How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale

OBJECTIVES

The aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans.

METHODS

Abdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC).

RESULTS

Twenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396-0.814) to 0.785 (95%CI 0.676-0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034-0.500) to 0.562 (95%CI 0.337-0.729). Intraobserver agreement increased for four out of six observers.

CONCLUSION

The PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement.

CLINICAL RELEVANCE STATEMENT

This study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms.

KEY POINTS

• Subjective assessment of diagnostic image quality via Likert scale has limited reliability. • A pairwise comparison method improves the inter- and intraobserver agreement. • The pairwise comparison method is more reliable for CT optimization studies.

Repurposing catheter ablation work-up to detect expiratory airflow limitation in patients with atrial fibrillation

Background

In atrial fibrillation (AF) patients, presence of expiratory airflow limitation may negatively impact treatment outcomes. AF patients are not routinely screened for expiratory airflow limitation, but existing examinations can help identify at-risk individuals. We aimed to assess the diagnostic value of repurposing existing assessments from the pre-ablation work-up to identify and understand the characteristics of affected patients.

Methods

We screened 110 consecutive AF patients scheduled for catheter ablation with handheld spirometry. Routine pre-ablation work-up included cardiac computed tomographic angiography (CCTA), transthoracic echocardiography and polygraphy. CCTA was analyzed qualitatively for emphysema and airway abnormalities. Multivariate logistic regression analysis was performed to determine predictors of expiratory airflow limitation.

Results

We found that 25 % of patients had expiratory airflow limitation, which was undiagnosed in 86 % of these patients. These patients were more likely to have pulmonary abnormalities on CCTA, including emphysema (odds ratio [OR] 4.2, 95 % confidence interval [CI] 1.12-15.1, p < 0.05) and bronchial wall thickening (OR 2.6, 95 % CI 1.0-6.5, p < 0.05). The absence of pulmonary abnormalities on CCTA accurately distinguished patients with normal lung function from those with airflow limitation (negative predictive value: 85 %). Echocardiography and polygraphy did not contribute significantly to identifying airflow limitation.

Conclusions

In conclusion, routine pre-ablation CCTA can detect pulmonary abnormalities in AF patients with airflow limitation, guiding further pulmonary assessment. Future studies should investigate its impact on ablation procedure success.

CT Scan-Derived Muscle, But Not Fat, Area Independently Predicts Mortality in COVID-19

BACKGROUND

COVID-19 has demonstrated a highly variable disease course, from asymptomatic to severe illness and eventually death. Clinical parameters, as included in the 4C Mortality Score, can predict mortality accurately in COVID-19. Additionally, CT scan-derived low muscle and high adipose tissue cross-sectional areas (CSAs) have been associated with adverse outcomes in COVID-19.

RESEARCH QUESTION

Are CT scan-derived muscle and adipose tissue CSAs associated with 30-day in-hospital mortality in COVID-19, independent of 4C Mortality Score?

STUDY DESIGN AND METHODS

This was a retrospective cohort analysis of patients with COVID-19 seeking treatment at the ED of two participating hospitals during the first wave of the pandemic. Skeletal muscle and adipose tissue CSAs were collected from routine chest CT-scans at admission. Pectoralis muscle CSA was demarcated manually at the fourth thoracic vertebra, and skeletal muscle and adipose tissue CSA was demarcated at the first lumbar vertebra level. Outcome measures and 4C Mortality Score items were retrieved from medical records.

RESULTS

Data from 578 patients were analyzed (64.6% men; mean age, 67.7 ± 13.5 years; 18.2% 30-day in-hospital mortality). Patients who died within 30 days demonstrated lower pectoralis CSA (median, 32.6 [interquartile range (IQR), 24.3-38.8] vs 35.4 [IQR, 27.2-44.2]; P = .002) than survivors, whereas visceral adipose tissue CSA was higher (median, 151.1 [IQR, 93.6-219.7] vs 112.9 [IQR, 63.7-174.1]; P = .013). In multivariate analyses, low pectoralis muscle CSA remained associated with 30-day in-hospital mortality when adjusted for 4C Mortality Score (hazard ratio, 0.98; 95% CI, 0.96-1.00; P = .038).

INTERPRETATION

CT scan-derived low pectoralis muscle CSA is associated significantly with higher 30-day in-hospital mortality in patients with COVID-19 independently of the 4C Mortality Score.

Towards texture accurate slice interpolation of medical images using PixelMiner

Quantitative image analysis models are used for medical imaging tasks such as registration, classification, object detection, and segmentation. For these models to be capable of making accurate predictions, they need valid and precise information. We propose PixelMiner, a convolution-based deep-learning model for interpolating computed tomography (CT) imaging slices. PixelMiner was designed to produce texture-accurate slice interpolations by trading off pixel accuracy for texture accuracy. PixelMiner was trained on a dataset of 7829 CT scans and validated using an external dataset. We demonstrated the model's effectiveness by using the structural similarity index (SSIM), peak signal to noise ratio (PSNR), and the root mean squared error (RMSE) of extracted texture features. Additionally, we developed and used a new metric, the mean squared mapped feature error (MSMFE). The performance of PixelMiner was compared to four other interpolation methods: (tri-)linear, (tri-)cubic, windowed sinc (WS), and nearest neighbor (NN). PixelMiner produced texture with a significantly lowest average texture error compared to all other methods with a normalized root mean squared error (NRMSE) of 0.11 (p < .01), and the significantly highest reproducibility with a concordance correlation coefficient (CCC) ≥ 0.85 (p < .01). PixelMiner was not only shown to better preserve features but was also validated using an ablation study by removing auto-regression from the model and was shown to improve segmentations on interpolated slices.

