Handheld Ultrasound Devices Used by Newly Certified Operators for Pneumonia in the Emergency Department-A Diagnostic Accuracy Study.

The diagnostic accuracy of handheld ultrasound (HHUS) devices operated by newly certified operators for pneumonia is unknown. This multicenter diagnostic accuracy study included patients prospectively suspected of pneumonia from February 2021 to February 2022 in four emergency departments. The index test was a 14-zone focused lung ultrasound (FLUS) examination, with consolidation with air bronchograms as diagnostic criteria for pneumonia. FLUS examinations were performed by newly certified operators using HHUS. The reference standard was computed tomography (CT) and expert diagnosis using all medical records. The sensitivity and specificity of FLUS and chest X-ray (CXR) were compared using McNemar's test. Of the 324 scanned patients, 212 (65%) had pneumonia, according to the expert diagnosis. FLUS had a sensitivity of 31% (95% CI 26-36) and a specificity of 82% (95% CI 78-86) compared with the experts' diagnosis. Compared with CT, FLUS had a sensitivity of 32% (95% CI 27-37) and specificity of 81% (95% CI 77-85). CXR had a sensitivity of 66% (95% CI 61-72) and a specificity of 76% (95% CI 71-81) compared with the experts' diagnosis. Compared with CT, CXR had a sensitivity of 69% (95% CI 63-74) and a specificity of 68% (95% CI 62-72). Compared with the experts' diagnosis and CT diagnosis, FLUS performed by newly certified operators using HHUS devices had a significantly lower sensitivity for pneumonia when compared to CXR (p < 0.001). FLUS had a significantly higher specificity than CXR using CT diagnosis as a reference standard (p = 0.02). HHUS exhibited low sensitivity for pneumonia when used by newly certified operators.

DCE-CT parameters as new functional imaging biomarkers at baseline and during immune checkpoint inhibitor therapy in patients with lung cancer - a feasibility study.

BACKGROUND: With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors. METHODS: Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BVdecon), blood flow deconvolution (BFdecon), blood flow maximum slope (BFMax slope) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses. RESULTS: High baseline Blood Volume (BVdecon) (> 12.97 ml × 100 g-1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BFdecon (> 24.50% for OS and > 12.04% for PFS) was associated with an unfavorable OS (8.7 months vs 23.1 months; p < 0.025) and PFS (2.5 vs 13.7 months; p < 0.018). The relative change in BFdecon (categorical) on day seven was a predictor of OS (HR 0.26, CI95: 0.06 to 0.93 p = 0.039) and PFS (HR 0.27, CI95: 0.09 to 0.85 p = 0.026). CONCLUSION: DCE-CT-identified parameters may serve as potential prognostic biomarkers at baseline and during early treatment in patients with NSCLC treated with immune checkpoint inhibitor therapy.

Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study.

BACKGROUND: Without increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians' assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist's assessments as reference standard. METHODS: This was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations. RESULTS: All clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians' CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77-88), specificity = 70% (95%CI: 59-81), positive predictive value = 80% (95%CI: 74-84), negative predictive value = 78% (95%CI: 73-82). CONCLUSION: This study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation.

BACKGROUND: The pancreas is a complex abdominal organ with many anatomical variations, and therefore automated pancreas segmentation from medical images is a challenging application. PURPOSE: In this paper, we present a framework for segmenting individual pancreatic subregions and the pancreatic duct from three-dimensional (3D) computed tomography (CT) images. METHODS: A multiagent reinforcement learning (RL) network was used to detect landmarks of the head, neck, body, and tail of the pancreas, and landmarks along the pancreatic duct in a selected target CT image. Using the landmark detection results, an atlas of pancreases was nonrigidly registered to the target image, resulting in anatomical probability maps for the pancreatic subregions and duct. The probability maps were augmented with multilabel 3D U-Net architectures to obtain the final segmentation results. RESULTS: To evaluate the performance of our proposed framework, we computed the Dice similarity coefficient (DSC) between the predicted and ground truth manual segmentations on a database of 82 CT images with manually segmented pancreatic subregions and 37 CT images with manually segmented pancreatic ducts. For the four pancreatic subregions, the mean DSC improved from 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shifted windowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, when utilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperforming the standard approaches and existing methods on different datasets. CONCLUSIONS: The resulting accuracy of the proposed RL-based segmentation framework demonstrates an improvement against segmentation with standard U-Net architectures.

