Association of clinical and imaging characteristics with pulmonary function testing in patients with Long-COVID.

Purpose: The purpose of this study is to identify clinical and imaging characteristics associated with post-COVID pulmonary function decline. Methods: This study included 22 patients recovering from COVID-19 who underwent serial spirometry pulmonary function testing (PFT) before and after diagnosis. Patients were divided into two cohorts by difference between baseline and post-COVID follow-up PFT: Decline group (>10 % decrease in FEV1), and Stable group (≤10 % decrease or improvement in FEV1). Demographic, clinical, and laboratory data were collected, as well as PFT and chest computed tomography (CT) at the time of COVID diagnosis and follow-up. CTs were semi-quantitatively scored on a five-point severity scale for disease extent in each lobe by two radiologists. Mann-Whitney U-tests, T-tests, and Chi-Squared tests were used for comparison. P-values <0.05 were considered statistically significant. Results: The Decline group had a higher proportion of neutrophils (79.47 ± 4.83 % vs. 65.45 ± 10.22 %; p = 0.003), a higher absolute neutrophil count (5.73 ± 2.68 × 109/L vs. 3.43 ± 1.74 × 109/L; p = 0.031), and a lower proportion of lymphocytes (9.90 ± 4.20 % vs. 21.21 ± 10.97 %; p = 0.018) compared to the Stable group. The Decline group also had significantly higher involvement of ground-glass opacities (GGO) on follow-up chest CT [8.50 (4.50, 14.50) vs. 3.0 (1.50, 9.50); p = 0.032] and significantly higher extent of reticulations on chest CT at time of COVID diagnosis [6.50 (4.00, 9.00) vs. 2.00 (0.00, 6.00); p = 0.039] and follow-up [5.00 (3.00, 13.00) vs. 2.00 (0.00, 5.00); p = 0.041]. ICU admission was higher in the Decline group than in the Stable group (71.4 % vs. 13.3 %; p = 0.014). Conclusions: This study provides novel insight into factors influencing post-COVID lung function, irrespective of pre-existing pulmonary conditions. Our findings underscore the significance of neutrophil counts, reduced lymphocyte counts, pulmonary reticulation on chest CT at diagnosis, and extent of GGOs on follow-up chest CT as potential indicators of decreased post-COVID lung function. This knowledge may guide prediction and further understanding of long-term sequelae of COVID-19 infection.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.

Evaluation of iron deposition in the motor CSTC loop of a Chinese family with paroxysmal kinesigenic dyskinesia using quantitative susceptibility mapping.

Introduction: Previous studies have revealed structural, functional, and metabolic changes in brain regions inside the cortico-striatal-thalamo-cortical (CSTC) loop in patients with paroxysmal kinesigenic dyskinesia (PKD), whereas no quantitative susceptibility mapping (QSM)-related studies have explored brain iron deposition in these areas. Methods: A total of eight familial PKD patients and 10 of their healthy family members (normal controls) were recruited and underwent QSM on a 3T magnetic resonance imaging system. Magnetic susceptibility maps were reconstructed using a multi-scale dipole inversion algorithm. Thereafter, we specifically analyzed changes in local mean susceptibility values in cortical regions and subcortical nuclei inside the motor CSTC loop. Results: Compared with normal controls, PKD patients had altered brain iron levels. In the cortical gray matter area involved with the motor CSTC loop, susceptibility values were generally elevated, especially in the bilateral M1 and PMv regions. In the subcortical nuclei regions involved with the motor CSTC loop, susceptibility values were generally lower, especially in the bilateral substantia nigra regions. Conclusion: Our results provide new evidence for the neuropathogenesis of PKD and suggest that an imbalance in brain iron levels may play a role in PKD.

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this study is to develop a novel deep learning segmentation model for H&N cancer based on independent and combined CT and FDG-PET modalities.Approach. In this study, we developed a robust deep learning-based model leveraging information from both CT and PET. We implemented a 3D U-Net architecture with 5 levels of encoding and decoding, computing model loss through deep supervision. We used a channel dropout technique to emulate different combinations of input modalities. This technique prevents potential performance issues when only one modality is available, increasing model robustness. We implemented ensemble modeling by combining two types of convolutions with differing receptive fields, conventional and dilated, to improve capture of both fine details and global information.Main Results. Our proposed methods yielded promising results, with a Dice similarity coefficient (DSC) of 0.802 when deployed on combined CT and PET, DSC of 0.610 when deployed on CT, and DSC of 0.750 when deployed on PET.Significance. Application of a channel dropout method allowed for a single model to achieve high performance when deployed on either single modality images (CT or PET) or combined modality images (CT and PET). The presented segmentation techniques are clinically relevant to applications where images from a certain modality might not always be available.

Association between cortical gyrification and white matter integrity in spinocerebellar ataxia type 3.