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%.

Improvement of an interobserver agreement of ARDS diagnosis by adding additional imaging and a confidence scale

Acute respiratory distress syndrome (ARDS) often is not recognized in clinical practice, largely due to variation in the interpretation of chest x-ray (CXR) leading to poor interobserver reliability. We hypothesized that the agreement in the interpretation of chest imaging for the diagnosis of ARDS in invasively ventilated intensive care unit patients between experts improves when using an 8-grade confidence scale compared to using a dichotomous assessment and that the agreement increases after adding chest computed tomography (CT) or lung ultrasound (LUS) to CXR. Three experts scored ARDS according to the Berlin definition based on case records from an observational cohort study using a dichotomous assessment and an 8-grade confidence scale. The intraclass correlation (ICC), imaging modality, and the scoring method were calculated per day and compared using bootstrapping. A consensus judgement on the presence of ARDS was based on the combined confidence grades of the experts, followed by a consensus meeting for conflicting scores. In total, 401 patients were included in the analysis. The best ICC was found using an 8-grade confidence scale for LUS (ICC: 0.49; 95%-CI: 0.29–0.63) and CT evaluation (ICC: 0.49; 95%-CI: 0.34–0.61). The ICC of CXR increased by 0.022 and of CT by 0.065 when 8-grade scoring was used instead of the dichotomous assessment. Adding information from LUS or chest CT increased the ICC by 0.25 when using the 8-grade confidence assessment. An agreement on the diagnosis of ARDS can increase substantially by adapting the scoring system from a dichotomous assessment to an 8-grade confidence scale and by adding additional imaging modalities such as LUS or chest CT. This suggests that a simple assessment of the diagnosis of ARDS with a chart review by one assessor is insufficient to define ARDS in future studies.

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction:

Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC.

Methods:

Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC.

Results:

In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively).

Conclusion:

CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.

Coronary Artery Calcifications Are Associated With More Severe Multiorgan Failure in Patients With Severe Coronavirus Disease 2019 Infection: Longitudinal Results of the Maastricht Intensive Care COVID Cohort

PURPOSE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is regarded as a multisystemic disease. Patients with preexisting cardiovascular disease have an increased risk for a more severe disease course. This study aimed to investigate if a higher degree of coronary artery calcifications (CAC) on a standard chest computed tomography (CT) scan in mechanically ventilated patients was associated with a more severe multiorgan failure over time.

MATERIALS AND METHODS

All mechanically ventilated intensive care unit patients with SARS-CoV-2 infection who underwent a chest CT were prospectively included. CT was used to establish the extent of CAC using a semiquantitative grading system. We categorized patients into 3 sex-specific tertiles of CAC: lowest, intermediate, and highest CAC score. Daily, the Sequential Organ Failure Assessment (SOFA) scores were collected to evaluate organ failure over time. Linear mixed-effects regression was used to investigate differences in SOFA scores between tertiles. The models were adjusted for age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, cardiovascular risk factors, and chronic liver, lung, and renal disease.

RESULTS

In all, 71 patients were included. Patients in the highest CAC tertile had, on average, over time, 1.8 (0.5-3.1) points higher SOFA score, compared with the lowest CAC tertile ( P =0.005). This association remained significant after adjustment for age, sex, and APACHE II score (1.4 [0.1-2.7], P =0.042) and clinically relevant after adjustment for cardiovascular risk factors (1.3 [0.0-2.7], P =0.06) and chronic diseases (1.3 [-0.2 to 2.7], P =0.085).

CONCLUSION

A greater extent of CAC is associated with a more severe multiorgan failure in mechanically ventilated coronavirus disease 2019 patients.

Automated detection and segmentation of non-small cell lung cancer computed tomography images

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours.

Correct interpretation of computer tomography (CT) scans is important for the correct assessment of a patient’s disease but can be subjective and timely. Here, the authors develop a system that can automatically segment the non-small cell lung cancer on CT images of patients and show in an in silico trial that the method was faster and more reproducible than clinicians.

Implementation of Bronchoscopic Lung Volume Reduction Using One-Way Endobronchial Valves: A Retrospective Single-Centre Cohort Study

BACKGROUND

Bronchoscopic lung volume reduction (BLVR) using 1-way endobronchial valves (EBV) has become a guideline treatment in patients with advanced emphysema. Evidence from this minimally invasive treatment derives mainly from well-designed controlled trials conducted in high-volume specialized intervention centres. Little is known about real-life outcome data in hospitals setting up this novel treatment and which favourable conditions are required for a continuous successful program.

OBJECTIVES

In this study, we aim to evaluate the eligibility rate for BLVR and whether the implementation of BLVR in our academic hospital is feasible and yields clinically significant outcomes.

METHOD

A retrospective evaluation of patients treated with EBV between January 2016 and August 2019 was conducted. COPD assessment test (CAT), forced expiratory volume in 1 s (FEV1), residual volume (RV), and 6-min walking test (6MWT) were measured at baseline and 3 months after intervention. Paired sample t tests were performed to compare means before and after intervention.

RESULTS

Of 350 subjects screened, 283 (81%) were not suitable for intervention mostly due to lack of a target lobe. The remaining 67 subjects (19%) underwent bronchoscopic assessment, and if suitable, valves were placed in the same session. In total, 55 subjects (16%) were treated with EBV of which 10 did not have complete follow-up: 6 subjects had their valves removed because of severe pneumothorax (n = 2) or lack of benefit (n = 4) and the remaining 4 had missing follow-up data. Finally, 45 patients had complete follow-up at 3 months and showed an average change ± SD in CAT -4 ± 6 points, FEV1 +190 ± 140 mL, RV -770 ± 790 mL, and +37 ± 65 m on the 6MWT (all p < 0.001). After 1-year follow-up, 34 (76%) subjects had their EBV in situ.

