Abstract
Characterization of pediatric low-grade glioma (pLGG) remains a significant challenge in the field of neuro-oncology, with a need for more effective precision diagnostics. Multi-modal ...analysis, which incorporates data from varied sources, has the potential to provide a comprehensive understanding of the underlying biology of pediatric brain tumors. Despite its potential, the utilization of true multi-modality clustering remains limited in pediatric brain tumor research. In this study, we aimed to address this gap by using a clustering model that incorporated genomics, radiomics, and clinical variables (age, sex, and tumor location) to group patients into distinct clusters. 103 patients with pLGGs were included. Mutations data was derived from whole genome sequencing obtained through the PedCBioportal. Radiomic data was obtained from MR imaging through the Children’s Brain Tumor Network and included features from pre- and post-contrast T1, T2, FLAIR, and ADC sequences. Categorical variables included sex (male vs female), genetic mutation status for 10 selected genes (BRAF, FGFR1, TSC1, TSC2, NF1, MYB, EGFR, ALK, IDH1, and RB1) that are known to play a role in the pathogenesis of pLGG (mutated vs non-mutated), and tumor location (9 categories). Continuous variables included age (days) and radiomic features. All numerical variables were normalized and reduced into principal components that captured 90% of the variance in the data prior to clustering. Various clustering iterations were performed incorporating combinations of radiomic, genomic, and clinical data. Our models identified two distinct clusters and the PFS differences between clusters approached statistical significance with the integration of information from all modalities when compared to any combination of subsets of data, highlighting the complementary value of these modalities in providing a comprehensive characterization of pLGG. This study provides preliminary evidence for the utility of multi-modality data clustering in improving our understanding of pLGG and supports further investigation into this approach.
Abstract
In pediatric low-grade gliomas (pLGG), prognosis and responses to treatments are heterogeneous. This heterogeneity may be explained by the differences in molecular composition of the tumors ...of the same histology and the likely upstream alterations following administered radiation or systemic therapy. Integration of radiomics and clinical variables could generate a non-invasive biomarker that provides upfront prediction about patient’s risk of progression. We show that our proposed radiomic-based risk-stratification signature for pLGGs is associated with alterations in transcriptomic pathways. Standard multiparametric MRI sequences of 134 pLGG patients from Children’s Brain Tumor Network (CBTN) were retrospectively collected and 881 quantitative radiomic features were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted on clinical (age, sex, tumor location, and extent of tumor resection) along with radiomic variables using 5-fold cross-validation, to predict patient’s risk of progression. The Cox-PH model showed excellent performance in prediction of PFS and patient’s risk scores, supported by the concordance index of 0.78. Radiogenomic analysis was performed to determine the transcriptomic pathways (1594 pathways, c2 MsigDb Reactome v2022.1) that contribute to the pLGG risk, predicted by the radiomic signature. ElasticNet regression was applied on the scores obtained by gene set enrichment analysis (GSEA) (in 70/134 subjects) to predict radiomic-based risk scores. Increased risk, corresponded to upregulation of DNA repair pathways, dysregulation of lipophagy, fatty acid beta oxidation, and vitamin D pathways, which are tumor-promoting. BRAFfusion signaling inversely correlated with risk, consistent with known favorable prognosis of KIAA1549-BRAF fused pLGGs. Upregulation in immune related pathways and Toll-Like Receptor (TLR) signaling was associated with lower risk. This study elucidates the synergistic dynamics between the biological processes that promote the risk of progression and radiomic-based risk-stratification signature. The proposed biomarker may be used to encourage targeted therapies in patients with increased predicted risk.
Abstract
Current response assessment in pediatric brain tumors (PBTs), as recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group, relies on 2D measurements of ...changes in tumor size. However, there is growing evidence of underestimation of tumor size in PBTs using 2D compared to volumetric (3D) measurement approach. Accordingly, automated methods that reduce manual burden and intra- and inter-rater variability in segmenting tumor subregions and volumetric evaluations are warranted to facilitate tumor response assessment of PBTs. We have developed a fully automatic deep learning (DL) model using the nnUNet architecture on a large cohort of multi-institutional and multi-histology PBTs. The model was trained on widely available standard multiparametric MRI sequences (T1-pre, T1-post, T2, T2-FLAIR) for segmentation of the whole tumor and RAPNO-recommended subregions, including enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). As a prerequisite step for accurate tumor segmentation, we also generated another DL model based on DeepMedic for brain extraction from mpMRIs. The models were trained on an institutional cohort of 151 subjects and independently tested on 64 subjects from the internal and 29 patients from external institutions. The trained models showed excellent performance with median Dice scores of 0.98±0.02/0.97±0.02 for brain tissue segmentation, 0.92±0.08/0.90±0.17 for whole tumor segmentation, 0.76±0.31/0.87±0.29 for ET subregion, and 0.82±0.15/0.80±0.28 for segmentation of non-enhancing components (combination of NET, CC, and ED) in internal/external test sets, respectively. The automated segmentation demonstrated strong agreement with expert segmentations in volumetric measurement of tumor components, with Pearson’s correlation coefficients of 0.97, 0.97, 0.99, and 0.79 (p<0.0001) for ET, NET, CC, and ED regions, respectively. Our proposed multi-institutional and multi-histology automated segmentation method has the potential to aid clinical neuro-oncology practice by providing reliable and reproducible volumetric measurements for treatment response assessment.
