Joint Modeling of RNAseq and Radiomics Data for Glioma Molecular Characterization and Prediction

RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. In this study, we propose a novel radiogenomics-NB model for glioma grading and prediction. Our radiogenomics-NB model is developed based on differentially expressed RNAseq and selected radiomics/volumetric features which characterize tumor volume and sub-regions. The NB distribution is fitted to RNAseq counts data, and a log-linear regression model is assumed to link between the estimated NB mean and radiomics. Three radiogenomics-NB molecular mutation models (e.g., IDH mutation, 1p/19q codeletion, and ATRX mutation) are investigated. Additionally, we explore gender-specific effects on the radiogenomics-NB models. Finally, we compare the performance of the proposed three mutation prediction radiogenomics-NB models with different well-known methods in the literature: Negative Binomial Linear Discriminant Analysis (NBLDA), differentially expressed RNAseq with Random Forest (RF-genomics), radiomics and differentially expressed RNAseq with Random Forest (RF-radiogenomics), and Voom-based count transformation combined with the nearest shrinkage classifier (VoomNSC). Our analysis shows that the proposed radiogenomics-NB model significantly outperforms (ANOVA test, p \u3c 0.05) for prediction of IDH and ATRX mutations and offers similar performance for prediction of 1p/19q codeletion, when compared to the competing models in the literature, respectively

Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models

Modified Pediatric ASPECTS Correlates with Infarct Volume in Childhood Arterial Ischemic Stroke

Background and Purpose: Larger infarct volume as a percent of supratentorial brain volume (SBV) predicts poor outcome and hemorrhagic transformation in childhood arterial ischemic stroke (AIS). In perinatal AIS, higher scores on a modified pediatric version of the Alberta Stroke Program Early CT Score using acute MRI (modASPECTS) predict later seizure occurrence. The objectives were to establish the relationship of modASPECTS to infarct volume in perinatal and childhood AIS and to establish the interrater reliability of the score. Methods: We performed a cross sectional study of 31 neonates and 40 children identified from a tertiary care center stroke registry with supratentorial AIS and acute MRI with diffusion weighted imaging (DWI) and T2 axial sequences. Infarct volume was expressed as a percent of SBV using computer-assisted manual segmentation tracings. ModASPECTS was performed on DWI by three independent raters. The modASPECTS were compared among raters and to infarct volume as a percent of SBV. Results: ModASPECTS correlated well with infarct volume. Spearman rank correlation coefficients (ρ) for the perinatal and childhood groups were 0.76, p < 0.001 and 0.69, p < 0.001, respectively. Excluding one perinatal and two childhood subjects with multifocal punctate ischemia without large or medium sized vessel stroke, ρ for the perinatal and childhood groups were 0.87, p < 0.001 and 0.80, p < 0.001, respectively. The intraclass correlation coefficients for the three raters for the neonates and children were 0.93 [95% confidence interval (CI) 0.89–0.97, p < 0.001] and 0.94 (95% CI 0.91–0.97, p < 0.001), respectively. Conclusion: The modified pediatric ASPECTS on acute MRI can be used to estimate infarct volume as a percent of SBV with a high degree of validity and interrater reliability

Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors

Unsupervised Machine Learning Using K-Means Identifies Radiomic Subgroups of Pediatric Low-Grade Gliomas That Correlate With Key Molecular Markers

Introduction: Despite advancements in molecular and histopathologic characterization of pediatric low-grade gliomas (pLGGs), there remains significant phenotypic heterogeneity among tumors with similar categorizations. We hypothesized that an unsupervised machine learning approach based on radiomic features may reveal distinct pLGG imaging subtypes. Methods: Multi-parametric MR images (T1 pre- and post-contrast, T2, and T2 FLAIR) from 157 patients with pLGGs were collected and 881 quantitative radiomic features were extracted from tumorous region. Clustering was performed using K-means after applying principal component analysis (PCA) for feature dimensionality reduction. Molecular and demographic data was obtained from the PedCBioportal and compared between imaging subtypes. Results: K-means identified three distinct imaging-based subtypes. Subtypes differed in mutational frequencies of BRAF (p \u3c 0.05) as well as the gene expression of BRAF (p\u3c0.05). It was also found that age (p \u3c 0.05), tumor location (p \u3c 0.01), and tumor histology (p \u3c 0.0001) differed significantly between the imaging subtypes. Conclusion: In this exploratory work, it was found that clustering of pLGGs based on radiomic features identifies distinct, imaging-based subtypes that correlate with important molecular markers and demographic details. This finding supports the notion that incorporation of radiomic data could augment our ability to better characterize pLGGs

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

Integrating neuroimaging biomarkers into the multicentre, high-dose erythropoietin for asphyxia and encephalopathy (HEAL) trial: rationale, protocol and harmonisation

Introduction: MRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms. Methods and analysis: Neonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation-thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans. Ethics and dissemination: IRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor

The Persistence of MRI Findings in Pediatric Pseudotumor Cerebri Syndrome

MRI findings, including optic nerve sheath distention (ONSD), pituitary gland flattening (PGF), flattening of the posterior sclera (FPS) and transverse sinus stenosis (TSS), can be used to diagnose pseudotumor cerebri syndrome (PTCS), when papilledema is absent. At least three imaging findings have a robust specificity for definite PTCS, of which TSS is the most specific. It is not known whether these findings persist on follow-up imaging in the pediatric population

MRI Findings in Children with Pseudotumor Cerebri Syndrome (PTCS), Intracranial Hypertension, and Normal Opening Pressure without Papilledema (.pdf)

Revised diagnostic criteria for pseudotumor cerebri syndrome (PTCS) include findings on neuroimaging, which can be used if papilledema is not present. We compared neuroimaging in children suspected to have PTCS, in order to facilitate the identification and treatment

Adult Onset Leukodystrophy with Neuroaxonal Spheroids: Clinical, Neuroimaging and Neuropathologic Observations

Pigmented orthochromatic leukodystrophy (POLD) and Hereditary diffuse leukoencephalopathy with spheroids HDLS are two adult onset leukodystrophies with neuroaxonal spheroids presenting with prominent neurobehavioral, cognitive, and motor symptoms. These are familial or sporadic disorders characterized by cerebral white matter degeneration including myelin and axonal loss, gliosis, macrophages, and axonal spheroids. We report clinical, neuroimaging and pathological correlations of four women ages 34–50 years with adult onset leukodystrophy. Their disease course ranged from 1.5–8 years. Three patients had progressive cognitive and behavioral changes whereas one had acute onset. Neuroimaging revealed white matter abnormalities characterized by symmetric, bilateral, T2 hyperintense and T1 hypointense MRI signal involving frontal lobe white matter in all patients. Extensive laboratory investigations were negative apart from abnormalities in some mitochondrial enzymes and immunologic parameters. Autopsies demonstrated severe leukodystrophy with myelin and axonal loss, axonal spheroids, and macrophages with early and severe frontal white matter involvement. The extent and degree of changes outside the frontal lobe appeared to correlate with disease duration. The prominent neurobehavioral deficits and frontal white matter disease provides clinical-pathologic support for association pathways linking distributed neural circuits subserving cognition. These observations lend further support to the notion that white matter disease alone can account for dementia