Neuroimaging Features of Cytokine-related Diseases.

Cytokines are small secreted proteins that have specific effects on cellular interactions and are crucial for functioning of the immune system. Cytokines are involved in almost all diseases, but as microscopic chemical compounds they cannot be visualized at imaging for obvious reasons. Several imaging manifestations have been well recognized owing to the development of cytokine therapies such as those with bevacizumab (antibody against vascular endothelial growth factor) and chimeric antigen receptor (CAR) T cells and the establishment of new disease concepts such as interferonopathy and cytokine release syndrome. For example, immune effector cell-associated neurotoxicity is the second most common form of toxicity after CAR T-cell therapy toxicity, and imaging is recommended to evaluate the severity. The emergence of COVID-19, which causes a cytokine storm, has profoundly impacted neuroimaging. The central nervous system is one of the systems that is most susceptible to cytokine storms, which are induced by the positive feedback of inflammatory cytokines. Cytokine storms cause several neurologic complications, including acute infarction, acute leukoencephalopathy, and catastrophic hemorrhage, leading to devastating neurologic outcomes. Imaging can be used to detect these abnormalities and describe their severity, and it may help distinguish mimics such as metabolic encephalopathy and cerebrovascular disease. Familiarity with the neuroimaging abnormalities caused by cytokine storms is beneficial for diagnosing such diseases and subsequently planning and initiating early treatment strategies. The authors outline the neuroimaging features of cytokine-related diseases, focusing on cytokine storms, neuroinflammatory and neurodegenerative diseases, cytokine-related tumors, and cytokine-related therapies, and describe an approach to diagnosing cytokine-related disease processes and their differentials. ©RSNA, 2024 Supplemental material is available for this article.

Radiologic Abnormalities in Prolonged SARS-CoV-2 Infection: A Systematic Review.

We systematically reviewed radiological abnormalities in patients with prolonged SARS-CoV-2 infection, defined as persistently positive polymerase chain reaction (PCR) results for SARS-CoV-2 for > 21 days, with either persistent or relapsed symptoms. We extracted data from 24 patients (median age, 54.5 [interquartile range, 44-64 years]) reported in the literature and analyzed their representative CT images based on the timing of the CT scan relative to the initial PCR positivity. Our analysis focused on the patterns and distribution of CT findings, severity scores of lung involvement on a scale of 0-4, and the presence of migration. All patients were immunocompromised, including 62.5% (15/24) with underlying lymphoma and 83.3% (20/24) who had received anti-CD20 therapy within one year. Median duration of infection was 90 days. Most patients exhibited typical CT appearance of coronavirus disease 19 (COVID-19), including ground-glass opacities with or without consolidation, throughout the follow-up period. Notably, CT severity scores were significantly lower during ≤ 21 days than during > 21 days (P < 0.001). Migration was observed on CT in 22.7% (5/22) of patients at ≤ 21 days and in 68.2% (15/22) to 87.5% (14/16) of patients at > 21 days, with rare instances of parenchymal bands in previously affected areas. Prolonged SARS-CoV-2 infection usually presents as migrating typical COVID-19 pneumonia in immunocompromised patients, especially those with impaired B-cell immunity.

Clinical and Imaging Characteristics of SARS-CoV-2 Breakthrough Infection in Hospitalized Immunocompromised Patients.

OBJECTIVE: To evaluate the clinical and imaging characteristics of SARS-CoV-2 breakthrough infection in hospitalized immunocompromised patients in comparison with immunocompetent patients. MATERIALS AND METHODS: This retrospective study analyzed consecutive adult patients hospitalized for COVID-19 who received at least one dose of the SARS-CoV-2 vaccine at two academic medical centers between June 2021 and December 2022. Immunocompromised patients (with active solid organ cancer, active hematologic cancer, active immune-mediated inflammatory disease, status post solid organ transplantation, or acquired immune deficiency syndrome) were compared with immunocompetent patients. Multivariable logistic regression analysis was performed to evaluate the effect of immune status on severe clinical outcomes (in-hospital death, mechanical ventilation, or intensive care unit admission), severe radiologic pneumonia (≥ 25% of lung involvement), and typical CT pneumonia. RESULTS: Of 2218 patients (mean age, 69.5 ± 16.1 years), 274 (12.4%), and 1944 (87.6%) were immunocompromised an immunocompetent, respectively. Patients with active solid organ cancer and patients status post solid organ transplantation had significantly higher risks for severe clinical outcomes (adjusted odds ratio = 1.58 [95% confidence interval {CI}, 1.01-2.47], P = 0.042; and 3.12 [95% CI, 1.47-6.60], P = 0.003, respectively). Patient status post solid organ transplantation and patients with active hematologic cancer were associated with increased risks for severe pneumonia based on chest radiographs (2.96 [95% CI, 1.54-5.67], P = 0.001; and 2.87 [95% CI, 1.50-5.49], P = 0.001, respectively) and for typical CT pneumonia (9.03 [95% CI, 2.49-32.66], P < 0.001; and 4.18 [95% CI, 1.70-10.25], P = 0.002, respectively). CONCLUSION: Immunocompromised patients with COVID-19 breakthrough infection showed an increased risk of severe clinical outcome, severe pneumonia based on chest radiographs, and typical CT pneumonia. In particular, patients status post solid organ transplantation was specifically found to be associated with a higher risk of all three outcomes than hospitalized immunocompetent patients.

