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

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

Development and validation of a semiquantitative brain maturation score on fetal MR images: Initial results

PURPOSE: To develop a valid, reliable, and simple-to-use semiquantitative visual scale of fetal brain maturation for use in clinical fetal MR imaging assessment and interpretation. MATERIALS AND METHODS: This is a retrospective assessment of data from a previous study that was prospective, institutional review board approved, and HIPAA compliant. Forty-eight normal pregnancies with a gestational age (GA) of 25 to 35 weeks were studied. A fetal total maturation score (fTMS) was developed by utilizing six subscores that evaluated cortical sulcation, myelination, and the germinal matrix and provided a single combined numerical value to be evaluated as a marker of brain maturity. The fTMS was correlated with GA and segmented brain volume. A regression model that associated GA based on the visual fTMS scoring was determined. The model was validated with a leave-one-out cross validation procedure. RESULTS: Mean GA was 29.3 weeks ± 2.9 (standard deviation) (range, 25.2–35.3 weeks) and mean fTMS was 8.6 ± 3.7 (range, 4–16). The intraclass correlation coefficient between the three readers (independent and blinded) was 0.948 (P < .001). The correlations were as follows: GA and brain volume, r = 0.964 (P < .001); fTMS and brain volume, r = 0.970 (P < .001); and GA and fTMS, r = 0.975 (P < .001). A regression model to calculate GA based on fTMS yielded the following equation: calculated GA (weeks) = 22.86 + 0.748 fTMS (P < .001; adjusted R(2) = 0.946). The standard error of the model for calculation of fetal GA from the visual fTMS scale was ±4.8 days. CONCLUSION: If validated further, the fTMS scale might be used to assess morphologic brain maturity of fetuses between 25 and 35 weeks GA on a single-case basis in a clinical setting. © RSNA, 2013 Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13111715/-/DC