bluuebunny/crossref_metadata_2025_split huggingface dataset:
P04.01.B DECIPHERING GLIOBLASTOMA METABOLIC SIGNATURE: MOLECULAR PROFILING AND NADH-FLIM IMAGING
Authors: Morelli M , Santonocito O , Lessi F , Ferri G , Menicagli M , Aretini P , Grimod G , Gambacciani C , Pieri F , Schweizer L , Ronellenfitsch M , Sanson M , Picca A , Garofano L , Iavarone A , Mazzanti C , Di Stefano A 
Published: October 2024

Abstract

BACKGROUND
Recent breakthroughs in single-cell analysis reveal distinct metabolic cellular states, leading to a novel classification of glioblastoma (GBM). An RNA Sequencing methodology optimized for FFPE tumor tissue of GBM allows for the detection of GBMs which are dependent on oxidative phosphorylation (OXPHOS). OXPHOS GBM show vulnerabilities to anti-mitochondrial agents, both in vitro and in vivo, and are currently the target population of clinical trials in newly diagnosed GBM patients (Optimum NCT04945148). To date, RNA Sequencing is the sole standardized method for the retrospective and prospective identification of OXPHOS GBM.

OBJECTIVE
To evaluate the efficacy of Fluorescence Lifetime Imaging (FLIM) microscopy of NADH, an intracellular enzymatic cofactor, in discerning the metabolic profile of FFPE GBM tumor tissue, with a focus on identifying OXPHOS GBM subtypes.

METHODS
We analyzed 3’ mRNA NGS FFPE tumor tissue from a retrospective series of IDHwt glioblastomas, newly diagnosed from 2021 to 2023. The OXPHOS logODDS ratio, GPM logODDS ratio, and the simplicity score were considered as quantitative criteria to select prototype cases. We selected OXPHOS mitochondrial and glycolytic/plurimetabolic cases for the NADH-FLIM microscopy study on FFPE slides. NADH-FLIM images were analyzed quantitatively. For each cases 10-16 subareas were analysed. Areas of necrosis were excluded from the quantitative analysis.

RESULTS
RNA Sequencing of 28 consecutive newly diagnosed IDHwt GBM patients from the Livorno Hospital found 9 OXPHOS, 6 glycolytic/plurimetabolic (GPM), 1 neuronal (NEU), 9 not classifiable, and 3 proliferative (PPR) cases. In the OXPHOS subgroup, 5 patients showed the highest score of association with the OXPHOS signature and were the subject of the NADH-FLIM study compared to the top 5 GPM cases. NADH bound/free distribution curves significantly differed between the OXPHOS’ and GPM’ subgroups, with higher values for GPM cases [GPM: median 0.21, range 0.0-0.93; OXPHOS: median 0.02, range 0.0-0.99; p&lt;0.0001]. We noticed different skewness (0.53 vs 1.21) and kurtosis (-1.25 vs -0.15) parameters in NADH bound/free curves between GPMs and OXPHOSs, respectively.

CONCLUSIONS
OXPHOS and GPM glioblastoma clusters significantly differed in NADH bound/free distribution curves. Supplementary cases are currently under study to confirm preliminary results and to assess the power of NADH-FLIM to predict the metabolic status of GBM samples.
P08.06.B TARGETED PLASMA METABOLOMICS DETECTS A METABOLIC SIGNATURE POTENTIALLY RELEVANT FOR DIAGNOSIS AND PROGNOSIS IN PATIENTS WITH GLIOBLASTOMA
Authors: Pantalone M , Söderberg Naucler C , Stragliotto G 
Published: October 2024

Abstract

BACKGROUND
Glioblastoma diagnosis is made initially with radiological examination and pathological examination of the resected tumor. Debut symptoms may vary and be mild in the beginning therefore diagnosis can be delayed. Additional un-invasive diagnostic strategies can be useful for diagnosis and clinical follow up. Moreover, despite glioblastoma being almost invariably fatal, there are no clinically established plasma biomarkers to define prognosis in patients with glioblastoma.

MATERIAL AND METHODS
In this study we employed flow injection analysis tandem mass spectrometry to compare the plasma metabolic profile of patients with GBM (n=51) and healthy controls (n=24).

RESULTS
Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) highlighted a distinctive glioblastoma signature. The best confusion matrix from the ROC curve built on two metabolites, GLU and GLN-LYS, provided specificity of 87% and sensitivity of 92%, with positive and negative predictive values of 95% and 88%, respectively. Multivariate survival analyses revealed that higher levels of ALA, MET, ORN, PHE, TYR were to be associated with a shorter overall survival.

