner Robert , Hoffman Lindsey , Hummel Trent , Kilburn Lindsay , Leary Sarah , Minturn Jane , Packer Roger , Ziegler David S , Chaney Brooklyn , Black Katie , de Blank Peter , Leach James 
Published: November 2021

Abstract

INTRODUCTION
Cross-sectional tumor measures are used as endpoints in clinical trials of DIPG, but may not capture meaningful changes in disease burden. Volumetric measures may provide a more accurate assessment of tumor growth. We measured the correlation between cross-sectional and volumetric measures and compared their prognostic impact to better understand response evaluation in DIPG.

METHODS
Patients from the International DIPG Registry with diagnostic and post-radiation MRIs were included. Utilizing mint LesionTM software, tumors were manually contoured by an experienced pediatric neuro-radiologist to extrapolate cross-sectional product (CP) and volume measures. Correlation between CP and volume was assessed by linear regression. Landmark analyses were performed to determine differences in overall survival (OS) (via log-rank) between patients classified as progressive disease (PD) versus non-PD according to CP and volumetric measurements at one-, three-, and five-months post-radiation. Imaging consistent with pseudoprogression was designated non-PD. Hazard ratios (HR) for survival after these timepoints were calculated by Cox regression.

RESULTS
A total of 317 MRIs from 46 patients were analyzed. When comparing change from smallest previous tumor size, CP increase of 25% (PD by RAPNO) correlated with volume increase of 28% (R2=0.685). There was no difference in OS between patients classified as PD versus non-PD by CP at one-month, three-months, or five-months post-radiation (p &gt;0.05). However, significant differences in OS were observed between patients classified as PD versus non-PD by volume (28% increase) at one-month (2.7 vs. 12.8 months, p=0.005), three-months (1.9 vs. 10.7 months, p=0.036), and five-months post-radiation (3.7 vs. 9.1 months, p=0.023). PD by volume, but not by CP, was predictive of survival at all timepoints (HR: 5.0, 2.4, 2.4).

CONCLUSIONS
Volumetric assessments of PD correlated better with survival than CP at all post-radiation timepoints. Tumor volume likely represents a more accurate, prognostically-relevant measure of disease burden that deserves investigation in future DIPG trials.
NIMG-14. RESTING STATE EXECUTIVE CONTROL AND SALIENCE NETWORK CONNECTIVITY IN CLINICALLY STABLE LOWER GRADE GLIOMA COVARIES WITH COGNITIVE PERFORMANCE
Authors: Luks Tracy , Villanueva-Meyer Javier , Weyer-Jamora Christina , Brie Melissa , Smith Ellen , Braunstein Steve , Bracci Paige , Chang Susan , Hervey-Jumper Shawn , Taylor Jennie 
Published: November 2021

Abstract

BACKGROUND
Survival outcomes for patients with lower grade gliomas (LrGG) are improving. However, injury from tumor growth and consequences of treatment often leads to impaired cognition, particularly in cognitive domains reliant on distributed functional networks and intact white-matter tracts. Resting state functional MRI (rsfMRI) is a method of investigating the integrity of these functional networks.

METHODS
This study investigated rsfMRI connectivity in 21 patients with clinically stable LrGG compared to age- and gender-matched healthy controls, and associated imaging measures with cognitive outcomes. Data were acquired for 12 cognitive tests administered within one week of imaging. RsfMRI and T1-weighted images for 21 research controls were acquired from OpenNeuro datasets. RsfMRI data were processed and analyzed using the CONN toolbox using CONN’s standard regions of interest (ROI) for the 8 canonical networks as seeds, and cognitive test scores as covariates, with a threshold for T tests of p&lt; .001 uncorrected.

RESULTS
Median age was 48 years old (range 27-67). There were 6 astrocytomas, IDHmut; 3 astrocytomas IDH-wt, 8 oligodendrogliomas, and 4 NOS. Thirteen had left hemisphere tumors (8 frontal, 3 parietal, 2 temporal), and 6 right (5 frontal, 1 temporal). Fourteen had previously recieved radiotherapy. There was significantly lower connectivity in frontoparietal executive control and the salience networks in LrGG patients versus controls. Within patients, lower executive control network connectivity covaried with worse performance on executive measures (FAS, Tower of London, Trails-A, Animal Naming, FrSBe), and attention and working memory measures (Digit Symbol, HVLT). Lower salience network connectivity covaried with poorer performance on executive measures (FrSBe, FAS) and attention and working memory measures (Digit Span, HVLT, WAIS-WM).