Occupational exposure to respirable crystalline silica and incident idiopathic interstitial pneumonias and pulmonary sarcoidosis: a national prospective follow-up study.

BACKGROUND: Respirable crystalline silica is a well-known cause of silicosis but may also be associated with other types of interstitial lung disease. We examined the associations between occupational exposure to respirable crystalline silica and the risk of idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. METHODS: The total Danish working population was followed 1977-2015. Annual individual exposure to respirable crystalline silica was estimated using a quantitative job exposure matrix. Cases were identified in the Danish National Patient Register. We conducted adjusted analyses of exposure-response relations between cumulative silica exposure and other exposure metrics and idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. RESULTS: Mean cumulative exposure was 125 µg/m3-years among exposed workers. We observed increasing incidence rate ratios with increasing cumulative silica exposure for idiopathic interstitial pneumonias, pulmonary sarcoidosis and silicosis. For idiopathic interstitial pneumonias and pulmonary sarcoidosis, trends per 50 µg/m3-years were 1.03 (95% CI 1.02 to 1.03) and 1.06 (95% CI 1.04 to 1.07), respectively. For silicosis, we observed the well-known exposure-response relation with a trend per 50 µg/m3-years of 1.20 (95% CI 1.17 to 1.23). CONCLUSION: This study suggests that silica inhalation may be related to pulmonary sarcoidosis and idiopathic interstitial pneumonias, though these findings may to some extent be explained by diagnostic misclassification. The observed exposure-response relations for silicosis at lower cumulative exposure levels than previously reported need to be corroborated in analyses that address the limitations of this study.

Risk of hypersensitivity pneumonitis and other interstitial lung diseases following organic dust exposure.

BACKGROUND: Organic dust is associated with hypersensitivity pneumonitis, and associations with other types of interstitial lung disease (ILD) have been suggested. We examined the association between occupational organic dust exposure and hypersensitivity pneumonitis and other ILDs in a cohort study. METHODS: The study population included all residents of Denmark born in 1956 or later with at least 1 year of gainful employment since 1976. Incident cases of hypersensitivity pneumonitis and other ILDs were identified in the Danish National Patient Register 1994-2015. Job exposure matrices were used to assign individual annual levels of exposure to organic dust, endotoxin and wood dust from 1976 to 2015. We analysed exposure-response relations by different exposure metrics using a discrete-time hazard model. RESULTS: For organic dust, we observed increasing risk with increasing cumulative exposure with incidence rate ratios (IRR) per 10 unit-years of 1.19 (95% CI 1.12 to 1.27) for hypersensitivity pneumonitis and 1.04 (95% CI 1.02 to 1.06) for other ILDs. We found increasing risk with increasing cumulative endotoxin exposure for hypersensitivity pneumonitis and other ILDs with IRRs per 5000 endotoxin units/m3-years of 1.55 (95% CI 1.38 to 1.73) and 1.09 (95% CI 1.00 to 1.19), respectively. For both exposures, risk also increased with increasing duration of exposure and recent exposure. No increased risks were observed for wood dust exposure. CONCLUSION: Exposure-response relations were observed between organic dust and endotoxin exposure and hypersensitivity pneumonitis and other ILDs, with lower risk estimates for the latter. The findings indicate that organic dust should be considered a possible cause of any ILD. TRIAL REGISTRATION NUMBER: j.no.: 1-16-02-196-17.

Identification and management of interstitial lung abnormalities.