CONCLUSION
These results are currently being evaluated prospectively and at different stages of disease in a larger cohort of patients with glioblastoma to identify plasma metabolic markers relevant for recurrence development.
P09.06.A ELEVATED TRANSLOCATOR PROTEIN 18KDA BINDING IN LOW MEAN DIFFUSIVITY CLUSTERS OF THE TRANSFORMING GLIOMA MICROENVIRONMENT
Authors: Breese J , Agushi E , Lewis D , Hinz R , Alfaifi B , Jackson A , Coope D 
Published: October 2024

Abstract

BACKGROUND
Approximately 95% of low-grade gliomas (LGGs) undergo anaplastic de-differentiation into high-grade gliomas, a process only partially understood from the current suite of in vivo clinical imaging methods.

MATERIAL AND METHODS
11 patients with suspected transforming gliomas undertook a comprehensive magnetic resonance imaging (MRI)/positron emission tomography (PET) imaging protocol, incorporating post-contrast T1W/FLAIR acquisition for tumour delineation, and diffusion tensor imaging. PET imaging with the radiotracer [11C]-(R)-PK11195 assessed the distribution of translocator protein 18kDa (TSPO), whose upregulation is associated with inflammation and glioma progression. [11C]-(R)-PK11195 binding potential (BPND) maps were generated, utilising the simplified reference tissue model, with the grey-matter cerebellar time activity curve serving as the tissue input function. KMeans clustering of MD intensity values within the tumour FLAIR (minus contrast enhancement) region marked spatially distinct “habitats” of high/low MD voxel clusters. BPND values within the high and low MD clustering habitats were collected and subsequently compared between tumour grades.

RESULTS
Within the FLAIR (minus contrast enhancement) tumour region of grade 2 and 3 gliomas (n = 6), mean BPND values in low MD habitats (0.042 ± 0.125) were significantly higher than habitats of high MD (-0.066 ± 0.130) (p = 0.000007). In grade 4 tumours (n = 5), no significant difference was observed in the mean TSPO BPND values between habitats of low MD (0.498 ± 0.297) and high MD (0.509 ± 0.167) clusters (p = 0.892).

CONCLUSION
Our findings demonstrate a unique aspect of spatial heterogeneity in grade 2 and 3 tumours, absent from grade 4 gliomas, indicating differences in the underlying inflammatory processes. Regions of restricted diffusion with high TSPO signal in grade 2 and 3 tumours may signify heterogenous distributions of active immune cells and/or neoplastic cell populations in the microenvironment. Conversely, grade 4 tumours may exhibit uniform patterns of immune cell activation/neoplastic cell infiltration throughout their surrounding microenvironment.
P09.08.B GADOPICLENOL CAN HELP WITH GAMMA KNIFE LOCALIZATION OF BRAIN METASTASIS OVER OTHER CLASS II GADOLINIUM BASED CONTRAST AGENTS
Authors: Stevens G H J , Wu J 
Published: October 2024

Abstract

BACKGROUND
Brain metastases are common and affect up to 30% of adult cancer patients. An estimated 150,000 to 200,000 brain metastases are diagnosed annually and some form of stereotactic radiosurgery (SRS) is commonly employed as a treatment modality. The magnetic resonance imaging (MRI) localization exam for SRS typically involves a volumetric post contrast T1 3D Flash or T1 Space sequence with 1mm slices using either gadoterate meglumine or gadoteridol as the contrast agent. Acquisition time is approximately 7 minutes. A gadolinium-based contrast agent that has high stability (lower risk of nephrogenic systemic fibrosis by having a higher dissociation half-life), a high T1 relaxivity (greater signal to noise ratio to allow for a lower dose of gadolinium and hence reduce gadolinium exposure), and high tolerability (lower rate of adverse reaction) is most ideal for lesion localization. Imaging requires a high lesion to background contrast and contrast to noise to allow accurate lesion visualization. Gadopiclenol is a class II Gadolinium based contrast agent that provides these properties.

MATERIAL AND METHODS
We analyzed 50 consecutive brain metastases patients using two MRI localization exams (as defined above), performed within 10 days using the same MRI scanner and same imaging parameters. One scan was completed using the contrast agent gadoterate meglumine and the other gadopiclenol. The MRIs were interpreted by the same Neuroradiologist.

RESULTS
There were 30 females and 20 males with a mean age of 62.4 years. The most common pathology was lung (N=28). In 10% of patients, the brain metastases were more conspicuous on the gadopiclenol study and /or a greater number of metastases were seen resulting in a treatment change. No adverse reactions were noted.