CONCLUSION

Implementing BLVR with EBV is feasible and effective. Only 16% of screened patients were eligible, indicating that this intervention is only applicable in a small subset of highly selected subjects with advanced emphysema, and therefore a high volume of COPD patients is essential for a sustainable BLVR program.

Functional Outcomes and Their Association With Physical Performance in Mechanically Ventilated Coronavirus Disease 2019 Survivors at 3 Months Following Hospital Discharge: A Cohort Study

OBJECTIVES

We performed a comprehensive health assessment in mechanically ventilated coronavirus disease 2019 survivors to assess the impact of respiratory and skeletal muscle injury sustained during ICU stay on physical performance at 3 months following hospital discharge.

DESIGN

Preregistered prospective observational cohort study.

SETTING

University hospital ICU.

PATIENTS

All mechanically ventilated coronavirus disease 2019 patients admitted to our ICU during the first European pandemic wave.

MEASUREMENTS AND MAIN RESULTS

At 3 months after hospital discharge, 46 survivors underwent a comprehensive physical assessment (6-min walking distance, Medical Research Council sum score and handgrip strength), a full pulmonary function test, and a chest CT scan which was used to analyze skeletal muscle architecture. In addition, patient-reported outcomes measures were collected. Physical performance assessed by 6-minute walking distance was below 80% of predicted in 48% of patients. Patients with impaired physical performance had more muscle weakness (Medical Research Council sum score 53 [51-56] vs 59 [56-60]; p < 0.001), lower lung diffusing capacity (54% [44-66%] vs 68% of predicted [61-72% of predicted]; p = 0.002), and higher intermuscular adipose tissue area (p = 0.037). Reduced lung diffusing capacity and increased intermuscular adipose tissue were independently associated with physical performance.

CONCLUSIONS

Physical disability is common at 3 months in severe coronavirus disease 2019 survivors. Lung diffusing capacity and intermuscular adipose tissue assessed on CT were independently associated with walking distance, suggesting a key role for pulmonary function and muscle quality in functional disability.

Dual-Energy Computed Tomography Compared to Lung Perfusion Scintigraphy to Assess Pulmonary Perfusion in Patients Screened for Endoscopic Lung Volume Reduction

BACKGROUND

Endoscopic lung volume reduction (ELVR) using one-way endobronchial valves is a technique to reduce hyperinflation in patients with severe emphysema by inducing collapse of a severely destroyed pulmonary lobe. Patient selection is mainly based on evaluation of emphysema severity on high-resolution computed tomography and evaluation of lung perfusion with perfusion scintigraphy. Dual-energy contrast-enhanced CT scans may be useful for perfusion assessment in emphysema but has not been compared against perfusion scintigraphy.

AIMS

The aim of the study was to compare perfusion distribution assessed with dual-energy contrast-enhanced computed tomography and perfusion scintigraphy.

MATERIAL AND METHODS

Forty consecutive patients with severe emphysema, who were screened for ELVR, were included. Perfusion was assessed with 99mTc perfusion scintigraphy and using the iodine map calculated from the dual-energy contrast-enhanced CT scans. Perfusion distribution was calculated as usually for the upper, middle, and lower thirds of both lungs with the planar technique and the iodine overlay.

RESULTS

Perfusion distribution between the right and left lung showed good correlation (r = 0.8). The limits of agreement of the mean absolute difference in percentage perfusion per region of interest were 0.75-5.6%. The upper lobes showed more severe perfusion reduction than the lower lobes. Mean difference in measured pulmonary perfusion ranged from -2.8% to 2.3%. Lower limit of agreement ranged from -8.9% to 4.6% and upper limit was 3.3-10.0%.

CONCLUSION

Quantification of perfusion distribution using planar 99mTc perfusion scintigraphy and iodine overlays calculated from dual-energy contrast-enhanced CTs correlates well with acceptable variability.

Symptomatic mediastinal mass in a 32-year-old male

A 32-year-old male, known to have atopic eczema, presented with a 3-day history of acute pleuritic chest pain and shortness of breath. No traumatic event had occurred. The pain was described as sharp and posture dependent, and was worse when leaning backwards. The patient had no palpitations, oedema or radiated pain. Dyspnoea was present both at rest and during exercise, without the presence of wheezing, haemoptysis or purulent sputum. He had no history of fever, cold shivers, weight loss or perspiration. His nutritional state was normal and he had no symptoms of nausea, vomiting or diarrhoea. For 3 days he had experienced dysphagia during meals. The patient was working full-time as a national courier and did not visit foreign countries for his work and did not travel outside Europe. The patient owned a dog but he had no specific contact with (farm) animals. He never smoked and never consumed alcohol. His physical condition, before the start of the present symptoms, was excellent due to daily exercises. On clinical examination our patient was conscious and orientated. He had a normal temperature of 36.9 °C, a respiratory rate of 16 breaths·min⁻¹, blood pressure of 115/75 mmHg, heart rate of 68 beats·min⁻¹ and an oxygen saturation of 95% on room air. Both percussion and respiratory sounds were reduced over the right hemithorax, without crackles or rhonchi. He had no pitting oedema around the ankles. A chest radiograph was performed (figure 1).

