Aims: To estimate the long term cost effectiveness of treatment for amblyopia in 3 year old children. Methods: A cost utility analysis was performed using decision analysis including a Markov state transition model. Incremental costs and effects during the children’s remaining lifetime were estimated. The model took into account the costs and success rate of treatment as well as effects of unilateral and bilateral visual impairment caused by amblyopia and other eye diseases coming along later in life on quality of life (utility). Model parameter values were obtained from the literature, and from a survey of experts. For the utility of unilateral visual impairment a base value of 0.96 was assumed. Costs were estimated from a third party payer perspective for the year 2002 in Germany. Costs and effects were discounted at 3%. Uncertainty was assessed by univariate and probabilistic sensitivity analysis (Monte-Carlo simulation). Results: The incremental cost effectiveness ratio (ICER) of treatment was €2369 per quality adjusted life year (QALY). In univariate sensitivity analysis the ICER was most sensitive to uncertainty concerning the utility of unilateral visual impairment—for example, if this utility was 0.99, the ICER would be €9148/QALY. Monte-Carlo simulation yielded a 95% uncertainty interval for the ICER of €710/QALY to €38 696/QALY; the probability of an ICER smaller than €20 000/QALY was 95%. Conclusion: Treatment for amblyopia is likely to be very cost effective. Much of the uncertainty in results comes from the uncertainty regarding the effect of amblyopia on quality of life. In order to reduce this uncertainty the impact of amblyopia on utility should be investigated.

The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in somatosensory and pain processing remains unclear. Recently, MR imaging techniques have been developed for objective in vivo observation of the DRG. In particular, DRG MR imaging endpoints such as DRG volume and DRG T2w signal are emerging as biomarkers with initial evidence of meaningful correlations with biochemical and genetic parameters as well as neuropathic pain as clinically relevant applications. However, the future validation and use of these novel imaging biomarkers critically depends on the development of fully automated methods for DRG image analysis. To date, DRG detection and evaluation on MR images has been limited to expert annotation through manual segmentation. Fast and operator-independent, yet accurate and robust, segmentation methods are required to enable observation of larger patient cohorts and across multiple sites. Thus, fully automated DRG segmentation is a prerequisite for the analysis of more complex microstructural and functional image datasets, such as from DRG diffusion tensor or perfusion metabolic imaging. Here, we developed a fully automated DRG segmentation workflow based on deep learning. A convolutional neural network (CNN) was trained using the nnU-Net framework on a large dataset of high-resolution 3D T2-weighted MR images of healthy controls (220 DRGs). Automated DRG segmentations generated with this network were on par with expert annotations (dice similarity coefficient of 0.87 for human expert vs. 0.89 for trained CNN) while being faster by a factor of 10. Finally, we validated the method in Fabry disease as a genetic model disorder for DRG pathomorphological injury. The trained CNN was able to reproduce the manually segmented changes known in FD patients as a function of FD genotype and FD pain phenotype. We developed a fully automated method for DRG MRI segmentation and validated its application as a novel imaging biomarker using the DRG injury example of Fabry disease.

Background/Objectives: Fabry disease (FD) is a rare X-linked lysosomal storage disorder characterised by progressive glycolipid accumulation affecting multiple organs, including the peripheral nervous system. The dorsal root ganglia (DRG) play a key role in Fabry-related neuropathy, but non-invasive biomarkers of DRG involvement and their association with overall disease severity remain limited. This study evaluated lumbosacral DRG T2 relaxation time (DRG-T2) in FD patients as a potential imaging biomarker of FD severity. Methods: In a prospective, single-centre study, 80 genetically confirmed FD patients underwent 3T MRI with quantitative T2 mapping of the lumbosacral DRG. DRG-T2 was analysed in relation to sex, genetic subtype and Fabry-specific biomarkers. Results: Results showed that DRG-T2 was higher in patients with classical FD mutations than in those with nonclassical mutations (p = 0.03). Furthermore, DRG-T2 showed a negative correlation with body weight (ρ = −0.31, p = 0.005) and BMI (ρ = −0.32, p = 0.004), while no associations were found with lyso-Gb3 levels or alpha-galactosidase A activity. The inter-rater and test–retest reliability of DRG-T2 were good to excellent (ICC = 0.76 and 0.89, respectively). Conclusions: These results demonstrate DRG-T2 as a marker of neuronal involvement, making it a strong and reliable imaging biomarker of disease severity in FD. However, future studies need to correlate its changes with clinical and histological studies.

