Preoperative Chest Computed Tomography Screening for Coronavirus Disease 2019 in Asymptomatic Patients Undergoing Cardiac Surgery

Due to the outbreak of Severe Acute Respiratory Syndrome coronavirus (SARS-Cov-2), an efficient COVID-19 screening strategy is required for patients undergoing cardiac surgery. The objective of this prospective observational study was to evaluate the role of preoperative computed tomography (CT) screening for COVID-19 in a population of COVID-19 asymptomatic patients scheduled for cardiac surgery. Between the 29th of March and the 26th of May 2020, patients asymptomatic for COVID-19 underwent a CT-scan the day before surgery, with reverse-transcriptase polymerase-chain reaction (RT-PCR) reserved for abnormal scan results. The primary endpoint was the prevalence of abnormal scans, which was evaluated using the CO-RADS score, a COVID-19 specific grading system. In a secondary analysis, the rate of abnormal scans was compared between the screening cohort and matched historical controls who underwent routine preoperative CT-screening prior to the SARS-Cov-2 outbreak. Of the 109 patients that underwent CT-screening, an abnormal scan result was observed in 7.3% (95% confidence interval: 3.2–14.0%). One patient, with a normal screening CT, was tested positive for COVID-19, with the first positive RT-PCR on the ninth day after surgery. A rate of preoperative CT-scan abnormalities of 8% (n = 8) was found in the unexposed historical controls (P &gt; 0.999). In asymptomatic patients undergoing cardiac surgery, preoperative screening for COVID-19 using computed tomography will identify pulmonary abnormalities in a small percentage of patients that do not seem to have COVID-19. Depending on the prevalence of COVID-19, this results in an unfavorable positive predictive value of CT screening. Care should be taken when considering CT as a screening tool prior to cardiac surgery.</p

Preoperative chest computed tomography screening for coronavirus disease 2019 in asymptomatic patients undergoing cardiac surgery

Due to the outbreak of Severe Acute Respiratory Syndrome coronavirus (SARS-Cov-2), an efficient COVID-19 screening strategy is required for patients undergoing cardiac surgery. The objective of this prospective observational study was to evaluate the role of preoperative computed tomography (CT) screening for COVID-19 in a population of COVID-19 asymptomatic patients scheduled for cardiac surgery. Between the 29th of March and the 26th of May 2020, patients asymptomatic for COVID-19 underwent a CT-scan the day before surgery, with reverse-transcriptase polymerase-chain reaction (RT-PCR) reserved for abnormal scan results. The primary endpoint was the prevalence of abnormal scans, which was evaluated using the CO-RADS score, a COVID-19 specific grading system. In a secondary analysis, the rate of abnormal scans was compared between the screening cohort and matched historical controls who underwent routine preoperative CT-screening prior to the SARS-Cov-2 outbreak. Of the 109 patients that underwent CT-screening, an abnormal scan result was observed in 7.3% (95% confidence interval: 3.2–14.0%). One patient, with a normal screening CT, was tested positive for COVID-19, with the first positive RT-PCR on the ninth day after surgery. A rate of preoperative CT-scan abnormalities of 8% (n = 8) was found in the unexposed historical controls (P > 0.999). In asymptomatic patients undergoing cardiac surgery, preoperative screening for COVID-19 using computed tomography will identify pulmonary abnormalities in a small percentage of patients that do not seem to have COVID-19. Depending on the prevalence of COVID-19, this results in an unfavorable positive predictive value of CT screening. Care should be taken when considering CT as a screening tool prior to cardiac surgery

Functional associations of pleuroparenchymal fibroelastosis and emphysema with hypersensitivity pneumonitis