Increasing the scan-efficiency of pulmonary imaging at 0.55 T using iterative concomitant field and motion-corrected reconstruction.

PURPOSE: To develop an iterative concomitant field and motion corrected (iCoMoCo) reconstruction for isotropic high-resolution UTE pulmonary imaging at 0.55 T. METHODS: A free-breathing golden-angle stack-of-spirals UTE sequence was used to acquire data for 8 min with prototype and commercial 0.55 T MRI scanners. The data was binned into 12 respiratory phases based on superior-inferior navigator readouts. The previously published iterative motion corrected (iMoCo) reconstruction was extended to include concomitant field correction directly in the cost function. The reconstruction was implemented within the Gadgetron framework for inline reconstruction. Data were retrospectively reconstructed to simulate scan times of 2, 4, 6, and 8 min. Image quality was assessed using apparent SNR and image sharpness. The technique was evaluated in healthy volunteers and patients with known lung pathology including coronavirus disease 2019 infection, chronic granulomatous disease, lymphangioleiomyomatosis, and lung nodules. RESULTS: The technique provided diagnostic-quality images, and image quality was maintained with a slight loss in SNR for simulated scan times down to 4 min. Parenchymal apparent SNR was 4.33 ± 0.57, 5.96 ± 0.65, 7.36 ± 0.64, and 7.87 ± 0.65 using iCoMoCo with scan times of 2, 4, 6, and 8 min, respectively. Image sharpness at the diaphragm was comparable between iCoMoCo and reference images. Concomitant field corrections visibly improved the sharpness of anatomical structures away from the isocenter. Inline image reconstruction and artifact correction were achieved in <5 min. CONCLUSION: The proposed iCoMoCo pulmonary imaging technique can generate diagnostic quality images with 1.75 mm isotropic resolution in less than 5 min using a 6-min acquisition, on a 0.55 T scanner.

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challenges, particularly in achieving high edge accuracy and addressing pixel classification errors. To tackle these issues, this study proposes the MIS-Net (Medical Images Segment Net) model, a deep learning-based approach. The MIS-Net model incorporates multi-scale atrous convolution into the encoding and decoding structure with symmetry, enabling the comprehensive extraction of multi-scale features from CT images. This enhancement aims to improve the accuracy of lung and liver edge segmentation. In the evaluation using the COVID-19 CT Lung and Infection Segmentation dataset, the left and right lung segmentation results demonstrate that MIS-Net achieves a Dice Similarity Coefficient (DSC) of 97.61. Similarly, in the Liver Tumor Segmentation Challenge 2017 public dataset, the DSC of MIS-Net reaches 98.78.

Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder.

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.

BACKGROUND: Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. OBJECTIVE: This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. METHODS: The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. RESULTS: This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic.

OBJECTIVE: To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia. METHODS: This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors. RESULTS: A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020. CONCLUSIONS: Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives.

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

VEGF-A plasma levels are associated with impaired DLCO and radiological sequelae in long COVID patients.

