Mediastinal Hemangioma Masquerading as a Simple Cyst.

ABSTRACT: This report presents imaging from a mediastinal mass in a patient with colon cancer. At baseline and surveillance chest computed tomography examinations, it was characterized as a pericardial cyst. However, during chemotherapy, complications arose and this mass was further characterized with a chest MRI. It was then decided to be removed, and histopathology confirmed the diagnosis of a hemangioma.

A Case of Idiopathic First Bite Syndrome, Possibly Linked to Type I Diabetes Mellitus.

We present a rare case of a 34-year-old male with poorly regulated type I diabetes and three-month history of excruciating pain in the right condylar process of the mandible, occurring only during the first bite of each meal. The patient had no history of surgery or trauma in the head and neck region. Clinical and imaging examination revealed no tumor or pathology deriving from the dentures, the temporomandibular joint (TMJ), or the salivary glands. Idiopathic first bite syndrome (FBS) was suspected and treated with pregabalin and glycemic control. This case highlights how a detailed pain history and clinical examination can lead to a rare diagnosis and indicates the potential involvement of diabetic neuropathy in idiopathic FBS, as well as the importance of glycemic regulation in treatment.

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo.

BACKGROUND: Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo). METHODS: Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences. RESULTS: Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules. CONCLUSIONS: Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

TAVR in a Patient With Anomalous Origin and Course of the Left Main Coronary Artery.

In patients with anomalous coronary arteries with high-risk features, corrective cardiac surgery should be considered. We report the first case of transcatheter aortic valve replacement using a self-expanding Evolut valve, in a patient with a single coronary artery arising from the right coronary cusp and an intramural course of the left main. (Level of Difficulty: Intermediate.).

CT patterns and serial CT Changes in lung Cancer patients post stereotactic body radiotherapy (SBRT).

BACKGROUND: To evaluate computed tomography (CT) patterns of post-SBRT lung injury in lung cancer and identify time points of serial CT changes. MATERIALS AND METHODS: One hundred eighty-three tumors in 170 patients were evaluated on sequential CTs within 29 months (median). Frequencies of post-SBRT CT patterns and time points of initiation and duration were assessed. Duration of increase of primary lesion or surrounding injury without evidence of local recurrence and time to stabilization or local recurrence were evaluated. RESULTS: Post-SBRT CT patterns could overlap in the same patient and were nodule-like pattern (69%), consolidation with ground glass opacity (GGO) (41%), modified conventional pattern (39%), peribronchial/patchy consolidation (42%), patchy GGO (24%), diffuse consolidation (16%), "orbit sign" (21%), mass-like pattern (19%), scar-like pattern (15%) and diffuse GGO (3%). Patchy GGO started at 4 months post-SBRT. Peribronchial/patchy consolidation and consolidation with GGO started at 4 and 5 months respectively. Diffuse consolidation, diffuse GGO and orbit sign started at 5, 6 and 8 months respectively. Mass-like, modified conventional and scar-like pattern started at 8, 12 and 12 months respectively. Primary lesion (n = 11) or surrounding injury (n = 85) increased up to 13 months. Primary lesion (n = 119) or surrounding injury (n = 115) started to decrease at 4 and 9 months respectively. Time to stabilization was 20 months. The most common CT pattern at stabilization was modified conventional pattern (49%), scar-like pattern (23%) and mass-like pattern (12%). Local recurrence (n = 15) occurred at a median time of 18 months. CONCLUSION: Different CT patterns of lung injury post-SBRT appear in predictable time points and have variable but predictable duration. Familiarity with these patterns and timeframes of appearance helps differentiate them from local recurrence.

Survival analysis in lung cancer patients with interstitial lung disease.

OBJECTIVE: Lung cancer patients with interstitial lung disease (ILD) are prone for higher morbidity and mortality and their treatment is challenging. The purpose of this study is to investigate whether the survival of lung cancer patients is affected by the presence of ILD documented on CT. MATERIALS AND METHODS: 146 patients with ILD at initial chest CT were retrospectively included in the study. 146 lung cancer controls without ILD were selected. Chest CTs were evaluated for the presence of pulmonary fibrosis which was classified in 4 categories. Presence and type of emphysema, extent of ILD and emphysema, location and histologic type of cancer, clinical staging and treatment were evaluated. Kaplan-Meier estimates and Cox regression models were used to assess survival probability and hazard of death of different groups. P value < 0.05 was considered significant. RESULTS: 5-year survival for the study group was 41% versus 48% for the control group (log-rank test p = 0.0092). No significant difference in survival rate was found between the four different categories of ILD (log-rank test, p = 0.195) and the different histologic types (log-rank test, p = 0.4005). A cox proportional hazard model was used including presence of ILD, clinical stage and age. The hazard of death among patients with ILD was 1.522 times that among patients without ILD (95%CI, p = 0.029). CONCLUSION: Patients with lung cancer and CT evidence of ILD have a significantly shorter survival compared to patients with lung cancer only. Documenting the type and grading the severity of ILD in lung cancer patients will significantly contribute to their challenging management.