BACKGROUND: Pleuroparenchymal fibroelastosis (PPFE) has been described in hypersensitivity pneumonitis (HP) yet its functional implications are unclear. Combined pulmonary fibrosis and emphysema (CPFE) has occasionally been described in never-smokers with HP, but epidemiological data regarding its prevalence is sparse. CTs in a large HP cohort were therefore examined to identify the prevalence and effects of PPFE and emphysema. Methods: 233 HP patients had CT extents of interstitial lung disease (ILD) and emphysema quantified to the nearest 5%. Lobar percentage pleural involvement of PPFE was quantified on a 4-point categorical scale: 0 = absent, 1 = affecting 33%. Marked PPFE reflected a total lung score of ≥3/18. Results were evaluated against FVC, DLco and mortality. RESULTS: Marked PPFE prevalence was 23% whilst 23% of never-smokers had emphysema. Following adjustment for patient age, gender, smoking status, and ILD and emphysema extents, marked PPFE independently linked to reduced baseline FVC (p = 0.0002) and DLco (p = 0.002) and when examined alongside the same covariates, independently linked to worsened survival (p = 0.01). CPFE in HP demonstrated a characteristic functional profile of artificial lung volume preservation and disproportionate DLco reduction. CPFE did not demonstrate a worsened outcome when compared to HP patients without emphysema beyond that explained by CT extents of ILD and emphysema. CONCLUSIONS: PPFE is not uncommon in HP, and is independently associated with impaired lung function and increased mortality. Emphysema was identified in 23% of HP never-smokers. CPFE appears not to link to a malignant microvascular phenotype as outcome is explained by ILD and emphysema extents

A prospective cohort study on the pharmacokinetics of nivolumab in metastatic non-small cell lung cancer, melanoma, and renal cell cancer patients

Background: Nivolumab is administered in a weight-based or fixed-flat dosing regimen. For patients with non-small cell lung cancer (NSCLC), a potential exposure-response relationship has recently been reported and may argue against the current dosing strategies. The primary objectives were to determine nivolumab pharmacokinetics (PK) and to assess the relationship between drug clearance and clinical outcome in NSCLC, melanoma, and renal cell cancer (RCC). Methods: In this prospective observational cohort study, individual estimates of nivolumab clearance and the impact of baseline covariates were determined using a population-PK model. Clearance was related to best overall response (RECISTv1.1), and stratified by tumor type. Results: Two-hundred-twenty-one patients with metastatic cancer receiving nivolumab-monotherapy were included of whom 1,715 plasma samples were analyzed. Three baseline parameters had a significant effect on drug clearance and were internally validated in the population-PK model: gender, BSA, and serum albumin. Women had 22% lower clearance compared to men, while the threshold of BSA and albumin that led to > 20% increase of clearance was > 2.2m2 and < 37.5 g/L, respectively. For NSCLC, drug clearance was 42% higher in patients with progressive disease (mean: 0.24; 95% CI: 0.22-0.27 L/day) compared to patients with partial/complete response (mean: 0.17; 95% CI: 0.15-0.19 L/day). A similar trend was observed in RCC, however, no clearance-response relationship was observed in melanoma. Conclusions: Based on the first real-world population-PK model of nivolumab, covariate analysis revealed a significant effect of gender, BSA, and albumin on nivolumab clearance. A clearance-response relationship was observed in NSCLC, with a non-significant trend in RCC, but not in melanoma. Individual pharmacology of nivolumab in NSCLC appears important and should be prospectively studied

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

The braf p.V600e mutation status of melanoma lung metastases cannot be discriminated on computed tomography by lidc criteria nor radiomics using machine learning

Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria

Desquamative interstitial pneumonia: A systematic review of its features and outcomes

Background: Desquamative Interstitial Pneumonia (DIP) is a rare form of idiopathic interstitial pneumonia (IIP). Data on clinical features, aetiology, prognosis and effect of treatment strategies are limited. We aimed to collect all published cases to better characterise DIP. Methods: A systematic literature search was performed for all original cases of adult patients with histopathologically-confirmed DIP. Individual patient data were extracted and summarised. Results: We included 68 individual cases and 13 case series reporting on 294 cases. Most common presenting symptoms were dyspnoea and cough. Pulmonary function showed a restrictive pattern (71%) with decreased diffusion capacity. We found a high incidence (81%) of ever smoking in patients with DIP and 22% of patients had other (occupational) exposures. Characteristic features on high-resolution computed tomography (HRCT) scan were bilateral ground-glass opacities with lower lobe predominance (92%). Treatment and duration of treatment widely varied. Initial response to treatment was generally good, but definitely not uniformly so. A significant proportion of patients died (25% of individual cases) or experienced a relapse (18% of individual cases). Conclusion: DIP remains an uncommon disease, frequently but not always related to smoking or other exposures. Furthermore, DIP behaves as a progressive disease more often than generally thought, possibly associated with different underlying aetiology

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning

Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.ImPhys/Medical ImagingImPhys/Computational Imagin

Observer performance.

<p>Average area under the ROC curves (AUC) for all observers. AUCs are displayed for all images and for analysis of the different groups of projection type (bedside and upright).</p><p>Observer performance.</p