CONCLUSION
In clinically stable LrGG, rsfMRI measures of network connectivity are potentially useful markers to monitor and track, given the concordance with cognition, and could help guide cognitive assessment and rehabilitation.
NIMG-17. SYSTEMATIC REVIEW OF LITERATURE EVALUATING MACHINE LEARNING ALGORITHMS TO DEVELOP OUTCOME PREDICTION MODELS IN GLIOMA USING MOLECULAR IMAGING WITH AMINO ACID PET
Authors: Shatalov Julia , Brim W R , Subramanian Harry , Bazaar John , Johnson Michele , Aboian Mariam 
Published: November 2021

Abstract

PURPOSE
Machine learning (ML) algorithms demonstrate accurate prediction of tumor segmentation, molecular pathology, and outcomes in gliomas using MRI and recently application of ML tools has expanded into molecular imaging with PET. We performed a systematic review to evaluate the role and applications of ML in characterization of gliomas with PET.

METHODS
Four databases were searched by medical school librarian and confirmed by an independent librarian: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science-Core Collection. The search strategy used keywords and controlled vocabulary combining the terms for: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and related terms. All articles were reviewed by at least 2 independent reviewers at abstract screening, full text review, data extraction, and bias analysis using TRIPOD.

RESULTS
An initial 11,727 publications were imported to Covidence for screening. After review, 1135 studies moved to full-text review and 715 articles were included. Twelve publications included PET imaging of gliomas. All publications used single-center databases (3-73 patients) with distribution of tracers being [18F]-FDG (1), [18F]-FET (6), [11C]-MET (3), [18F]-FDOPA (1), and [18F]-AMP (1). All but 2 papers used supervised machine learning algorithms. Number of features ranged from 4-19,284. Nine papers manually extracted semiquantitative features TBRmax, TBRmean, SUV, TTP, in addition to demographics. Study outcomes included prediction of treatment response, survival, molecular subtypes, tumor grade, segmentation, and accuracy of image fusion. Accuracy ranged from 0.64-0.95 with AUC 0.43-0.9.

CONCLUSION
ML can be used on small datasets of PET imaging of brain tumors. While majority of the clinical scans are performed with FDG-PET, the machine learning approaches are being applied to mostly amino acid tracers. Extending ML approaches to FDG-PET, which is more common in clinical practice, is recommended. Overall, ML has potential as a useful tool for predicting patient outcomes and improving image postprocessing.
NIMG-31. PROPOSED ORIGINS AND PATHWAYS OF DIFFUSE GLIOMAS REVEALED BY LESION COVARIANCE NETWORKS
Authors: Mandal Ayan , Romero-Garcia Rafael , Seidlitz Jakob , Hart Michael , Alexander-Bloch Aaron , Suckling John 
Published: November 2021

Abstract
Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers, and large-scale connectivity networks. Using lesion data from a total of 410 patients with high- and low-grade glioma, we identified – and replicated in an independent sample – three lesion covariance networks (LCNs), which reflect clusters of frequent glioma localization. These three LCNs overlapped with the anterior, posterior, and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal, and temporal cortices. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly coincided with multiple structural and functional connectivity networks, with LCN1 bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each LCN. These results build upon prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Cumulatively, our findings support a model wherein periventricular brain connectivity guides glioma development from the subventricular zone into distributed brain regions.
NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION
Authors: Tillmanns Niklas , Lum Avery , Brim W R , Subramanian Harry , Lin Ming , Bousabarah Khaled , Malhotra Ajay , cui Jin , Brackett Alexandria , Payabvash Sam , Ikuta Ichiro , Johnson Michele , Turowski Bernd , Aboian Mariam 
Published: November 2021

Abstract

PURPOSE
Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models into clinical practice. While a large body of literature has been published on machine learning methods for segmentation of brain tumors, a systematic evaluation of paper quality and reproducibility has not been done. We investigated the use of “Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis” (TRIPOD) items, among papers published in this relatively new and growing field.

METHODS
According to PRISMA a literature review was performed on four databases, Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL) and Web of science core-collection first in October 2020 and a second time in February 2021. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. The publications were assessed in order to the TRIPOD items.

RESULTS
37 publications from our database search were screened in TRIPOD and yielded an average score of 12.08 with the maximum score being 16 and the minimum score 7. The best scoring item was interpretation (item 19) where all papers scored a point. The lowest scoring items were the title, the abstract, risk groups and the model performance (items number 1, 2, 11 and 16), where no paper scored a point. Less than 1% of the papers discussed the problem of missing data (item 9) and the funding of research (item 22).