Interstitial lung abnormalities (ILA) are incidentally observed specific CT findings in patients without clinical suspicion of interstitial lung disease (ILD). ILA with basal and peripheral predominance and features suggestive of fibrosis in more than 5% of any part of the lung should be referred for pulmonologist review. The strategy for monitoring as described in this review is based on clinical and radiological risk factors. ILA are associated with risk of progression to ILD and increased mortality. Early identification and assessment of risk factors for progression are essential to improve outcome.

Consequences of Losing Incidental Pulmonary Nodules to Follow-Up: Unmonitored Nodules Progressing to Stage IV Lung Cancer.

INTRODUCTION: Lung cancer is the leading cause of cancer-related death globally. Incidental pulmonary nodules represent a golden opportunity for early diagnosis, which is critical for improving survival rates. This study explores the impact of missed pulmonary nodules on the progression of lung cancer. METHODS: A total of 4,066 stage IV lung cancer cases from 2019 to 2021 in Danish hospitals were investigated to determine whether a chest computed tomography (CT) had been performed within 2 years before diagnosis. CT reports and images were reviewed to identify nodules that had been missed by radiologists or were not appropriately monitored, despite being mentioned by the radiologist, and to assess whether these nodules had progressed to stage IV lung cancer. RESULTS: Among stage IV lung cancer patients, 13.6% had undergone a chest CT scan before their diagnosis; of these, 44.4% had nodules mentioned. Radiologists missed a nodule in 7.6% of cases. In total, 45.3% of nodules were not appropriately monitored. An estimated 2.5% of stage IV cases could have been detected earlier with proper surveillance. CONCLUSION: This study underlines the significance of monitoring pulmonary nodules and proposes strategies for enhancing detection and surveillance. These strategies include centralized monitoring and the implementation of automated registries to prevent gaps in follow-up.

The usual Interstitial pneumonia pattern in autoimmune rheumatic diseases.

Usual Interstitial Pneumonia (UIP) is characterized by progression of lung parenchyma that may be observed in various autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis and connective tissue diseases. From a diagnostic point of view, a UIP pattern related to ARDs may display imaging and pathological features able to distinguish it from that related to IPF, such as the "straight-edge" sign at HRCT and lymphoplasmacytic infiltrates at histologic specimens. Multidisciplinary approach (MDD), involving at least pulmonologist, rheumatologist and radiologist, is fundamental in the differential diagnosis process, but MDD is also required in the evaluation of severity, progression and response to treatment, that is based on the combination of changes in symptoms, pulmonary function trends, and, in selected patients, serial CT evaluation. Differently from IPF, in patients with ARDs both functional evaluation and patient-reported outcomes may be affected by systemic involvement and comorbidities, including musculoskeletal manifestations of disease. Finally, in regards to pharmacological treatment, immunosuppressants have been considered the cornerstone of therapy, despite the lack of solid evidence in most cases; recently, antifibrotic drugs were also proposed for the treatment of progressive fibrosing ILDs other than IPF. In ARD-ILD, the therapeutic choice should balance the need for the control of systemic and lung involvements with the risk of adverse events from multi-morbidities and -therapies. Purpose of this review is to summarize the definition, the radiological and morphological features of the UIP pattern in ARDs, together with risk factors, diagnostic criteria, prognostic evaluation, monitoring and management approaches of the UIP-ARDs.

Potential impact of texture analysis in contrast enhanced CT in non-small cell lung cancer as a marker of survival: A retrospective feasibility study.