CONCLUSION
Our institution has transitioned to using primarily gadopiclenol for MRI gamma knife localization exams as it appears to be more effective in lesion localization compared to other available agents, with similar safety profile. Multiple cases will be presented.
P09.11.A IMPACT OF FET-PET IMAGING IN SURVEILLANCE OF THERANOSTIC TREATMENT WITH 4-L-[131I]IODO-PHENYLALANINE ([131I]IPA) IN RELAPSED GLIOBLASTOMA PATIENTS
Authors: Pichler J , Leibetseder A , Höllmüller I , Feichtinger J , Dierneder J , Osipova O , Pichler R 
Published: October 2024

Abstract

BACKGROUND
Theranostic is an evolving field in oncology. IPAX1 and IPAX-Linz studies used the radioligand Iodine-131[131I] IPA in combination with external re-irradiation in glioblastoma (GBM) patients. Modality of choice for assessment after therapy is contrast-enhanced magnetic resonance imaging (CE-MRI). Further, O-(2-[18F]fluoroethyl-)-L-tyrosine (18F-FET) positron emission tomography (PET) has become a valuable tool for brain tumor surveillance by adding metabolic information. However, no real-world data exist in the joint application of RANO 2.0 and PET RANO 1.0 criteria in response assessment of GBM, especially in patients treated with a theranostic approach.

MATERIAL AND METHODS
12 patients with histological diagnosis of recurrent GBM treated in our center within study protocols IPAX 1 and IPAX Linz were evaluated to compare MRI with FET-PET in follow up after treatment with theranostic therapy. PET RANO 1.0 criteria were used to evaluate the validity and predictive value of additional FET-PET imaging in treatment surveillance. Primary aim of the study was the response assessment and comparison of progression-free survival following RANO 2.0 criteria and PET RANO 1.0 criteria.

RESULTS
Mean PFS according to MRI criteria was 20.5 weeks (4.7 months). Mean OS was 48.5 weeks (11.2 months). No partial or complete responses were seen in MRI follow up. 7/12 (58.3 %) patients show progression at first FU MRI two months after baseline. In 2nd FU 11/12 (91.7 %) patients progressed. Evaluation based on PET RANO 1.0 criteria at first FET-PET FU investigation resulted in different outcomes depending on which PET parameter was used (volume, TBRmax, TBRmean). Mean PFS calculated by PET volume is 77 days (11 weeks/2.5 mo). Mean PFS based on TBRmax or TBRmean is not assessable because in the 2 follow up investigations most patients were stable or had a partial response in these two PET parameters.

CONCLUSION
This pilot study demonstrated different outcome in respect of PFS depending on whether MRI or PET RANO criteria have been used for analysis. Further, application of PET RANO criteria showed varying results in terms of response for each individual parameter (TBRmax, TBRmean and PETvolume). Based on our data, the value of an additional PET scan for a valid prediction of response in recurrent GBM patients must be discussed. Disclosure: IPAX-1 was sponsored by Telix Pharmaceuticals. IPAX Linz is an investigator-initiated trial; IMP was provided by Telix Pharmaceuticals.
P09.17.B TUMOUR SIZE AND OVERALL SURVIVAL IN A COHORT OF PATIENTS WITH UNIFOCAL GLIOBLASTOMA. A UNI- AND MULTIVARIABLE PROGNOSTIC MODELLING AND RESAMPLING STUDY
Authors: Fatania K , Frood R , Mistry H , Short S C , O’Connor J , Scarsbrook A F , Currie S 
Published: October 2024

Abstract

BACKGROUND
Identifying prognostic factors for patients with glioblastoma (GBM) may better stratify patients into different risk groups, and accelerate individualised treatment strategies. One potential prognostic factor is tumour size. This is routinely assessed clinically usually by measurement of the diameter of the enhancing core, but there are now more readily available segmentation algorithms that allow estimation of tumour volume. Published models typically vary in their selection of tumour size in the final model. We aimed to investigate the effect of tumour size on prognosis in an institutional cohort of patients with GBM, and investigate the effect of log-transform and different sample sizes with bootstrap resampling.

MATERIAL AND METHODS
279 patients with unifocal IDH-wild type WHO grade 4 GBM (2014-2020) had their tumours segmented using deep-learning and manual correction and diameter measured manually. Institutional data was used to examine any prognostic effect between diameter, core, or whole tumour volume (CV, WV respectively) using Cox proportional hazards models. Data underwent bootstrap analysis (1,000,000 repetitions) to determine if log-transformation and/or changes in sample size had an impact on the chance of detecting a prognostic relationship of size. Multivariable models were adjusted for clinical predictors: age, gender, surgery (biopsy, gross-total, near-total or sub-total resection), MGMT promoter methylation and completion of adjuvant therapy (full Stupp, partial Stupp or none). P-value distribution across all resamples was assessed with a 2-sided Kolmogorov Smirnov (KS) test to ascertain the impact of log-transform and sample size on the chance of detecting a prognostic relationship.

