Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer.

Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT radiomics-based models for predicting EGFR mutation status were retrieved. Forest plots and related statistical tests were performed to summarize the model performance and inter-study heterogeneity. The methodological quality of the selected studies was assessed via the Radiomics Quality Score (RQS). The performance of the models was evaluated using the area under the curve (ROC AUC). The range of the Risk RQS across the selected articles varied from 11 to 24, indicating a notable heterogeneity in the quality and methodology of the included studies. The average score was 15.25, which accounted for 42.34% of the maximum possible score. The pooled Area Under the Curve (AUC) value was 0.801, indicating the accuracy of CT radiomics-based models in predicting the EGFR mutation status. CT radiomics-based models show promising results as non-invasive alternatives for predicting EGFR mutation status in NSCLC patients. However, the quality of the studies using CT radiomics-based models varies widely, and further harmonization and prospective validation are needed before the generalization of these models.

Failed remyelination of the nonhuman primate optic nerve leads to axon degeneration, retinal damages, and visual dysfunction.

White matter disorders of the central nervous system (CNS), such as multiple sclerosis (MS), lead to failure of nerve conduction and long-lasting neurological disabilities affecting a variety of sensory and motor systems, including vision. While most disease-modifying therapies target the immune and inflammatory response, the promotion of remyelination has become a new therapeutic avenue to prevent neuronal degeneration and promote recovery. Most of these strategies have been developed in short-lived rodent models of demyelination, which spontaneously repair and do not reflect the size, organization, and biology of the human CNS. Thus, well-defined nonhuman primate models are required to efficiently advance therapeutic approaches for patients. Here, we followed the consequence of long-term toxin-induced demyelination of the macaque optic nerve on remyelination and axon preservation, as well as its impact on visual functions. Findings from oculomotor behavior, ophthalmic examination, electrophysiology, and retinal imaging indicate visual impairment involving the optic nerve and retina. These visual dysfunctions fully correlated at the anatomical level, with sustained optic nerve demyelination, axonal degeneration, and alterations of the inner retinal layers. This nonhuman primate model of chronic optic nerve demyelination associated with axonal degeneration and visual dysfunction, recapitulates several key features of MS lesions and should be instrumental in providing the missing link to translate emerging repair promyelinating/neuroprotective therapies to the clinic for myelin disorders, such as MS.

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.

The intra-axonal water exchange time (τi), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R2 =0.99, τi: R2 =0.84, intrinsic diffusivity d: R2 =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τi). Finally, we find a statistically significant decrease in τi in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τi>=310ms/330ms/350ms) compared to the WT group (<τi>=370ms/370ms/380ms). This is in line with our expectations that τi is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .

Alterations of the nigrostriatal pathway in a 6-OHDA rat model of Parkinson's disease evaluated with multimodal MRI.

Parkinson's disease is characterized by neurodegeneration of the dopaminergic neurons in the substantia nigra pars compacta. The 6-hydroxydopamine (6-OHDA) rat model has been used to study neurodegeneration in the nigro-striatal dopaminergic system. The goal of this study was to evaluate the reliability of diffusion MRI and resting-state functional MRI biomarkers in monitoring neurodegeneration in the 6-OHDA rat model assessed by quantitative histology. We performed a unilateral injection of 6-OHDA in the striatum of Sprague Dawley rats to produce retrograde degeneration of the dopamine neurons in the substantia nigra pars compacta. We carried out a longitudinal study with a multi-modal approach combining structural and functional MRI together with quantitative histological validation to follow the effects of the lesion. Functional and structural connectivity were assessed in the brain of 6-OHDA rats and sham rats (NaCl injection) at 3 and 6 weeks post-lesioning using resting-state functional MRI and diffusion-weighted. Our results showed (i) increased functional connectivity in ipsi- and contra-lesioned regions of the cortico-basal ganglia network pathway including the motor cortex, the globus pallidus, and the striatum regions at 3 weeks; (ii) increased fractional anisotropy (FA) in the ipsi- and contralateral striatum of the 6-OHDA group at 3 weeks, and increased axial diffusivity (AD) and mean diffusivity in the ipsilateral striatum at 6 weeks; (iii) a trend for increased FA in both substantia nigra of the 6-OHDA group at 3 weeks. Optical density measurements of tyrosine-hydroxylase (TH) staining of the striatum showed good correlations with the FA and AD measurements in the striatum. No correlations were found between the number of TH-stained dopaminergic neurons and MRI measurements in the substantia nigra. This study suggested that (i) FA and AD were reliable biomarkers to evaluate neurodegeneration in the cortico-basal ganglia network of the 6-OHDA model, (ii) diffusion MRI and resting-state functional MRI (rsfMRI) were not sensitive enough to detect changes in the substantia nigra in this model.