Most bronchogenic cysts are found incidentally and clinicians should be aware of an atypical case presentation. Total surgical resection is the treatment of choice of a bronchogenic cyst, especially in symptomatic patients.https://bit.ly/3uQrFXo

The prognostic value of weight and body composition changes in patients with non-small-cell lung cancer treated with nivolumab

BACKGROUND

It is not well known to what extent effectiveness of treatment with immune checkpoint inhibitors in stage IV non-small-cell lung cancer (NSCLC) is influenced by weight loss and changes in body composition. Therefore, the goal of this study was to evaluate body composition changes in relation to early weight change and overall survival (OS) in stage IV NSCLC patients treated with second-line nivolumab.

METHODS

All patients with stage IV NSCLC, who were treated with second-line nivolumab between June 2015 and December 2018 at Maastricht University Medical Center, were evaluated. Skeletal muscle mass (SMM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were assessed at the first lumbar level on computed tomography images obtained before initiation of nivolumab and at week 6 of treatment. The contribution of changes in body weight (defined as >2% loss), SMM, VAT, and SAT to OS was analysed by Kaplan-Meier method and adjusted for clinical confounders in a Cox regression analysis. The results from the study cohort were validated in another Dutch cohort from Erasmus Medical Center, Rotterdam.

RESULTS

One hundred and six patients were included in the study cohort. Loss of body weight of >2% at week 6 was an independent predictor for poor OS (hazard ratio 2.39, 95% confidence interval 1.51-3.79, P < 0.001) when adjusted for gender, >1 organ with metastasis, pretreatment hypoalbumenaemia, and pretreatment elevated C-reactive protein. The result was confirmed in the validation cohort (N = 62). Loss of SMM as a feature of cancer cachexia did not significantly predict OS in both cohorts. Significant (>2%) weight loss during treatment was reflected by a significant loss of VAT and SAT, while loss of SMM was comparable between weight-stable and weight-losing patients.

CONCLUSIONS

Weight loss, characterized by loss of subcutaneous and visceral adipose tissues, at week 6 of treatment with nivolumab, is a significant poor prognostic factor for survival in patients with Stage IV NSCLC.

Serial markers of coagulation and inflammation and the occurrence of clinical pulmonary thromboembolism in mechanically ventilated patients with SARS-CoV-2 infection; the prospective Maastricht intensive care COVID cohort

Background

The incidence of pulmonary thromboembolism is high in SARS-CoV-2 patients admitted to the Intensive Care. Elevated biomarkers of coagulation (fibrinogen and D-dimer) and inflammation (c-reactive protein (CRP) and ferritin) are associated with poor outcome in SARS-CoV-2. Whether the time-course of fibrinogen, D-dimer, CRP and ferritin is associated with the occurrence of pulmonary thromboembolism in SARS-CoV-2 patients is unknown. We hypothesise that patients on mechanical ventilation with SARS-CoV-2 infection and clinical pulmonary thromboembolism have lower concentrations of fibrinogen and higher D-dimer, CRP, and ferritin concentrations over time compared to patients without a clinical pulmonary thromboembolism.

Methods

In a prospective study, fibrinogen, D-dimer, CRP and ferritin were measured daily. Clinical suspected pulmonary thromboembolism was either confirmed or excluded based on computed tomography pulmonary angiography (CTPA) or by transthoracic ultrasound (TTU) (i.e., right-sided cardiac thrombus). In addition, patients who received therapy with recombinant tissue plasminogen activator were included when clinical instability in suspected pulmonary thromboembolism did not allow CTPA. Serial data were analysed using a mixed-effects linear regression model, and models were adjusted for known risk factors (age, sex, APACHE-II score, body mass index), biomarkers of coagulation and inflammation, and anticoagulants.

Results

Thirty-one patients were considered to suffer from pulmonary thromboembolism ((positive CTPA (n = 27), TTU positive (n = 1), therapy with recombinant tissue plasminogen activator (n = 3)), and eight patients with negative CTPA were included. After adjustment for known risk factors and anticoagulants, patients with, compared to those without, clinical pulmonary thromboembolism had lower average fibrinogen concentration of − 0.9 g/L (95% CI: − 1.6 – − 0.1) and lower average ferritin concentration of − 1045 μg/L (95% CI: − 1983 – − 106) over time. D-dimer and CRP average concentration did not significantly differ, 561 μg/L (− 6212–7334) and 27 mg/L (− 32–86) respectively. Ferritin lost statistical significance, both in sensitivity analysis and after adjustment for fibrinogen and D-dimer.

Conclusion

Lower average concentrations of fibrinogen over time were associated with the presence of clinical pulmonary thromboembolism in patients at the Intensive Care, whereas D-dimer, CRP and ferritin were not. Lower concentrations over time may indicate the consumption of fibrinogen related to thrombus formation in the pulmonary vessels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12959-021-00286-7.

Impact of the COVID-19 pandemic on incidence and severity of acute appendicitis: a comparison between 2019 and 2020

Background

During the COVID-19 pandemic, a decrease in the number of patients presenting with acute appendicitis was observed. It is unclear whether this caused a shift towards more complicated cases of acute appendicitis. We compared a cohort of patients diagnosed with acute appendicitis during the 2020 COVID-19 pandemic with a 2019 control cohort.

Methods

We retrospectively included consecutive adult patients in 21 hospitals presenting with acute appendicitis in a COVID-19 pandemic cohort (March 15 – April 30, 2020) and a control cohort (March 15 – April 30, 2019). Primary outcome was the proportion of complicated appendicitis. Secondary outcomes included prehospital delay, appendicitis severity, and postoperative complication rates.