BACKGROUND: Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is characterized by persistent clinical symptoms following COVID-19. OBJECTIVE: To correlate biomarkers of endothelial dysfunction with persistent clinical symptoms and pulmonary function defects at distance from COVID-19. METHODS: Consecutive patients with long COVID-19 suspicion were enrolled. A panel of endothelial biomarkers was measured in each patient during clinical evaluation and pulmonary function test (PFT). RESULTS: The study included 137 PASC patients, mostly male (68%), with a median age of 55 years. A total of 194 PFTs were performed between months 3 and 24 after an episode of SARS-CoV-2 infection. We compared biomarkers evaluated in PASC patients with 20 healthy volunteers (HVs) and acute hospitalized COVID-19 patients (n = 88). The study found that angiogenesis-related biomarkers and von Willebrand factor (VWF) levels were increased in PASC patients compared to HVs without increased inflammatory or platelet activation markers. Moreover, VEGF-A and VWF were associated with persistent lung CT scan lesions and impaired diffusing capacity of the lungs for carbon monoxide (DLCO) measurement. By employing a Cox proportional hazards model adjusted for age, sex, and body mass index, we further confirmed the accuracy of VEGF-A and VWF. Following adjustment, VEGF-A emerged as the most significant predictive factor associated with persistent lung CT scan lesions and impaired DLCO measurement. CONCLUSION: VEGF-A is a relevant predictive factor for DLCO impairment and radiological sequelae in PASC. Beyond being a biomarker, we hypothesize that the persistence of angiogenic disorders may contribute to long COVID symptoms.

Thyroid and adrenal incidentalomas on chest CT: Prevalence, diagnostic work-up and outcomes in a cohort of COVID-19 suspected patients.

OBJECTIVE: Due to increased use of computed tomography (CT), prevalence of thyroid and adrenal incidentalomas is rising. Yet, previous studies on the outcomes of diagnostic work-up of incidentalomas are subjected to inclusion bias. Therefore, we aimed to investigate prevalence and outcomes of diagnostic work-up of thyroid and adrenal incidentalomas detected on chest CT in a less selected population of COVID-19 suspected patients. DESIGN: A retrospective, observational cohort study. METHODS: We included all COVID-19 suspected patients who underwent chest CT between March 2020 and March 2021. Radiology reports and medical records were reviewed for the presence and subsequent diagnostic work-up of thyroid and adrenal incidentalomas. RESULTS: A total of 1,992 consecutive COVID-19 patients were included (59.4% male, median age 71 years [IQR: 71-80]). Thyroid and adrenal incidentalomas were identified in 95 (4.8%) and 133 (6.7%) patients, respectively. Higher prevalence was observed with increasing age, among female patients and in patients with malignancy. Forty-four incidentalomas were further analyzed, but no malignancies were found. Only three lesions were hormonally active (1 thyrotoxicosis and 2 mild autonomous cortisol secretion). Diagnostic work-up did not lead to any change in clinical management in 97.7% of the analyzed patients. CONCLUSION: Prevalence rates of thyroid and adrenal incidentalomas on chest CT in a less selected COVID-19 cohort were 4.8% and 6.7%, respectively. Yet, as all incidentalomas turned out to be benign and only three lesions were (mildly) hormonally active, this raises the question whether intensive diagnostic work-up of incidentalomas is necessary in all patients.

Three-Dimensional Remodeling of SARS-CoV2-Infected Cells Revealed by Cryogenic Soft X-ray Tomography.

Plus-strand RNA viruses are proficient at remodeling host cell membranes for optimal viral genome replication and the production of infectious progeny. These ultrastructural alterations result in the formation of viral membranous organelles and may be observed by different imaging techniques, providing nanometric resolution. Guided by confocal and electron microscopy, this study describes the generation of wide-field volumes using cryogenic soft-X-ray tomography (cryo-SXT) on SARS-CoV-2-infected human lung adenocarcinoma cells. Confocal microscopy showed accumulation of double-stranded RNA (dsRNA) and nucleocapsid (N) protein in compact perinuclear structures, preferentially found around centrosomes at late stages of the infection. Transmission electron microscopy (TEM) showed accumulation of membranous structures in the vicinity of the infected cell nucleus, forming a viral replication organelle containing characteristic double-membrane vesicles and virus-like particles within larger vesicular structures. Cryo-SXT revealed viral replication organelles very similar to those observed by TEM but indicated that the vesicular organelle observed in TEM sections is indeed a vesiculo-tubular network that is enlarged and elongated at late stages of the infection. Overall, our data provide additional insight into the molecular architecture of the SARS-CoV-2 replication organelle.

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma.

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[18F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen's k: 0.91, CI 0.86-0.95; p-value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[18F]-FDG PET/CT and SARS-CoV-2 vaccination.