The objective of this feasibility study was to assess computed tomography (CT) texture analysis (CTTA) of pulmonary lesions as a predictor of overall survival in patients with suspected lung cancer on contrast-enhanced computed tomography (CECT). In a retrospective pilot study, 94 patients (52 men and 42 women; mean age, 67.2 ±â€…10.8 yrs) from 1 center with non-small cell lung cancer (NSCLC) underwent CTTA on the primary lesion by 2 individual readers. Both simple and multivariate Cox regression analyses correlating textural parameters with overall survival were performed. Statistically significant parameters were selected, and optimal cutoff values were determined. Kaplan-Meier plots based on these results were produced. Simple Cox regression analysis showed that normalized uniformity had a hazard ratio (HR) of 16.059 (3.861-66.788, P < .001), and skewness had an HR of 1.914 (1.330-2.754, P < .001). The optimal cutoff values for both parameters were 0.8602 and 0.1554, respectively. Normalized uniformity, clinical stage, and skewness were found to be prognostic factors for overall survival in multivariate analysis. Tumor heterogeneity, assessed by normalized uniformity and skewness on CECT may be a prognostic factor for overall survival.

Assessment of Correlation between Dual-Energy Ct (De-Ct)-Derived Iodine Concentration and Local Flourodeoxyglucose (Fdg) Uptake in Patients with Primary Non-Small-Cell Lung Cancer.

(1) The current literature contains several studies investigating the correlation between dual-energy-derived iodine concentration (IC) and positron emission tomography (PET)-derived Flourodeoxyglucose (18F-FDG) uptake in patients with non-small-cell lung cancer (NSCLC). In previously published studies, either the entire tumor volume or a region of interest containing the maximum IC or 18F-FDG was assessed. However, the results have been inconsistent. The objective of this study was to correlate IC with FDG both within the entire volume and regional sub-volumes of primary tumors in patients with NSCLC. (2) In this retrospective study, a total of 22 patients with NSCLC who underwent both dual-energy CT (DE-CT) and 18F-FDG PET/CT were included. A region of interest (ROI) encircling the entire primary tumor was delineated, and a rigid registration of the DE-CT, iodine maps and FDG images was performed for the ROI. The correlation between tumor measurements and area-specific measurements of ICpeak and the peak standardized uptake value (SUVpeak) was found. Finally, a correlation between tumor volume and the distance between SUVpeak and ICpeak centroids was found. (3) For the entire tumor, moderate-to-strong correlations were found between SUVmax and ICmax (R = 0.62, p = 0.002), and metabolic tumor volume vs. total iodine content (R = 0.91, p < 0.001), respectively. For local tumor sub-volumes, a negative correlation was found between ICpeak and SUVpeak (R = −0.58, p = 0.0046). Furthermore, a strong correlation was found between the tumor volume and the distance in millimeters between SUVpeak and ICpeak centroids (R = 0.81, p < 0.0001). (4) In patients with NSCLC, high FDG uptakes and high DE-CT-derived iodine concentrations correlated on a whole-tumor level, but the peak areas were positioned at different locations within the tumor. 18F-FDG PET/CT and DE-CT provide complementary information and might represent different underlying patho-physiologies.

Early reduction in spectral dual-layer detector CT parameters as favorable imaging biomarkers in patients with metastatic renal cell carcinoma.