RESULTS
All models adjusted for surgery were significant (p = 0.016, 0.013 and 0.018 respectively). Diameter adjusted for all clinical predictors was also significant (p=0.03). Log-transformation had a large effect on chances of detecting a prognostic effect of WV, by significantly decreasing the distribution of p-values towards 0.05 or below (KS test &lt;0.05). Multivariable resampling experiments also demonstrated that with increased sample size, the chance of detecting a prognostic relationship of size (diameter, CV or WV) increased, which is the opposite of the expected behaviour for noise variables (i.e. predictors with random association to overall survival (OS)).

CONCLUSION
This study of a large single institutional cohort of patients with GBM,found: (1)diameter and WV are prognostic of OS in multivariable models, (2) log-transformation increases the chances of detecting a prognostic relationship of WTV with OS, and (3) greater sample sizes increase the chance of finding a prognostic role for tumour size.
P10.11.B CLINICAL APPLICATION OF MACHINE-BASED DEEP LEARNING IN PATIENTS WITH RADIOLOGICALLY PRESUMED ADULT-TYPE DIFFUSE GLIOMA GRADE 2 OR 3
Authors: Gómez Vecchio T , Neimantaite A , Solheim O , Pallud J , Berger M S , Widhalm G , Bartek jr. J , Gu I Y H , Jakola A S 
Published: October 2024

Abstract

BACKGROUND
Diffuse lower-grade gliomas (dLGG) are nowadays characterized by the IDH mutation. Presumed dLGG are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. However, one third of the radiologically presumed dLGG turn out to be patients with IDH wild-type and have a disadvantaged prognosis. The aim of this study was to reduce clinical uncertainty applying deep learning (DL) to the prediction of IDH mutation in patients with radiologically presumed dLGG.

MATERIAL AND METHODS
314 patients were retrospectively recruited from six neurosurgical departments in Sweden, Norway, France, Austria, and the United States. Collected data included patient’s age, sex, tumor molecular characteristics (IDH, and 1p19q status), and routine preoperative radiological images. A clinical model was built using multivariable logistic regression with the variables age and voxel-based tumor location. Four DL architectures used in glioma research were chosen for comparison, the best performing DL model was selected with a grid search. In the final test, the clinical model and the DL model were scored with an unseen validation dataset consisting of 155 patients from the Erasmus Glioma Dataset.

RESULTS
The mean age in the training and validation cohorts were 45.0 (SD 14.3) and 44.3 years (SD 14.6). The training and validation cohorts were similar, except concerning the variables sex (53.5% vs 64.5% males, p-value 0.03) and IDH status (30.9% vs 12.9% IDH wild-type, p-value &lt;0.01). Scored accuracy, sensitivity, specificity, positive predicted value, negative predicted value, and area under the curve for clinical model: 0.86, 0.90, 0.55, 0.93, 0.46, and AUC 0.86; DL model: 0.68, 0.66, 0.80, 0.96, 0.26, and 0.82; combined model: 0.78, 0.77, 0.85, 0.97, 0.35, and AUC 0.87; combined model (specificity tuned): 0.62, 0.58, 0.90, 0.98, 0.24, and AUC 0.87.

CONCLUSION
We present a large cohort of patients with non or minimal enhancing presumed dLGG studied with DL. Compared to acceptable results in the clinical model, the DL model showed lower sensitivity and higher specificity. Furthermore, when these models were combined, the AUC was slightly improved also showing good results at a high specificity cutoff. However, the DL model failed to deliver additional gain over the clinical model. DL models should be further improved if they are to become clinically applicable.
P10.13.A DEMOCRATIZING QUALITY: A STRATEGY TO MAKE LEVEL OF EVIDENCE ASSESSMENTS QUICK, EASY, AND ACCESSIBLE TO ALL
Authors: Badani A , Grzyb C , Church E W , Glantz M 
Published: October 2024

Abstract

BACKGROUND
Advances in clinical care depend decisively on evidence from high-quality clinical trials. EANO and other international societies have established systematic and transparent methodologies for assessing the quality of clinical trials, but the process is time-consuming and challenging to master. Using advanced computational strategies, we have developed three novel models—an Excel-based calculator, a machine learning (ML) model, and a permutation-based decision tree—designed to facilitate this assessment and make it easily accessible to clinicians and researchers.