Results

The COVID-19 pandemic cohort comprised 607 patients vs. 642 patients in the control cohort. During the COVID-19 pandemic, a higher proportion of complicated appendicitis was seen (46.9% vs. 38.5%; p = 0.003). More patients had symptoms exceeding 24 h (61.1% vs. 56.2%, respectively, p = 0.048). After correction for prehospital delay, presentation during the first wave of the COVID-19 pandemic was still associated with a higher rate of complicated appendicitis. Patients presenting > 24 h after onset of symptoms during the COVID-19 pandemic were older (median 45 vs. 37 years; p = 0.001) and had more postoperative complications (15.3% vs. 6.7%; p = 0.002).

Conclusions

Although the incidence of acute appendicitis was slightly lower during the first wave of the 2020 COVID-19 pandemic, more patients presented with a delay and with complicated appendicitis than in a corresponding period in 2019. Spontaneous resolution of mild appendicitis may have contributed to the increased proportion of patients with complicated appendicitis. Late presenting patients were older and experienced more postoperative complications compared to the control cohort.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00454-y.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Yield of Screening for COVID-19 in Asymptomatic Patients Before Elective or Emergency Surgery Using Chest CT and RT-PCR (SCOUT): Multicenter Study

OBJECTIVE

To determine the yield of preoperative screening for COVID-19 with chest CT and RT-PCR in patients without COVID-19 symptoms.

SUMMARY OF BACKGROUND DATA

Many centers are currently screening surgical patients for COVID-19 using either chest CT, RT-PCR or both, due to the risk for worsened surgical outcomes and nosocomial spread. The optimal design and yield of such a strategy are currently unknown.

METHODS

This multicenter study included consecutive adult patients without COVID-19 symptoms who underwent preoperative screening using chest CT and RT-PCR before elective or emergency surgery under general anesthesia.

RESULTS

A total of 2093 patients without COVID-19 symptoms were included in 14 participating centers; 1224 were screened by CT and RT-PCR and 869 by chest CT only. The positive yield of screening using a combination of chest CT and RT-PCR was 1.5% [95% confidence interval (CI): 0.8-2.1]. Individual yields were 0.7% (95% CI: 0.2-1.1) for chest CT and 1.1% (95% CI: 0.6-1.7) for RT-PCR; the incremental yield of chest CT was 0.4%. In relation to COVID-19 community prevalence, up to ∼6% positive RT-PCR was found for a daily hospital admission rate >1.5 per 100,000 inhabitants, and around 1.0% for lower prevalence.

CONCLUSIONS

One in every 100 patients without COVID-19 symptoms tested positive for SARS-CoV-2 with RT-PCR; this yield increased in conjunction with community prevalence. The added value of chest CT was limited. Preoperative screening allowed us to take adequate precautions for SARS-CoV-2 positive patients in a surgical population, whereas negative patients needed only routine procedures.

Idiopathic pulmonary fibrosis: Current knowledge, future perspectives and its importance in radiation oncology

Idiopathic pulmonary fibrosis (IPF) is a progressive, fibrotic lung disease with an unknown cause. Uncertainties still remain regarding the pathogenesis of IPF, and the prognosis of this disease is poor despite some recent improvements in treatment. Radiation induced lung injury (RILI) is a common complication and a dose-limiting toxicity of thoracic radiotherapy. Importantly, IPF is a crucial risk factor for pulmonary toxicity after thoracic radiotherapy. Although IPF is not universally accepted as a definite contraindication for thoracic radiotherapy at present, it has been shown that IPF can increase the risk of severe and fatal complications after thoracic radiotherapy. Proton beam therapy has shown promising results in reducing the incidence of thoracic radiotherapy related life-threatening complications in IPF patients, but the current evidence is not sufficient to recommend the standard use of it. Many similarities are noticeable between IPF and RILI in terms of pathogenesis and underlying mechanisms. Better understanding of the mechanisms of IPF and RILI may enable clinicians to provide safer and more effective thoracic radiotherapy treatments in cancer patients with IPF. In this review, we summarize the current knowledge of IPF, present the importance of IPF in radiation oncology practice, and highlight the similarities and relationship between IPF and RILI.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience

Background

Clinicians need rapid and reliable diagnosis of coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions.

Purpose

To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 reporting and data system (CO-RADS).

Materials and Methods

This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT was obtained for primary workup and scored using the 5-point CO-RADS scheme for suspicion of COVID-19. CT was compared with SARS-CoV-2 RT-PCR, and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristics curve (AUC) analysis and diagnostic odds ratios (OR) against both reference standards. Subgroup analysis was performed based on symptom duration grouped presentations of < 48 hours, 48 hours through 7 days, and > 7 days.

Results

A total of 1070 patients (median age 66, IQR 54-75, 626 men) were included, of whom 536/1070 (50%) had a positive RT-PCR, 137/1070 (13%) patients were considered to have a possible or probable COVID- 19 based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95%CI 0.84-0.89) compared with RT-PCR and 0.87 (95%CI 0.85-0.89) compared with the clinical reference standard. A CO-RADS score ≥4 yielded an OR of 25.9 (95%CI 18.7-35.9) for a COVID-19 diagnosis by RT-PCR, and an OR of 30.6 (95%CI 21.1-44.4) by the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95%CI 0.62-0.80; P<.001).

Conclusion

Chest CT analysis using the COVID-19 reporting and data system (CO-RADS) enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours.

See also the editorial by Elicker.