Canadian Society of Thoracic Radiology/Canadian Association of Radiologists Clinical Practice Guidance for Non-Vascular Thoracic MRI.

Historically thoracic MRI has been limited by the lower proton density of lung parenchyma, cardiac and respiratory motion artifacts and long acquisition times. Recent technological advancements in MR hardware systems and improvement in MR pulse sequences have helped overcome these limitations and expand clinical opportunities for non-vascular thoracic MRI. Non-vascular thoracic MRI has been established as a problem-solving imaging modality for characterization of thymic, mediastinal, pleural chest wall and superior sulcus tumors and for detection of endometriosis. It is increasingly recognized as a powerful imaging tool for detection and characterization of lung nodules and for assessment of lung cancer staging. The lack of ionizing radiation makes thoracic MRI an invaluable imaging modality for young patients, pregnancy and for frequent serial follow-up imaging. Lack of familiarity and exposure to non-vascular thoracic MRI and lack of consistency in existing MRI protocols have called for clinical practice guidance. The purpose of this guide, which was developed by the Canadian Society of Thoracic Radiology and endorsed by the Canadian Association of Radiologists, is to familiarize radiologists, other interested clinicians and MR technologists with common and less common clinical indications for non-vascular thoracic MRI, discuss the fundamental imaging findings and focus on basic and more advanced MRI sequences tailored to specific clinical questions.

Pathological entities that may affect the lungs and the myocardium. Evaluation with chest CT and cardiac MR.

Certain entities may simultaneously involve the lungs and the myocardium. Knowing their cardiac and thoracic manifestations enhances the understanding of those conditions and increases awareness and suspicion for possible concurrent cardiothoracic involvement. Entities that can present with pulmonary and myocardial involvement include infiltrative diseases like sarcoidosis and amyloidosis, eosinophil-associated conditions including eosinophilic granulomatosis with polyangiitis (EGPA) and hypereosinophilic syndrome (HES), connective tissue diseases such as systemic sclerosis (SSc) and lupus erythematosus and genetic disorders like Fabry disease (FD). Lung involvement in sarcoidosis is almost universal. While cardiac involvement is less common, concurrent cardiothoracic involvement can often be seen. Pulmonary amyloidosis is more often a localized process and generally occurs separately from cardiac involvement, except for diffuse alveolar-septal amyloidosis. EGPA and HES can present with consolidative or ground glass opacities, cardiac inflammation and endomyocardial fibrosis. Manifestations of SSc include interstitial lung disease, pulmonary hypertension and cardiomyopathy. Lupus can present with serositis, pneumonitis and cardiac inflammation. FD causes left ventricular thickening and fibrosis, and small airways disease. This article aims to review the clinicopathological features of chest and cardiac involvement of these entities and describe their main findings on chest CT and cardiac MR.

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models.

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.

[Formula: see text]: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinical outcomes in patients with lung cancer. Hand-crafted features, however, are pre-defined and extracted without taking the desired target into account. Furthermore, accurate segmentation of the tumor is required for development of a reliable predictive model, which may be objective and a time-consuming task. To address these drawbacks, we propose a deep learning-based radiomics model for the time-to-event outcome prediction, referred to as DRTOP that takes raw images as inputs, and calculates the image-based risk of death or recurrence, for each patient. Our experiments on an in-house dataset of 132 lung cancer patients show that the obtained image-based risks are significant predictors of the time-to-event outcomes. Computed Tomography (CT)-based features are predictors of the overall survival (OS), with the hazard ratio (HR) of 1.35, distant control (DC), with HR of 1.06, and local control (LC), with HR of 2.66. The Positron Emission Tomography (PET)-based features are predictors of OS and recurrence free survival (RFS), with hazard ratios of 1.67 and 1.18, respectively. The concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text] for predicting the OS, DC, and RFS show that the deep learning-based radiomics model is as accurate or better in predicting predefined clinical outcomes compared to hand-crafted radiomics, with concordance indices of [Formula: see text], [Formula: see text], and [Formula: see text], for predicting the OS, DC, and RFS, respectively. Deep learning-based radiomics has the potential to offer complimentary predictive information in the personalized management of lung cancer patients.