Gray matter volume and thickness reductions have been reported in patients with spinocerebellar ataxia type 3 (SCA3), whereas cortical gyrification alterations of this disease remain largely unexplored. Using local gyrification index (LGI) and fractional anisotropy (FA) from structural and diffusion MRI data, this study investigated the cortical gyrification alterations as well as their relationship with white matter microstructural abnormalities in patients with SCA3 (n = 61) compared with healthy controls (n = 69). We found widespread reductions in cortical LGI and white matter FA in patients with SCA3 and that changes in these 2 features were also coupled. In the patient group, the LGI of the left middle frontal gyrus, bilateral insula, and superior temporal gyrus was negatively correlated with the severity of depressive symptoms, and the FA of a cluster in the left cerebellum was negatively correlated with the Scale for the Assessment and Rating of Ataxia scores. Our findings suggest that the gyrification abnormalities observed in this study may account for the clinical heterogeneity in SCA3 and are likely to be mediated by the underlying white matter microstructural abnormalities of this disease.

Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System.

BACKGROUND: Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. PURPOSE: To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin. STUDY TYPE: Retrospective. POPULATION: A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). FIELD STRENGTH/SEQUENCE: A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts. STATISTICAL TESTS: Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS: Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). DATA CONCLUSION: The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Changed cerebral function and morphology serve as neuroimaging evidence for subclinical type 2 diabetic polyneuropathy.

Introduction: Central and peripheral nervous systems are all involved in type 2 diabetic polyneuropathy mechanisms, but such subclinical changes and associations remain unknown. This study aims to explore subclinical changes of the central and peripheral and unveil their association. Methods: A total of 55 type-2 diabetes patients consisting of symptomatic (n = 23), subclinical (n = 12), and no polyneuropathy (n = 20) were enrolled in this study. Cerebral morphology, function, peripheral electrophysiology, and clinical information were collected and assessed using ANOVA and post-hoc analysis. Gaussian random field correction was used for multiple comparison corrections. Pearson/Spearman correlation analysis was used to evaluate the association of the cerebral with the peripheral. Results: When comparing the subclinical group with no polyneuropathy groups, no statistical differences were shown in peripheral evaluations except amplitudes of tibial nerves. At the same time, functional connectivity from the orbitofrontal to bilateral postcentral and middle temporal cortex increased significantly. Gray matter volume of orbitofrontal and its functional connectivity show a transient elevation in the subclinical group compared with the symptomatic group. Besides, gray matter volume in the orbitofrontal cortex negatively correlated with the Neuropathy Symptom Score (r = -0.5871, p < 0.001), Neuropathy Disability Score (r = -0.3682, p = 0.009), and Douleur Neuropathique en 4 questions (r = -0.4403, p = 0.003), and also found correlated positively with bilateral peroneal amplitude (r > 0.4, p < 0.05) and conduction velocities of the right sensory sural nerve(r = 0.3181, p = 0.03). Similarly, functional connectivity from the orbitofrontal to the postcentral cortex was positively associated with cold detection threshold (r = 0.3842, p = 0.03) and negatively associated with Neuropathy Symptom Score (r = -0.3460, p = 0.01). Discussion: Function and morphology of brain changes in subclinical type 2 diabetic polyneuropathy might serve as an earlier biomarker. Novel insights from subclinical stage to investigate the mechanism of type 2 diabetic polyneuropathy are warranted.

Functional Connectivity Density for Radiation Encephalopathy Prediction in Nasopharyngeal Carcinoma.

The diagnostic efficiency of radiation encephalopathy (RE) remains heterogeneous, and prediction of RE is difficult at the pre-symptomatic stage. We aimed to analyze the whole-brain resting-state functional connectivity density (FCD) of individuals with pre-symptomatic RE using multivariate pattern analysis (MVPA) and explore its prediction efficiency. Resting data from NPC patients with nasopharyngeal carcinoma (NPC; consisting of 20 pre-symptomatic RE subjects and 26 non-RE controls) were collected in this study. We used MVPA to classify pre-symptomatic RE subjects from non-RE controls based on FCD maps. Classifier performances were evaluated by accuracy, sensitivity, specificity, and area under the characteristic operator curve. Permutation tests and leave-one-out cross-validation were applied for assessing classifier performance. MVPA was able to differentiate pre-symptomatic RE subjects from non-RE controls using global FCD as a feature, with a total accuracy of 89.13%. The temporal lobe as well as regions involved in the visual processing system, the somatosensory system, and the default mode network (DMN) revealed robust discrimination during classification. Our findings suggest a good classification efficiency of global FCD for the individual prediction of RE at a pre-symptomatic stage. Moreover, the discriminating regions may contribute to the underlying mechanisms of sensory and cognitive disturbances in RE.

Aberrant Brain Network Integration and Segregation in Diabetic Peripheral Neuropathy Revealed by Structural Connectomics.

Diabetic peripheral neuropathy (DPN) is one of the most common forms of peripheral neuropathy, and its incidence has been increasing. Mounting evidence has shown that patients with DPN have been associated with widespread alterations in the structure, function and connectivity of the brain, suggesting possible alterations in large-scale brain networks. Using structural covariance networks as well as advanced graph-theory-based computational approaches, we investigated the topological abnormalities of large-scale brain networks for a relatively large sample of patients with DPN (N = 67) compared to matched healthy controls (HCs; N = 88). Compared with HCs, the structural covariance networks of patients with DPN showed an increased characteristic path length, clustering coefficient, sigma, transitivity, and modularity, suggestive of inefficient global integration and increased local segregation. These findings may improve our understanding of the pathophysiological mechanisms underlying alterations in the central nervous system of patients with DPN from the perspective of large-scale structural brain networks.