OBJECTIVES: To associate the early change in DL-CT parameters and HU with survival outcomes and treatment response in patients with metastatic renal cell carcinoma (mRCC). METHODS: DL-CT scans were performed at baseline and after 1 month of checkpoint immunotherapy or tyrosine kinase inhibitor therapy. Scans were reconstructed to conventional CT and DL-CT series, and used for assessment of HU, iodine concentration (IC), and the effective atomic number (Zeffective) in the combined RECISTv.1.1 target lesions. The relative changes, defined as ΔIC(combined), ΔZeffective(combined), and ΔHU(combined), were associated with progression-free survival (PFS), overall survival (OS), and objective response rate (ORR). The reduction in the sum of diameters of target lesions ≥ 30% after 1 month was associated with OS, PFS, and ORR. RESULTS: Overall, 115 and 104 mRCC patients were included at baseline and 1 month, respectively. Median IC(combined) decreased from 2.3 to 1.2 mg/ml (p < 0.001), Zeffective(combined) from 8.5 to 8.0 (p < 0.001), and HU(combined) from 86.0 to 64.00 HU (p < 0.001). After multivariate adjustments, the largest reductions in ΔIC(combined) (HR 0.47, 95% CI: 0.24-0.94, p = 0.033) and ΔZeffective(combined) (HR = 0.43, 95% CI: 0.21-0.87, p = 0.019) were associated with favorable OS; the largest reduction in ΔZeffective(combined) was associated with higher response (OR = 2.79, 95% CI: 1.12-6.94, p = 0.027). The largest reduction in ΔHU(combined) was solely associated with OS in univariate analysis (HR 0.45, 95% CI: 0.23-0.91). Reduction in SOD ≥ 30% at 1 month was not associated with outcomes (p > 0.075). CONCLUSIONS: Early reductions at 1 month in ΔIC(combined) and ΔZeffective(combined) are associated with favorable outcomes in patients with mRCC. This information may reassure physicians and patients about treatment strategy. KEY POINTS: • Early reductions following 1 month of therapy in spectral dual-layer detector CT-derived iodine concentration and the effective atomic number (Zeffective) are independent biomarkers for better overall survival in patients with metastatic renal cell carcinoma. • Early reduction after 1 month of therapy in the effective atomic number (Zeffective) is an independent imaging biomarker for better treatment response metastatic renal cell carcinoma.

Prognostic Utility of Parameters Derived From Pretreatment Dual-Layer Spectral-Detector CT in Patients With Metastatic Renal Cell Carcinoma.

BACKGROUND. New therapies have emerged for metastatic renal cell carcinoma (mRCC), though corresponding imaging markers are lacking. Dual-layer spectral-detector CT (DLCT) can quantify iodine concentration (IC) and effective atomic number (Zeffective), providing information beyond attenuation that may indicate mRCC prognosis. OBJECTIVE. The purpose of our study was to assess the utility of the DLCT-derived parameters IC and Zeffective for predicting mRCC treatment response and survival. METHODS. This prospective study enrolled 120 participants with mRCC from January 2018 to January 2020 who underwent DLCT, with reconstruction of IC and Zeffective maps, before treatment initiation. Final analysis included 115 participants (86 men, 29 women; median age, 65.1 years), incorporating 313 target lesions that were clinically selected using RECIST version 1.1 on arterial phase acquisitions of the chest and abdomen. Semiautomatic volumetric segmentation was performed of the target lesions. Voxels from all lesions were combined to a single histogram per patient. The median IC and Zeffective of the combined histograms were recorded. Measurements above and below the cohort median values were considered high and low, respectively. Univariable associations were explored between IC and Zeffective with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Multivariable associations were explored between IC and ORR, PFS, and OS, adjusting for treatment (tyrosine kinase inhibitor vs checkpoint immunotherapy) and significant univariable predictors (including tumor histology and International Metastatic Renal Cell Carcinoma Database Consortium [IMDC] risk factors). RESULTS. At baseline, median IC was 2.26 mg/mL, and median Zeffective was 8.49. In univariable analysis, high IC and high Zeffective were associated with better ORR (both, odds ratio [OR] = 4.35; p = .001), better PFS (both, hazard ratio [HR] = 0.51; p = .004), and better OS (both, HR = 0.38; p < .001). In multivariable models, high IC independently predicted better ORR (OR = 4.35, p = .001), better PFS (HR = 0.51, p = .004), and better OS (HR = 0.37, p < .001); neutrophilia independently predicted worse PFS (HR = 2.10, p = .004) and worse OS (HR = 2.28, p = .003). The estimated C-index for predicting OS using IMDC risk factors alone was 0.650 versus 0.687 when incorporating high attenuation and 0.692 when incorporating high IC or high Zeffective. CONCLUSION. High IC and high Zeffective are significant predictors of better treatment response and survival in mRCC. CLINICAL IMPACT. Baseline DLCT parameters may improve current mRCC prognostic models. TRIAL REGISTRATION. ClinicalTrials.gov NCT03616951.

Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study.