Dorsal root ganglia (DRG) of the peripheral nervous system (PNS) transmit information from inside and outside the body to the central nervous system (CNS). The DRG's unique multicellular unit of neurons, endothelial cells, satellite glial and immune cells undergoes remarkable changes after nerve injury and in neuropathic pain conditions and is a target for local and systemic pain therapies. DRG-magnetic resonance imaging (DRG-MRI) visualizes structural DRG injury in various neuropathic pain syndromes and has become a promising tool for detection and monitoring of DRG-pathology in humans. Here, we provide novel insights for murine DRG-MRI by applying MR-microscopy and high field MR-sequences optimized for DRG visualization and DRG-volumetry. We characterize murine DRG-volume (DRG_vol) as level specific for the lumbar levels L2 < L3 < L4 (p < 0.001) and positively correlated to height (p < 0.001), age (p < 0.001) and weight (p < 0.05) but independent of sex in a cohort of 24 wildtype mice. In a proof of principle experiment we report DRG-enlargement by up to 30 % in an α-galactosidase A deficient mouse model of Fabry disease (p < 0.01). Finally, we demonstrate the DRG's high microvascular perfusion compared to muscle and spinal cord in dynamic contrast enhanced MRI in a subcohort of six wildtype mice. DRG-MRI is a promising non-invasive imaging method, applicable not only to humans but also to rodent models, which are frequently utilized in pain research. The parallel use of DRG-MRI in humans and rodents can enhance comparability and translatability in preclinical and clinical pain research thoroughly.

Background/Objectives: Fabry disease (FD) is a lysosomal storage disorder often associated with early-onset neuropathic pain, attributed to small fibre neuropathy (SFN). The dorsal root ganglion (DRG) has emerged as a critical site of early pathophysiological involvement in FD, with structural and functional alterations implicated in the development of neuropathic symptoms. This exploratory study introduces DRG proton density (DRG-PD) as a novel MRI-derived biomarker and evaluates its association with SFN. Methods: Eighty genetically confirmed FD patients underwent high-resolution 3T MRI with DRG-PD quantification at the lumbosacral levels L5 and S1. DRG-PD was derived from B1-corrected multi-echo spin echo sequences and normalised to cerebrospinal fluid intensity. All patients underwent clinical, biochemical and histological evaluation to determine SFN status. Associations between DRG imaging parameters and clinical variables were analysed using correlation and regression models. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. Results: DRG-PD values were significantly increased in patients with classical FD and SFN, demonstrating a large effect size (Cliff’s δ = 0.92) and excellent discriminatory performance (AUC = 0.96). In contrast, DRG volume and T2 relaxation time were not significantly associated with SFN status. DRG-PD remained an independent predictor of SFN in multivariable logistic regression (p = 0.019). Conclusions: DRG-PD is a non-invasive correlate of SFN in classical FD. It may provide superior diagnostic value compared to existing MRI metrics and reflects proximal ganglionic pathology not captured by distal histological assessments.

Purpose: To evaluate MRI changes in T2-weighted imaging (T2WI) signal intensity (T2SI) as a potential imaging marker for assessing response to radiotherapy (RT) in pediatric low-grade glioma (pLGG). Materials and methods: This retrospective study analyzed imaging data of 56 pLGG patients (mean age, 12.4 ± 3.5 years; 33/56 [58.9%] male) treated with photon-based or proton-based RT within the SIOP-LGG 2004 study and registry. Tumor signal characteristics on T2WI were qualitatively and quantitatively assessed at baseline and up to 24 months post-RT. Tumor volumes were calculated, and correlations between ∆T2SI and volumetric changes were examined. Statistical tests included inferential tests, correlation analysis, and linear regression. Results: At baseline, 87.5% tumors were rated as hyperintense, while none was rated hypointense. The mean ratio between T2SI of the tumors compared to the cerebral cortex was 1.70. A significant decrease in T2SI was observed over time with the strongest decrease at 24 months post-RT (− 18.7%; p = 0.002). ∆T2SI correlated significantly with tumor volume reduction (r = 0.46, p < 0.001) and response assessment (ρ = 0.51, p < 0.001). There was no significant influence of age, sex, tumor location, histology, or RT type on ∆T2SI. Cases of pseudoprogression cases exhibited stable T2SI despite transient increases in contrast enhancement or tumor volume. Conclusion: A reduction in T2SI was consistently associated with tumor volume reduction, suggesting that a decrease in T2SI may serve as an additional imaging marker of a positive response to RT in pLGG patients.