Differentiation of COVID-19 Pneumonitis and ICI Induced Pneumonitis

Immune checkpoint inhibitors (ICI) have become the standard of care treatment for several tumor types. ICI-induced pneumonitis is a serious complication seen with treatment with these agents. Cancer has been reported to be one of the risk factors for severe coronavirus disease 2019 (COVID-19) caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that has engulfed the world in the last couple of months. In patients with cancer treated with ICI who present at the emergency department with respiratory symptoms during the COVID-19 pandemic, correct diagnosis can be challenging. Symptoms and radiological features of ICI pneumonitis can be overlapping with those of COVID-19 related pneumonia. For the latter, dexamethasone and remdesivir have shown encouraging results, while vaccines are currently being evaluated in phase III trials. The mainstay of treatment in ICI pneumonitis is immunosuppressive therapy, as this is a potentially fatal adverse event. It has been speculated that immunosuppression may be associated with increased risk of progression to severe COVID-19, especially during the early stage of infection with SARS-CoV-2. Therefore, distinction between these two entities is warranted. We summarize the clinical, radiological features as well as additional investigations of both entities, and suggest a diagnostic algorithm for distinction between the two. This algorithm may be a supportive tool for clinicians to diagnose the underlying cause of the pneumonitis in patients treated with ICI during this COVID-19 pandemic.

The prevalence of pulmonary embolism in patients with COVID-19 and respiratory decline: A three-setting comparison

BACKGROUND

The risk of pulmonary embolism (PE) in patients with Coronavirus Disease 2019 (COVID-19) is recognized. The prevalence of PE in patients with respiratory deterioration at the Emergency Department (ED), the regular ward, and the Intensive Care Unit (ICU) are not well-established.

OBJECTIVES

We aimed to investigate how often PE was present in individuals with COVID-19 and respiratory deterioration in different settings, and whether or not disease severity as measured by CT-severity score (CTSS) was related to the occurrence of PE.

PATIENTS/METHODS

Between April 6th and May 3rd, we enrolled 60 consecutive adult patients with confirmed COVID-19 from the ED, regular ward and ICU who met the pre-specified criteria for respiratory deterioration.

RESULTS

A total of 24 (24/60: 40% (95% CI: 28-54%)) patients were diagnosed with PE, of whom 6 were in the ED (6/23: 26% (95% CI: 10-46%)), 8 in the regular ward (8/24: 33% (95% CI: 16-55%)), and 10 in the ICU (10/13: 77% (95% CI: 46-95%)). CTSS (per unit) was not associated with the occurrence of PE (age and sex-adjusted OR 1.06 (95%CI 0.98-1.15)).

CONCLUSION

The number of PE diagnosis among patients with COVID-19 and respiratory deterioration was high; 26% in the ED, 33% in the regular ward and 77% in the ICU respectively. In our cohort CTSS was not associated with the occurrence of PE. Based on the high number of patients diagnosed with PE among those scanned we recommend a low threshold for performing computed tomography angiography in patients with COVID-19 and respiratory deterioration.

Exploring imaging features of molecular subtypes of large cell neuroendocrine carcinoma (LCNEC)

OBJECTIVES

Radiological characteristics and radiomics signatures can aid in differentiation between small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). We investigated whether molecular subtypes of large cell neuroendocrine carcinoma (LCNEC), i.e. SCLC-like (with pRb loss) vs. NSCLC-like (with pRb expression), can be distinguished by imaging based on (1) imaging interpretation, (2) semantic features, and/or (3) a radiomics signature, designed to differentiate between SCLC and NSCLC.

MATERIALS AND METHODS

Pulmonary oncologists and chest radiologists assessed chest CT-scans of 44 LCNEC patients for 'small cell-like' or 'non-small cell-like' appearance. The radiologists also scored semantic features of 50 LCNEC scans. Finally, a radiomics signature was trained on a dataset containing 48 SCLC and 76 NSCLC scans and validated on an external set of 58 SCLC and 40 NSCLC scans. This signature was applied on scans of 28 SCLC-like and 8 NSCLC-like LCNEC patients.

RESULTS

Pulmonary oncologists and radiologists were unable to differentiate between molecular subtypes of LCNEC and no significant differences in semantic features were found. The area under the receiver operating characteristics curve of the radiomics signature in the validation set (SCLC vs. NSCLC) was 0.84 (95% confidence interval (CI) 0.77-0.92) and 0.58 (95% CI 0.29-0.86) in the LCNEC dataset (SCLC-like vs. NSCLC-like).

CONCLUSION

LCNEC appears to have radiological characteristics of both SCLC and NSCLC, irrespective of pRb loss, compatible with the SCLC-like subtype. Imaging interpretation, semantic features and our radiomics signature designed to differentiate between SCLC and NSCLC were unable to separate molecular LCNEC subtypes, which underscores that LCNEC is a unique disease.

CT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study

INTRODUCTION

Early differentiation between emergency department (ED) patients with and without corona virus disease (COVID-19) is very important. Chest CT scan may be helpful in early diagnosing of COVID-19. We investigated the diagnostic accuracy of CT using RT-PCR for SARS-CoV-2 as reference standard and investigated reasons for discordant results between the two tests.

METHODS

In this prospective single centre study in the Netherlands, all adult symptomatic ED patients had both a CT scan and a RT-PCR upon arrival at the ED. CT results were compared with PCR test(s). Diagnostic accuracy was calculated. Discordant results were investigated using discharge diagnoses.

RESULTS

Between March 13th and March 24th 2020, 193 symptomatic ED patients were included. In total, 43.0% of patients had a positive PCR and 56.5% a positive CT, resulting in a sensitivity of 89.2%, specificity 68.2%, likelihood ratio (LR)+ 2.81 and LR- 0.16. Sensitivity was higher in patients with high risk pneumonia (CURB-65 score ≥3; n = 17, 100%) and with sepsis (SOFA score ≥2; n = 137, 95.5%). Of the 35 patients (31.8%) with a suspicious CT and a negative RT-PCR, 9 had another respiratory viral pathogen, and in 7 patients, COVID-19 was considered likely. One of nine patients with a non-suspicious CT and a positive PCR had developed symptoms within 48 hours before scanning.