Molecular Imaging of ACE2 Expression in Infectious Disease and Cancer.

Angiotensin-converting enzyme 2 (ACE2) is a cell-surface receptor that plays a critical role in the pathogenesis of SARS-CoV-2 infection. Through the use of ligands engineered for the receptor, ACE2 imaging has emerged as a valuable tool for preclinical and clinical research. These can be used to visualize the expression and distribution of ACE2 in tissues and cells. A variety of techniques including optical, magnetic resonance, and nuclear medicine contrast agents have been developed and employed in the preclinical setting. Positron-emitting radiotracers for highly sensitive and quantitative tomography have also been translated in the context of SARS-CoV-2-infected and control patients. Together this information can be used to better understand the mechanisms of SARS-CoV-2 infection, the potential roles of ACE2 in homeostasis and disease, and to identify potential therapeutic modulators in infectious disease and cancer. This review summarizes the tools and techniques to detect and delineate ACE2 in this rapidly expanding field.

First-in-human immunoPET imaging of COVID-19 convalescent patients using dynamic total-body PET and a CD8-targeted minibody.

With most of the T cells residing in the tissue, not the blood, developing noninvasive methods for in vivo quantification of their biodistribution and kinetics is important for studying their role in immune response and memory. This study presents the first use of dynamic positron emission tomography (PET) and kinetic modeling for in vivo measurement of CD8+ T cell biodistribution in humans. A 89Zr-labeled CD8-targeted minibody (89Zr-Df-Crefmirlimab) was used with total-body PET in healthy individuals (N = 3) and coronavirus disease 2019 (COVID-19) convalescent patients (N = 5). Kinetic modeling results aligned with T cell-trafficking effects expected in lymphoid organs. Tissue-to-blood ratios from the first 7 hours of imaging were higher in bone marrow of COVID-19 convalescent patients compared to controls, with an increasing trend between 2 and 6 months after infection, consistent with modeled net influx rates and peripheral blood flow cytometry analysis. These results provide a promising platform for using dynamic PET to study the total-body immune response and memory.

Impact of the COVID-19 Pandemic on Iron Overload Assessment by MRI in Patients with Hemoglobinopathies: The E-MIOT Network Experience.

BACKGROUND: The E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project is an Italian Network assuring high-quality quantification of tissue iron overload by magnetic resonance imaging (MRI). We evaluated the impact of the COVID-19 pandemic on E-MIOT services. METHODS: The activity of the E-MIOT Network MRI centers in the year 2020 was compared with that of 2019. A survey evaluated whether the availability of MRI slots for patients with hemoglobinopathies was reduced and why. RESULTS: The total number of MRI scans was 656 in 2019 and 350 in 2020, with an overall decline of 46.4% (first MRI: 71.7%, follow-up MRI: 36.9%), a marked decline (86.9%) in the period March-June 2020, and a reduction in the gap between the two years in the period July-September. A new drop (41.4%) was recorded in the period October-December for two centers, due to the general reduction in the total amount of MRIs/day for sanitization procedures. In some centers, patients refused MRI scans for fear of getting COVID. Drops in the MRI services >80% were found for patients coming from a region without an active MRI site. CONCLUSIONS: The COVID-19 pandemic had a strong negative impact on MRI multi-organ iron quantification, with a worsening in the management of patients with hemoglobinopathies.

Chest CT features and functional correlates of COVID-19 at 3 months and 12 months follow-up.

Long-term pulmonary sequelae of Coronavirus 2019 (COVID-19) remain unclear. Thus, we aimed to establish post-COVID-19 temporal changes in chest computed tomography (CT) features of pulmonary fibrosis and to investigate associations with respiratory symptoms and physiological parameters at 3 and 12 months' follow-up. Adult patients who attended our initial COVID-19 follow-up service and developed chest CT features of interstitial lung disease, in addition to cases identified using British Society of Thoracic Imaging codes, were evaluated retrospectively. Clinical data were gathered on respiratory symptoms and physiological parameters at baseline, 3 months, and 12 months. Corresponding chest CT scans were reviewed by two thoracic radiologists. Associations between CT features and functional correlates were estimated using random effects logistic or linear regression adjusted for age, sex and body mass index. In total, 58 patients were assessed. No changes in reticular pattern, honeycombing, traction bronchiectasis/bronchiolectasis index or pulmonary distortion were observed. Subpleural curvilinear lines were associated with lower odds of breathlessness over time. Parenchymal bands were not associated with breathlessness or impaired lung function overall. Based on our results, we conclude that post-COVID-19 chest CT features of irreversible pulmonary fibrosis remain static over time; other features either resolve or remain unchanged. Subpleural curvilinear lines do not correlate with breathlessness. Parenchymal bands are not functionally significant. An awareness of the different potential functional implications of post-COVID-19 chest CT changes is important in the assessment of patients who present with multi-systemic sequelae of COVID-19 infection.