CONCLUSION
TRIPOD analysis showed that a majority of the papers do not score high on critical elements that allow reproducibility, translation, and objectivity of research. An average score of 12.08 (40%) indicates that the publications usually achieve a relatively low score. The categories that were consistently poorly described include the ML network description, measuring model performance, title details and inclusion of information into the abstract.
NIMG-45. DIFFUSION MRI AS AN EARLY BIOMARKER OF OVERALL SURVIVAL BENEFIT IN RECURRENT GLIOBLASTOMA TREATED WITH IMMUNE CHECKPOINT INHIBITORS
Authors: Hagiwara Akifumi , Jingwen Yao , Oughourlian Talia , Wang Chencai , Raymond Catalina , Everson Richard , Patel Kunal , Mareninov Sergey , Yong William , Salamon Noriko , Pope Whitney , Nghiemphu Phioanh , Liau Linda , Prins Robert , Cloughesy Timothy , Ellingson Benjamin 
Published: November 2021

Abstract
Diffusion magnetic resonance imaging (MRI) estimates of the apparent diffusion coefficient (ADC) has been revealed to be useful for predicting treatment response in patients with glioblastoma (GBM), with an increase in ADC indicating tumor cell death. The aim of the current study was to investigate whether quantitative changes in diffusion MRI after treatment with immune checkpoint inhibitors (ICI) could predict OS in patients with recurrent GBM. A total of 61 patients were included in this retrospective study with the following inclusion criteria: (i) diagnosis of recurrent GBM treated either with pembrolizumab or nivolumab and (ii) availability of diffusion data in pre- and post-ICI MRI. Tumor volume and median relative apparent diffusion coefficient (rADC) within enhancing tumor with respect to normal-appearing white matter were calculated. Median OS for all patients was 7.6 months (range, 1.0–30.6 months). Results showed that higher post-treatment rADC was associated with significantly longer OS (median, 372 days for rADC ≧ 1.42 versus 211 days for rADC &lt; 1.42; P = 0.01, log-rank test), whereas tumor volume and pre-treatment rADC were not significantly associated with OS. Cox regression analysis revealed post-treatment rADC significantly influences OS (P = 0.02, univariate analysis), even after controlling for age, sex, and IDH status (P = 0.007, multivariate analysis), and additionally controlling for whether surgery was performed after ICI treatment (P = 0.049, multivariate analysis). In conclusion, a high post-treatment rADC may be an early imaging biomarker for identifying OS benefit in recurrent GBM patients receiving ICI treatment.
NIMG-49. A PROSPECTIVE, MULTI-CENTRE TRIAL OF FET-PET IN GLIOBLASTOMA PATIENTS - THE TROG 18.06 FIG STUDY: KEY ASPECTS OF IMAGING AND RADIATION ONCOLOGY CREDENTIALING
Authors: Koh Eng-Siew , Francis Roslyn J , Ebert Martin , Gan Hui , Lee Sze Ting , Lau Eddie , Moore Alisha , Grose Andrew , Rossi Alana , Ng Sweet Ping , Yap June , Ly Tam , Lin Peter , Pinkham Mark B , Ngai Stanley , Yu Christopher , Gorayski Peter , Le Hien , Kirkwood Ian D , Vallat Wilson , Syed Farhan , Krishna Dayanethee , Khan Shahroz , Gill Suki , Thomas Elizabeth , Back Michael , Barnes Elizabeth H , Moffat Bradford A , Scott Fiona , Adda Lucas , Foroudi Farshad , Lourenco Richard De Abreu , Nowak Anna K , Bailey Dale L , Scott Andrew M 
Published: November 2021

Abstract
The FIG study is a prospective non-randomised study now recruiting up to 210 newly diagnosed GBM participants across ten Australian sites. Study outcomes will address the role of [18F] fluoroethyl-L-tyrosine positron emission tomography (FET-PET) in radiotherapy (RT) planning, evaluation of post-treatment changes versus disease progression and prognostication. We describe here the methodology and preliminary outcomes for site credentialing. Eligible participants with GBM undergo FET-PET imaging at three time-points: FET-PET1-post-operative pre-chemo-RT, FET-PET2 acquired one month post-chemo-RT and FET-PET3 (+/-FDG-PET) triggered when clinical and/or radiological (MRI) progression is suspected. Dynamic and static FET-PET images are analysed qualitatively and quantitatively. Radiotherapy is as per standard care with the treating Radiation Oncologist (RO) blinded to FET-PET1. Site nuclear medicine (NM) physicians are required to delineate a biological target volume (BTV) based on FET-PET1 with hybrid RT volumes derived post-hoc. Pre-trial NM quality assurance comprises certification from the Australasian Radiopharmaceutical Trials Network encompassing FET-PET radiochemistry Quality Control and PET camera calibration. Site and central integrated workflows incorporating multi-modality image registration, target volume/region of interest contouring and analysis have been developed. NM benchmarking involves delineation of FET-PET BTVs in 3 cases with another 3 cases addressing response criteria interpretation harmonized across FET-PET, FDG-PET and MRI. Site ROs complete 3 cases involving standard and hybrid target volume delineation based on pre-derived FET-PET volumes. All NM and RO credentialing cases undergo central expert review. To date, of six sites which have submitted full credentialing data, 19/21 RO and 6/6 planning cases were passed. Of 72 NM cases, 18/72 (25%) required resubmission, primarily related to ensuring standardisation of background regions and time activity curve interpretation. The FIG study will be pivotal in establishing the role of FET-PET in GBM management. The robust NM and RO credentialing program will build capacity and expertise in FET-PET production, acquisition and image interpretation.