RATIONAL AND OBJECTIVES: This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams. MATERIALS AND METHODS: An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer. After each case radiologists answered questions regarding additional findings and perceived case overview. Reading times were recorded for 25 cases without and 20 cases with AI tool assistance for each reader. Differences in reading time with and without the AI tool were assessed using Welch's t-test for non-inferiority with non-inferiority limits defined as 100 seconds for the consultant and 200 seconds for the resident. RESULTS: The mean reading time for the radiology resident was not significantly affected by the AI tool (without AI 370s vs with AI 437s; +67s 95% CI -28s to +163s, p = 0.16). The reading time for the radiology consultant was also not significantly affected by the AI tool (without AI 366s vs with AI 380s; +13s (95% CI - -57s to 84s, p = 0.70). The AI tool led to additional actionable findings in 5/40 (12.5%) studies and better overview in 18/20 (90%) of studies for the resident. CONCLUSION: A PACS based implementation of an AI tool for concurrent reading of chest CT exams did not increase reading time with additional actionable findings made as well as a perceived better case overview for the radiology resident.

Economic impact of spectral body imaging in diagnosis of patients suspected for occult cancer.

BACKGROUND: Based on prior studies spectral CT has shown a higher sensitivity for malignant lesions than conventional CT at the cost of lower specificity. For the radiologists, it also offers a higher degree of certainty in the diagnosis of benign lesions. The objective of this study was to evaluate the economic impact of spectral CT in patients suspected of occult cancer in a medical center in Denmark. METHODS: This study was a secondary analysis using de-identified data from a prospective study of patients receiving a contrast-enhanced spectral CT scan. Based on suggested follow-up examinations on both spectral CT and contrast-enhanced CT, costs from a payer's perspective were determined using unit costs obtained from national databases. RESULTS: The dataset contained 400 patients. Overall, 203 follow-up procedures were eliminated based on spectral data reading. The largest reduction in suggested follow-up procedures was found for the kidney (83%), followed by the liver (66%), adrenal glands (60%), and pancreas (42%). The total estimated costs for suggested follow-up procedures based on spectral data reading were €155,219, 25.2% (€52,384) less than that of conventional CT reading. CONCLUSION: Our results provide support for spectral body imaging as an advanced imaging modality for suspected occult cancer. A substantial number of follow-up diagnostic procedures could be eliminated based on spectral data reading, which would result in significant cost savings.

Descriptive analysis of long COVID sequelae identified in a multidisciplinary clinic serving hospitalised and non-hospitalised patients.

BACKGROUND: There are emerging data of long-term effects of coronavirus disease 2019 (COVID-19) comprising a diversity of symptoms. The aim of this study was to systematically describe and measure pulmonary and extra-pulmonary post-COVID-19 complications in relation to acute COVID-19 severity. METHODS: Patients attending a standard of care 3 months post-hospitalisation follow-up visit and those referred by their general practitioner because of persistent post-COVID-19 symptoms were included. Patients underwent symptomatic, quality of life, pulmonary (lung function and high-resolution computed tomography (HRCT)), cardiac (high-resolution ECG), physical (1-min sit and stand test (1-MSTST), handgrip strength, cardiopulmonary exercise testing (CPET)) and cognitive evaluations. RESULTS: All 34 hospitalised and 22 out of 23 non-hospitalised patients had ≥1 complaint or abnormal finding at follow-up. Overall, 67% of patients were symptomatic (Medical Research Council (MRC) ≥2 or COPD assessment test (CAT) ≥10), with no difference between hospitalised versus non-hospitalised patients. Pulmonary function (forced expiratory volume in 1 s (FEV1) or diffusing capacity of the lung for carbon monoxide (D LCO)) <80% of predicted) was impaired in 68% of patients. D LCO was significantly lower in those hospitalised compared to non-hospitalised (70.1±18.0 versus 80.2±11.2% predicted, p=0.02). Overall, 53% had an abnormal HRCT (predominantly ground-glass opacities) with higher composite computed tomography (CT) scores in hospitalised versus non-hospitalised patients (2.3 (0.1-4.8) and 0.0 (0.0-0.3), p<0.001). 1-MSTST was below the 25th percentile in almost half of patients, but no signs of cardiac dysfunction were found. Cognitive impairments were present in 59-66% of hospitalised and 31-44% of non-hospitalised patients (p=0.08). CONCLUSION: Three months after COVID-19 infection, patients were still symptomatic and demonstrated objective respiratory, functional, radiological and cognitive abnormalities, which were more prominent in hospitalised patients. Our study underlines the importance of multidimensional management strategies in these patients.