3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction.

Despite the advances in automatic lung cancer malignancy prediction, achieving high accuracy remains challenging. Existing solutions are mostly based on Convolutional Neural Networks (CNNs), which require a large amount of training data. Most of the developed CNN models are based only on the main nodule region, without considering the surrounding tissues. Obtaining high sensitivity is challenging with lung nodule malignancy prediction. Moreover, the interpretability of the proposed techniques should be a consideration when the end goal is to utilize the model in a clinical setting. Capsule networks (CapsNets) are new and revolutionary machine learning architectures proposed to overcome shortcomings of CNNs. Capitalizing on the success of CapsNet in biomedical domains, we propose a novel model for lung tumor malignancy prediction. The proposed framework, referred to as the 3D Multi-scale Capsule Network (3D-MCN), is uniquely designed to benefit from: (i) 3D inputs, providing information about the nodule in 3D; (ii) Multi-scale input, capturing the nodule's local features, as well as the characteristics of the surrounding tissues, and; (iii) CapsNet-based design, being capable of dealing with a small number of training samples. The proposed 3D-MCN architecture predicted lung nodule malignancy with a high accuracy of 93.12%, sensitivity of 94.94%, area under the curve (AUC) of 0.9641, and specificity of 90% when tested on the LIDC-IDRI dataset. When classifying patients as having a malignant condition (i.e., at least one malignant nodule is detected) or not, the proposed model achieved an accuracy of 83%, and a sensitivity and specificity of 84% and 81% respectively.

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT.

The clinical significance of indeterminate pulmonary nodules in melanoma patients at baseline and during follow-up chest CT.

OBJECTIVE: This study aims to determine an appropriate timeline to monitor indeterminate pulmonary nodules (IPN) in melanoma patients to confirm metastatic origin. MATERIALS AND METHODS: 588 clinically non-metastatic melanoma patients underwent curative intent surgery during 3 years. Patients with baseline chest CT and at least one follow-up (FU) CT were retrospectively analyzed to assess for IPN. Patients with definitely benign nodules, metastases and non-melanoma malignancies were excluded. Change in volume from first to FU CT, initial diameter (D1) and volume (V1), distance from pleura, peripheral and perifissural location, density and clinical stage were evaluated. Nodules were volumetrically measured on CTs and were considered metastases if they increased in size between two CTs or if increase was accompanied by multiple new nodules or extrapulmonary metastases. RESULTS: 148 patients were included. Two out of 243 baseline IPN detected in 70 patients, increased significantly in volume in 3 and 5 months and were proven metastases. During FU, 86% of 40 interval IPN detected in 28 patients, were proven metastases. Interval nodule (p < 0.0001, HR:243,CI:[57.32,1033.74]), 3-month volume change (OR:1.023,CI:[1.014,1.033]), V1 (OR:1.006,CI:[1.003,1.009]), D1 (OR:1.424,CI:[1.23,1.648]), distance from pleura (OR:1.03,CI:[1.003,1.059]), and combined stage IIC + III (OR:11.29,CI:[1.514,84.174]), were associated with increased risk for metastasis. 43%, 72% and 94% of patients with IPN were confirmed with metastases in the first FU CT at 3, 6 and 12 months respectively. CONCLUSION: Baseline IPN are most likely benign, while interval IPN are high risk for metastasis. Absence of volume increase of IPN within 6 months excluded metastasis in most patients.

Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.

We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0.38, p = 0.02], distant control (DC) [PC4-HR:0.51, p = 0.02 and PC1-HR:1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR:1.08, p = 0.04], disease specific survival (DSS) [PC2-HR:1.34, p = 0.03 and PC3-HR:0.64, p = 0.02] and overall survival (OS) [PC4-HR:0.45, p = 0.004 and PC3-HR:0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR:1.1, p = 0.04] and DC [HR:1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR:0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.

Relationship between right and left ventricular function in candidates for implantable cardioverter defibrillator with low left ventricular ejection fraction.