Viscous dissipation is the production of heat due to the slip of fluid layers and can raise the temperature of the fluid that is affected by high shear stresses. This raise of temperature in fluids ...with explosive properties can cause the explosion during the processing. The present paper investigates the temperature distribution of an explosive fluid beside the wall of a converging tube. This study has been done by using the computational fluid dynamics and OpenFOAM software. The studied cases contain the fluid with two viscosities (50 and 500 kg/m × s) and two inlet conditions (constant and developed velocity profile). The results of this study show that at the end of a converging pipe, duo to the viscous dissipation effects, the temperature rise for high viscosity fluid is more intensive and this is a dangerous fact for high viscosity explosive fluids discharging. Also, it has been considered that the constant inlet velocity is safer in comparison with the developed profile, as the slope of temperature rise is less.
Abstract
Pediatric low-grade glioma (pLGG) encompasses a variety of tumor subtypes with heterogeneous treatment response and relatively long progression-free survival (PFS). Radiomics may serve as a ...non-invasive and in-vivo tool for early prediction of PFS as a surrogate marker for treatment response and to objectively gauge the efficacy of novel treatment strategies. Here, we present a multivariate model based on radiomic features and clinical variables for risk stratification of pLGGs in terms of PFS and seek associations of the predicted risk groups and mutations in key molecular markers using data from PedCBioportal. Pre-operative multi-parametric MRI scans (T1-pre, T1-post, T2, T2-FLAIR) of 129 patients with newly diagnosed pLGG (median age, 7.76, range, 0.35-19.58 years; median PFS, 28.5, range, 1.1-124.8 months) were collected and quantitative radiomic features (n = 881) were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted based on clinical (age, sex, and extent of tumor resection) and radiomic variables using 4-fold cross-validation. A subset of radiomic features (n = 27) that were most predictive of PFS was selected by applying Elastic Net regularization penalty during Cox-PH model fitting. High-, medium- and low-risk groups were determined based on model predictions. Cox-PH modeling showed excellent performance for prediction of PFS as suggested by the concordance index of 0.78. Radiogenomic assessment (data available in 94/129 patients) showed more enrichment of mutations in NF1 and RB1 genes in the high-risk group, as compared to the low- and medium-risk groups. We showed the potential value of radiomics in providing upfront prediction of PFS, which may further be used as an added treatment arm for early assessment of treatment response of the pLGG patients enrolled in the clinical trials. In the next step of this work, we will expand the cohort and cross-validate these results in an external cohort.
Abstract
MRI is the current gold standard imaging technique for diagnostic evaluation and monitoring of pediatric CNS tumors, however MRI measures are unable to fully relate to tumor biology and ...molecular stratification. Circulating in blood and cerebrospinal fluid (CSF), miRNAs are an abundant and stable nucleic acid which can be utilized as a tumor biomarker. Relating miRNA biomarkers and radiological tumor measurements may provide improved diagnostic and monitoring tools for pediatric brain tumors. Using a cohort of 54 pediatric brain tumors including low grade glioma, ependymoma, germ cell tumor, medulloblastoma, atypical teratoid rhabdoid tumor and high-grade glioma we attempted to combine MRI findings and circulating miRNA data. The miRNA expression was profiled in 33 CSF and 52 plasma samples using the HTG EdgeSeq platform. Clinically acquired, multi-parametric MRI scans at time-points close in proximity to liquid biopsy collection were collected retrospectively and used to generate volumetric tumor segmentations. We identified unique miRNA targets significantly correlated with MRI features, clinical findings, and patient outcomes. In both CSF and plasma, miRNA expression was identified to correlate with diagnosis and clinical features including tumor grade and survival status (p < 0.05). In CSF, miRNA expression was correlated with MRI measurements including cystic core volume, non-enhancing tumor volume, leptomeningeal disease, tumor size and location (p < 0.05). Combination of miRNA targets and radiomic tumor measurements improved diagnostic predictions between low- and high-grade tumors. In plasma, miRNA expression was correlated with MRI measurements including cystic core volume, location, and leptomeningeal disease (p < 0.05). These results demonstrate utility of miRNAs as a pediatric brain tumor biomarker which combined with imaging features can improve minimally to non-invasive diagnostics and management of pediatric brain tumors.