DISCUSSION

The accuracy of chest CT in symptomatic ED patients is high, but used as a single diagnostic test, CT can not safely diagnose or exclude COVID-19. However, CT can be used as a quick tool to categorize patients into "probably positive" and "probably negative" cohorts.

The Emerging Role of Radiomics in COPD and Lung Cancer

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.

CT in relation to RT-PCR in diagnosing COVID-19 in The Netherlands: A prospective study

INTRODUCTION

Early differentiation between emergency department (ED) patients with and without corona virus disease (COVID-19) is very important. Chest CT scan may be helpful in early diagnosing of COVID-19. We investigated the diagnostic accuracy of CT using RT-PCR for SARS-CoV-2 as reference standard and investigated reasons for discordant results between the two tests.

METHODS

In this prospective single centre study in the Netherlands, all adult symptomatic ED patients had both a CT scan and a RT-PCR upon arrival at the ED. CT results were compared with PCR test(s). Diagnostic accuracy was calculated. Discordant results were investigated using discharge diagnoses.

RESULTS

Between March 13th and March 24th 2020, 193 symptomatic ED patients were included. In total, 43.0% of patients had a positive PCR and 56.5% a positive CT, resulting in a sensitivity of 89.2%, specificity 68.2%, likelihood ratio (LR)+ 2.81 and LR- 0.16. Sensitivity was higher in patients with high risk pneumonia (CURB-65 score ≥3; n = 17, 100%) and with sepsis (SOFA score ≥2; n = 137, 95.5%). Of the 35 patients (31.8%) with a suspicious CT and a negative RT-PCR, 9 had another respiratory viral pathogen, and in 7 patients, COVID-19 was considered likely. One of nine patients with a non-suspicious CT and a positive PCR had developed symptoms within 48 hours before scanning.

DISCUSSION

The accuracy of chest CT in symptomatic ED patients is high, but used as a single diagnostic test, CT can not safely diagnose or exclude COVID-19. However, CT can be used as a quick tool to categorize patients into "probably positive" and "probably negative" cohorts.

What you see is (not) what you get: tools for a non-radiologist to evaluate image quality in lung cancer

Medical images are an integral part of oncological patient records and they are reviewed by many different specialists. Therefore, it is important that besides imaging experts, other clinicians are also aware that the diagnostic value of a scan is influenced by the applied imaging protocol. Based on two clinical lung cancer trials, we experienced that, even within a study protocol, there is a large variability in imaging parameters, which has direct impact on the interpretation of the image. These two trials were: 1) the NTR3628 in which the added value of gadolinium magnetic resonance imaging (Gd-MRI) to dedicated contrast enhanced computed tomography (CE-CT) for detecting asymptomatic brain metastases in stage III non-small cell lung cancer (NSCLC) was investigated and 2) a sub-study of the NVALT 12 trial (NCT01171170) in which repeated 18 F-fludeoxyglucose positron emission tomography (¹⁸F-FDG-PET) imaging for early response assessment was investigated. Based on the problems encountered in the two trials, we provide recommendations for non-radiology clinicians, which can be used in daily interpretation of imaging. Variations in image parameters cannot only influence trial results, but sub-optimal imaging can also influence treatment decisions in daily lung cancer care, when a physician is not aware of the scanning details.

Semi-automatic classification of textures in thoracic CT scans

The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan.

Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach. The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated. In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) participants available for analysis. The median follow-up time was 95 months (range 20-110 months). 33 (28%) SSNs were resected and 28 of those were (pre-) invasive. None of the non-resected SSNs progressed into a clinically relevant malignancy. Persistent SSNs rarely developed into clinically manifest malignancies unexpectedly. Close follow-up with computed tomography may be a safe option to monitor changes.

Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements

Rationale

Accurate measurement of subsolid pulmonary nodules (SSN) is becoming increasingly important in the management of these nodules. SSNs were previously quantified with time-consuming manual measurements. The aim of the present study is to test the feasibility of semi-automatic SSNs measurements and to compare the results to the manual measurements.

Methods

In 33 lung cancer screening participants with 33 SSNs, the nodules were previously quantified by two observers manually. In the present study two observers quantified these nodules by using semi-automated nodule volumetry software. Nodules were quantified for effective diameter, volume and mass. The manual and semi-automatic measurements were compared using Bland-Altman plots and paired T tests. Observer agreement was calculated as an intraclass correlation coefficient. Data are presented as mean (SD).

Results

Semi-automated measurements were feasible in all 33 nodules. Nodule diameter, volume and mass were 11.2 (3.3) mm, 935 (691) ml and 379 (311) milligrams for observer 1 and 11.1 (3.7) mm, 986 (797) ml and 399 (344) milligrams for observer 2, respectively. Agreement between observers and within observer 1 for the semi-automatic measurements was good with an intraclass correlation coefficient >0.89. For observer 1 and observer 2, measured diameter was 8.8% and 10.3% larger (p<0.001), measured volume was 24.3% and 26.5% larger (p<0.001) and measured mass was 10.6% and 12.0% larger (p<0.001) with the semi-automatic program compared to the manual measurements.

Conclusion

Semi-automated measurement of the diameter, volume and mass of SSNs is feasible with good observer agreement. Semi-automated measurement makes quantification of mass and volume feasible in daily practice.