BACKGROUND: Indications for the primary prevention of sudden death using an implantable cardioverter defibrillator (ICD) are based predominantly on left ventricular ejection fraction (LVEF). However, right ventricular ejection fraction (RVEF) is also a known prognostic factor in a variety of structural heart diseases that predispose to sudden cardiac death. We sought to investigate the relationship between right and left ventricular parameters (function and volume) measured by cardiovascular magnetic resonance (CMR) among a broad spectrum of patients considered for an ICD. METHODS: In this retrospective, single tertiary-care center study, consecutive patients considered for ICD implantation who were referred for LVEF assessment by CMR were included. Right and left ventricular function and volumes were measured. RESULTS: In total, 102 patients (age 62±14 years; 23% women) had a mean LVEF of 28±11% and RVEF of 44±12%. The left ventricular and right ventricular end diastolic volume index was 140±42 mL/m2 and 81±27 mL/m2, respectively. Eighty-six (84%) patients had a LVEF <35%, and 63 (62%) patients had right ventricular systolic dysfunction. Although there was a significant and moderate correlation between LVEF and RVEF (r=0.40, p<0.001), 32 of 86 patients (37%) with LVEF <35% had preserved RVEF, while 9 of 16 patients (56%) with LVEF ≥35% had right ventricular systolic dysfunction (Kappa=0.041). CONCLUSIONS: Among patients being considered for an ICD, there is a positive but moderate correlation between LVEF and RVEF. A considerable proportion of patients who qualify for an ICD based on low LVEF have preserved RVEF, and vice versa.

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

Arterial input function placement effect on computed tomography lung perfusion maps.

BACKGROUND: A critical source of variability in dynamic perfusion computed tomography (DPCT) is the arterial input function (AIF). However, the impact of the AIF location in lung DPCT has not been investigated yet. The purpose of this study is to determine whether the location of the AIF within the central pulmonary arteries influences the accuracy of lung DPCT maps. METHODS: A total of 54 lung DPCT scans were performed in three pigs using different rates and volumes of iodinated contrast media. Pulmonary blood flow (PBF) perfusion maps were generated using first-pass kinetics in three different AIF locations: the main pulmonary trunk (PT), the right main (RM) and the left main (LM) pulmonary arteries. A total of 162 time density curves (TDCs) and corresponding PBF perfusion maps were generated. Linear regression and Spearman's rank correlation coefficient were used to compare the TDCs. PBF perfusion maps were compared quantitatively by taking twenty six regions of interest throughout the lung parenchyma. Analysis of variance (ANOVA) was used to compare the mean PBF values among the three AIF locations. Two chest radiologists performed qualitative assessment of the perfusion maps using a 3-point scale to determine regions of perfusion mismatch. RESULTS: The linear regression of the TDCs from the RM and LM compared to the PT had a median (range) of 1.01 (0.98-1.03). The Spearman rank correlation between the TDCs was 0.88 (P<0.05). ANOVA analysis of the perfusion maps demonstrated no statistical difference (P>0.05). Qualitative comparison of the perfusion maps resulted in scores of 1 and 2, demonstrating either identical or comparable maps with no significant difference in perfusion defects between the different AIF locations. CONCLUSIONS: Accurate PBF perfusion maps can be generated with the AIF located either at the PT, RM or LM pulmonary arteries.

Clinical Correlation of Early Atelectasis after Bilateral Internal Thoracic Artery Harvest for Coronary Artery Bypass Grafting.

BACKGROUND: Atelectasis is a significant complication after cardiac surgery. The current study was designed to assess the significance of atelectasis after bilateral internal thoracic artery (BITA) harvest. METHODS: The ICU admission chest x-ray of 565 patients undergoing BITA was reviewed. Linear regression modelling was used to assess the relationship between atelectasis and oxygenation as well as patient variables to length of ventilation and length of stay in the Intensive Care Unit (ICU). RESULTS: Eighty-nine patients (15.8%) had Grade 2/3 atelectasis which was significantly more common on the left as compared to the right (left 0.149 95% CI [0.119-0.178], right 0.027 95% CI [0.013-0.040], p<0.001). Grade 2/3 atelectasis on the right was associated with a significant drop in the pO2 (p=0.001) and the per cent O2-fractional O2 (PF) ratio (p=0.002). Factors associated with increased ventilation time included presence of Grade 2/3 atelectasis (p=0.001) and peripheral vascular disease (PVD) (p<0.001), both of which were predictors of prolonged ICU length of stay (p=0.002 and p<0.001 respectively). CONCLUSIONS: Early atelectasis is related to impaired oxygenation, prolonged ventilation and prolonged ICU stay. Future research should focus on strategies to minimise atelectasis and to determine if these changes translate into better patient outcomes.