Metastases or benign adrenal lesions in patients with histopathological verification of lung cancer: Can CT texture analysis distinguish?

INTRODUCTION: Distant metastases are found in the many of patients with lung cancer at time of diagnosis. Several diagnostic tools are available to distinguish between metastatic spread and benign lesions in the adrenal gland. However, all require additional diagnostic steps after the initial CT. The purpose of this study was to evaluate if texture analysis of CT-abnormal adrenal glands on the initial CT correctly differentiates between malignant and benign lesions in patients with confirmed lung cancer. MATERIALS AND METHODS: In this retrospective study 160 patients with endoscopic ultrasound-guided biopsy from the left adrenal gland and a contrast-enhanced CT in portal venous phase were assessed with texture analysis. A region of interest encircling the entire adrenal gland was used and from this dataset the slice with the largest cross section of the lesion was analyzed individually. RESULTS: Several texture parameters showed statistically significantly difference between metastatic and benign lesions but with considerable between-groups overlaps in confidence intervals. Sensitivity and specificity were assessed using ROC-curves, and in univariate binary logistic regression the area under the curve ranged from 36 % (Kurtosis 0.5) to 69 % (Entropy 2.5) compared to 73 % in the best fitting model using multivariate binary logistic regression. CONCLUSION: In lung cancer patients with abnormal adrenal gland at imaging, adrenal gland texture analyses appear not to have any role in discriminating benign from malignant lesions.

Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19.

The clinical management of COVID-19 is challenging. Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of this disease. Chest x-ray radiography and computed tomography) are the standard imaging modalities used for the structural assessment of the disease status, while functional imaging (namely, positron emission tomography) has had limited application. Artificial intelligence can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. We review the current landscape of these imaging modalities and artificial intelligence approaches as applied in COVID-19 management.

Classification of renal tumour using convolutional neural networks to detect oncocytoma.

PURPOSE: To investigate the ability of convolutional neural networks (CNNs) to facilitate differentiation of oncocytoma from renal cell carcinoma (RCC) using non-invasive imaging technology. METHODS: Data were collected from 369 patients between January 2015 and September 2018. True labelling of scans as benign or malignant was determined by subsequent histological findings post-surgery or ultrasound-guided percutaneous biopsy. The data included 20,000 2D CT images. Data were randomly divided into sets for training (70 %), validation (10 %) and independent testing (20 %, DataTest_1). A small dataset (DataTest_2) was used for additional validation of the training model. Data were divided into sets at the patient level, rather than by individual image. A modified version of the ResNet50V2 was used. Accuracy of detecting benign or malignant renal mass was evaluated by a 51 % majority vote of individual image classifications to determine the classification for each patient. RESULTS: Test results from DataTest_1 indicate an area under the curve (AUC) of 0.973 with 93.3 % accuracy and 93.5 % specificity. Results from DataTest_2 indicate an AUC of 0.946 with 90.0 % accuracy and 98.0 % specificity when evaluation is performed image by image. There is no case in which multiple false negative images originate from the same patient. When evaluated with 51 % majority of scans for each patient, the accuracy rises to 100 % and the incidence of false negatives falls to zero. CONCLUSION: CNNs and deep learning technology can classify renal tumour masses as oncocytoma with high accuracy. This diagnostic method could prevent overtreatment for patients with renal masses.