Abstract
Recent studies have shown preliminary evidence for differentiation of the tumor microenvironment (TME) and immune landscape between molecularly-defined medulloblastoma (MB) subtypes. ...Identifying radiological correlates of these TME patterns could establish a non-invasive method of immune profile characterization for guiding patient-centered therapies. Here, we examine immune profiles between MB subtypes using data from Open Pediatric Brain Tumor Atlas (OpenPBTA), and their relationship to tumor measurements from pre-operative MRIs. We identified a retrospective cohort of 94 pediatric MB patients with available molecular subtyping and immune profiles (36 cell types) from bulk gene expression data. A random forest analysis was used to classify the four MB subtypes based on immune profiles. Four cell types had high impact on classification performance: plasmacytoid dendritic cells (PDC; 25.8% accuracy decrease when randomized), hematopoietic stem cells (HSC; 21.9%), plasma B cells (20.3%), and cancer associated fibroblasts (18.8%). Pairwise comparisons revealed SHH and WNT tumors had significantly higher numbers of fibroblasts and HSCs compared to Group3/Group4. We also found novel evidence for significantly lower amounts of plasma B cells in the SHH group, and high PDC levels in Group4, followed by Group3, and low PDC in SHH/WNT. Multi-parametric MRI scans for 39 patients were used to segment tumor volumes. Overall tumor volume was significantly correlated with composite stroma scores (R = 0.34, p = 0.036). Additionally, patients with higher volumes of gadolinium contrast-enhancing compared to non-enhancing components had higher immune (R = 0.42, p = 0.009) and microenvironment (summed immune and stromal cell types; R = 0.44, p = 0.006) scores, regardless of their molecular subtype. Together, our results demonstrate: (1) the use of rich immune profiles for differentiating molecular subtypes of MB and their unique TME characterization; and (2) initial evidence for radiological correlates of these profiles based on pre-operative imaging collected through standard practices.
Abstract
Volumetric measurements of whole tumor and its components on MRI scans, facilitated by automatic segmentation tools, are essential to reduce inter-observer variability in monitoring tumor ...progression and response assessment for pediatric brain tumors. Here, we present a fully automatic segmentation model based on deep learning that reliably delineates the tumor components recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group for evaluation of treatment response. Multi-parametric MRI (mpMRI) scans (T1-pre, T1-post, T2, and T2-FLAIR), acquired on multiple MRI scanners with different field strengths and vendors, for a cohort of 218 pediatric patients with a variety of histologically confirmed brain tumor subtypes were collected. The mpMRI scans were co-registered and manually segmented by experienced neuroradiologists in consensus to identify the tumor subregions including the enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED) regions. A convolutional neural network model based on DeepMedic architecture was trained using mpMRI scans as the inputs for segmentation of the whole tumor and subregions. The trained model showed excellent performance in segmentation of the whole tumor, as suggested by median dice of 0.90/0.85 for validation (n = 44)/independent test (n = 22) sets. ET and non-enhancing components (union of NET, CC, and ED) were segmented with median dice scores of 0.78/0.84 and 0.76/0.74 for validation/test sets, respectively. The automated and manual segmentations demonstrated strong agreement in estimating VASARI (Visually AcceSAble Rembrandt Images) MRI features with Pearson’s correlation coefficient R > 0.75 (p < 0.0001) for ET, NET, CC, and ED components. Our proposed automated segmentation method developed based on MRI scans acquired with different protocols, equipment, and from a variety of brain tumor subtypes, shows potential application for reliable and generalizable volumetric measurements which can be used for treatment response assessment in clinical trials.
Abstract
PURPOSE: Treatment response is heterogeneous among patients with pediatric low-grade glioma (pLGG), the most frequent childhood brain tumor. Upfront prediction of progression-free survival ...(PFS) may facilitate more personalized treatment planning and improve outcomes for the pLGG patients. In this work, we explored the additive value of radiomics to clinical measures for prediction of PFS in pLGGs. We further sought associations between the derived risk groups and underlying alterations in key genomic and transcriptomic variables. METHODS: Quantitative radiomic features were extracted from pre-operative multi-parametric MRI scans (T1, T1-post, T2, T2-FLAIR) of 96 patients with newly diagnosed pLGG (median age, 8.59, range, 0.35-18.87 years; median PFS, 25.23, range, 3.03-124.83 months). Multivariate Cox proportional hazard’s (Cox-PH) regression models were fitted using 5-fold cross-validation on a training cohort of 68 subjects and tested on 28 patients. Three models were generated using (1) only clinical variables (age, sex, and extent of tumor resection), (2) radiomic features, and (3) clinical and radiomic variables. The dimensionality of radiomic features in Cox-PH models was reduced by applying Elastic Net regularization penalty to identify a subset of variables that are most predictive of PFS. The patients were then stratified into three groups of high, medium, and low-risk based on model predictions. RESULTS: Cox-PH modeling resulted in a concordance index (c-index) of 0.55 for clinical data, 0.65 for radiomics, and 0.73 for a combination of clinical and radiomic variables, highlighting the additive value of radiomics to the readily available clinical information in prediction of PFS. Radiogenomic assessments revealed significant differences in expression of BRAF, NF1, TSC1, ALK (p<0.01), and RB1 (p<0.05) genes in the high-risk group, compared to the medium and low-risk groups. CONCLUSION: Our results demonstrate the value of integrating radiomics with clinical measures to improve risk assessment of patients with pLGG through improved pretreatment prediction of PFS.
Abstract MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in ...Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumors from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.
PDF