METHODS AND MATERIALS
A PRISMA-compliant literature review was conducted to identify all therapeutic human trials involving adult neuro-oncology patients published in the last 12 months in high-impact journals. Articles fulfilling pre-specified inclusion and exclusion criteria were selected and a level of evidence was assigned independently by two investigators, with differences resolved by consensus. Simultaneously, an advanced ML model was created, employing a Support Vector Machine (SVM) algorithm renowned for its efficacy in classification tasks. The SVM model was configured with a 0.2 train-test split to validate its predictive accuracy and recursive feature elimination to evaluate the significance of input features, confirming their equal importance. Additionally, a permutation-based decision tree was developed, where a collaborative team analyzed all possible permutations of questionnaire responses to assign explicit quality ratings through a data-driven decision framework. Finally, an Excel-based tool was developed to calculate the level of evidence. The results of all four assessment strategies were statistically compared.

RESULTS
The ML model was developed and trained on a dataset of 225 categorized research papers and achieved a classification accuracy of 97%. The permutation-based decision tree tool was designed to map all possible questionnaire response permutations to specific trial quality levels, with 11,521 independently graded permutation response pathways. The Excel-based tool was programmed to use a phrase-matching algorithm to identify an evidence level based on a user’s provided answers. The permutation and Excel-based strategies provided excellent classification accuracy but were inferior to the ML model. As we will demonstrate, all three models were easy to use and provided rapid and reliable levels of evidence assessments.

CONCLUSION
By providing open access to these tools to clinicians and researchers and making the level of evidence assessments efficient, consistent, and transparent, we hope to democratize the ability to evaluate published trials, thereby improving the quality of clinical care. We also hope that clinical researchers will utilize these tools to enhance the quality of their clinical trials at the design stage.
P10.17.B IMPACT OF INTENSITY STANDARDISATION AND COMBAT BATCH SIZE VARIATION ON COMBINED CLINICAL AND RADIOMICS PROGNOSTIC MODELS IN PATIENTS WITH UNIFOCAL GLIOBLASTOMA
Authors: Fatania K , Frood R , Mistry H , Short S C , O’Connor J , Scarsbrook A F , Currie S 
Published: October 2024

Abstract

BACKGROUND
Prognostic models for patients with glioblastoma (GBM) may include quantitative image features extracted from MRI (‘radiomics’). Multicentre image acquisition poses a challenge to radiomic feature (RF) reproducibility and MRI intensity standardisation techniques (ISTs) and ComBat may help. Optimal IST and minimum batch size (MBS) for ComBat are uncertain - the latter especially with real-world datasets where few patients are scanned at each site. Our aim was to assess the effect of three popular ISTs in conjunction with varying MBS on prognostic model performance and stability in a real-world, multi-centre GBM patient cohort.

MATERIAL AND METHODS
Patients with IDH-wildtype unifocal WHO grade 4 GBM and pre-operative MRI acquired across multiple geographic locations in the region between 2014-2020 were retrospectively included. Tumours were segmented using deep-learning with manual correction. WhiteStripe (WS), Nyul histogram matching (HM) and Z-score (ZS) ISTs were applied before RF extraction. Post-extraction, RFs were realigned using ComBat using MBS of 5, 10 or 15. Cox proportional hazards models were produced using five strategies to select the four best performing RFs, and the impact of IST and MBS was evaluated with bootstrapping (1,000 repetitions), and measuring model calibration, discrimination, relative explained variation and model fit. Model instability was assessed using bootstrap resampling to produce 95% confidence intervals (95% CIs), feature instability tables and calibration instability plots.

RESULTS
195 patients were included with median overall survival of 13 months (95% CI 12-14). Increasing the ComBat MBS reduced the sample size available for modelling (195, MBS=5; 159 MBS=10; 129 MBS = 15). HM and WS tended to produce the highest relative increase in model discrimination, explained variation and model fit but IST choice did not influence model stability or calibration. Increasing the ComBat MBS improved model discrimination, relative explained variance and model fit, however it degraded model stability and calibration accuracy. Calibration instability plots showed a greater spread of calibration curves across the bootstrap resamples as the ComBat MBS increased (and sample size reduced), indicating worse calibration accuracy and stability.

CONCLUSION
IST and ComBat MBS had varied effects. Increased ComBat MBS (and reduced sample size) improved most performance metrics but stability and calibration deteriorated. HM and WS tended to increase most model performance metrics and did not negatively influence stability. Our real-world, multi-centre data showed that while ComBat may improve multi-centre radiomics models, it requires data to be excluded, with negative results for sample size and stability and unknown consequences for patients imaged in smaller centres.