Clinical application of radiological AI for pulmonary nodule evaluation: Replicability and susceptibility to the population shift caused by the COVID-19 pandemic.

PURPOSE: replicability and generalizability of medical AI are the recognized challenges that hinder a broad AI deployment in clinical practice. Pulmonary nodes detection and characterization based on chest CT images is one of the demanded use cases for automatization by means of AI, and multiple AI solutions addressing this task are becoming available. Here, we evaluated and compared the performance of several commercially available radiological AI with the same clinical task on the same external datasets acquired before and during the pandemic of COVID-19. APPROACH: 5 commercially available AI models for pulmonary nodule detection were tested on two external datasets labelled by experts according to the intended clinical task. Dataset1 was acquired before the pandemic and did not contain radiological signs of COVID-19; dataset2 was collected during the pandemic and did contain radiological signs of COVID-19. ROC-analysis was applied separately for the dataset1 and dataset2 to select probability thresholds for each dataset separately. AUROC, sensitivity and specificity metrics were used to assess and compare the results of AI performance. RESULTS: Statistically significant differences in AUROC values were observed between the AI models for the dataset1. Whereas for the dataset2 the differences of AUROC values became statistically insignificant. Sensitivity and specificity differed statistically significantly between the AI models for the dataset1. This difference was insignificant for the dataset2 when we applied the probability threshold initially selected for the dataset1. An update of the probability threshold based on the dataset2 created statistically significant differences of sensitivity and specificity between AI models for the dataset2. For 3 out of 5 AI models, the update of the probability threshold was valuable to compensate for the degradation of AI model performances with the population shift caused by the pandemic. CONCLUSIONS: Population shift in the data is able to deteriorate differences of AI models performance. Update of the probability threshold together with the population shift seems to be valuable to preserve AI models performance without retraining them.

Follow-up evaluation of pulmonary function and computed tomography findings in chronic kidney disease patients after COVID-19 infection.

Pulmonary complications are common after SARS-CoV2- infection. However, data on pulmonary sequelae of COVID-19 after recovery in dialysis patients are limited. We determined the prevalence of abnormal lung function tests and CT findings and investigate the association factors impacting pulmonary dysfunction. This prospective observational cohort study enrolled 100 patients with stage 5 chronic kidney disease (CKD) undergoing dialysis who had recovered from COVID-19 for ≥3 months. Pulmonary function test (PFT) and chest computed tomography (CT) were performed. Demographic data and laboratory results were recorded. The mean patient age was 55.15 ± 12.84 years. Twenty-one patients (21%) had severe COVID-19, requiring mechanical ventilation or oxygen supplementation. Pulmonary function tests revealed a restrictive pattern in 41% (95% confidence interval [CI], 31.73-50.78;) and an obstructive pattern in 7.29% (95% CI, 3.19-13.25) patients. The severe group showed PFT test results similar to the non-severe group, with three patients showing severe obstructive lung disease. The CT scan findings included reticulation (64%), multifocal parenchymal band (43%), ground glass opacities (32%), and bronchiectasis (28%). The median total CT score was 3 (interquartile range, 1-8.5). The CT score and PFT findings showed no association with pulmonary dysfunction extent, except in bronchiectasis. Lung function indices were associated with abnormal CT findings. Abnormal CT findings (bronchiectasis, reticulation, and ground-glass opacities) was associated with higher oxygen requirements than normal CT findings (p = 0.008, bronchiectasis; p = 0.041, reticulation; p = 0.032, ground-glass appearance). Aside from CT findings and CRP levels, no significant lung abnormalities were observed in severe and non-severe patients. Some patients had residual symptoms at follow-up. The findings indicate persistence of both radiological and physiological abnormalities in dialysis patients after COVID-19. However, the prevalence of these abnormalities was comparable to that in the normal population; few patients experienced ongoing symptoms. Follow-up observations and evaluations are warranted. Trial registration. Clinicaltrials.gov Identifier: NCT05348759.