Visual versus automated evaluation of chest computed tomography for the presence of chronic obstructive pulmonary disease

Background

Incidental CT findings may provide an opportunity for early detection of chronic obstructive pulmonary disease (COPD), which may prove important in CT-based lung cancer screening setting. We aimed to determine the diagnostic performance of human observers to visually evaluate COPD presence on CT images, in comparison to automated evaluation using quantitative CT measures.

Methods

This study was approved by the Dutch Ministry of Health and the institutional review board. All participants provided written informed consent. We studied 266 heavy smokers enrolled in a lung cancer screening trial. All subjects underwent volumetric inspiratory and expiratory chest computed tomography (CT). Pulmonary function testing was used as the reference standard for COPD. We evaluated the diagnostic performance of eight observers and one automated model based on quantitative CT measures.

Results

The prevalence of COPD in the study population was 44% (118/266), of whom 62% (73/118) had mild disease. The diagnostic accuracy was 74.1% in the automated evaluation, and ranged between 58.3% and 74.3% for the visual evaluation of CT images. The positive predictive value was 74.3% in the automated evaluation, and ranged between 52.9% and 74.7% for the visual evaluation. Interobserver variation was substantial, even within the subgroup of experienced observers. Agreement within observers yielded kappa values between 0.28 and 0.68, regardless of the level of expertise. The agreement between the observers and the automated CT model showed kappa values of 0.12–0.35.

Conclusions

Visual evaluation of COPD presence on chest CT images provides at best modest accuracy and is associated with substantial interobserver variation. Automated evaluation of COPD subjects using quantitative CT measures appears superior to visual evaluation by human observers.

Variation in quantitative CT air trapping in heavy smokers on repeat CT examinations

Objectives

To determine the variation in quantitative computed tomography (CT) measures of air trapping in low-dose chest CTs of heavy smokers.

Methods

We analysed 45 subjects from a lung cancer screening trial, examined by CT twice within 3 months. Inspiratory and expiratory low-dose CT was obtained using breath hold instructions. CT air trapping was defined as the percentage of voxels in expiratory CT with an attenuation below −856 HU (EXP₋₈₅₆) and the expiratory to inspiratory ratio of mean lung density (E/I-ratio_MLD). Variation was determined using limits of agreement, defined as 1.96 times the standard deviation of the mean difference. The effect of both lung volume correction and breath hold reproducibility was determined.

Results

The limits of agreement for uncorrected CT air trapping measurements were −15.0 to 11.7 % (EXP₋₈₅₆) and −9.8 to 8.0 % (E/I-ratio_MLD). Good breath hold reproducibility significantly narrowed the limits for EXP₋₈₅₆ (−10.7 to 7.5 %, P = 0.002), but not for E/I-ratio_MLD (−9.2 to 7.9 %, P = 0.75). Statistical lung volume correction did not improve the limits for EXP₋₈₅₆ (−12.5 to 8.8 %, P = 0.12) and E/I-ratio_MLD (−7.5 to 5.8 %, P = 0.17).

Conclusions

Quantitative air trapping measures on low-dose CT of heavy smokers show considerable variation on repeat CT examinations, regardless of lung volume correction or reproducible breath holds.

Key Points

• Computed tomography quantitatively measures small airways disease in heavy smokers.

• Measurements of air trapping vary considerably on repeat CT examinations.

• Variation remains substantial even with reproducible breath holds and lung volume correction.

The relationship between lung function impairment and quantitative computed tomography in chronic obstructive pulmonary disease

OBJECTIVES

To determine the relationship between lung function impairment and quantitative computed tomography (CT) measurements of air trapping and emphysema in a population of current and former heavy smokers with and without airflow limitation.

METHODS

In 248 subjects (50 normal smokers; 50 mild obstruction; 50 moderate obstruction; 50 severe obstruction; 48 very severe obstruction) CT emphysema and CT air trapping were quantified on paired inspiratory and end-expiratory CT examinations using several available quantification methods. CT measurements were related to lung function (FEV(1), FEV(1)/FVC, RV/TLC, Kco) by univariate and multivariate linear regression analysis.

RESULTS

Quantitative CT measurements of emphysema and air trapping were strongly correlated to airflow limitation (univariate r-squared up to 0.72, p < 0.001). In multivariate analysis, the combination of CT emphysema and CT air trapping explained 68-83% of the variability in airflow limitation in subjects covering the total range of airflow limitation (p < 0.001).

CONCLUSIONS

The combination of quantitative CT air trapping and emphysema measurements is strongly associated with lung function impairment in current and former heavy smokers with a wide range of airflow limitation.

Quantitative computed tomography in COPD: possibilities and limitations

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease that is characterized by chronic airflow limitation. Unraveling of this heterogeneity is challenging but important, because it might enable more accurate diagnosis and treatment. Because spirometry cannot distinguish between the different contributing pathways of airflow limitation, and visual scoring is time-consuming and prone to observer variability, other techniques are sought to start this phenotyping process. Quantitative computed tomography (CT) is a promising technique, because current CT technology is able to quantify emphysema, air trapping, and large airway wall dimensions. This review focuses on CT quantification techniques of COPD disease components and their current status and role in phenotyping COPD.

Screening for emphysema via exhaled volatile organic compounds

Chronic obstructive pulmonary disease (COPD)/emphysema risk groups are well defined and screening allows for early identification of disease. The capability of exhaled volatile organic compounds (VOCs) to detect emphysema, as found by computed tomography (CT) in current and former heavy smokers participating in a lung cancer screening trial, was investigated. CT scans, pulmonary function tests and breath sample collections were obtained from 204 subjects. Breath samples were analyzed with a proton-transfer reaction mass spectrometer (PTR-MS) to obtain VOC profiles listed as ions at various mass-to-charge ratios (m/z). Using bootstrapped stepwise forward logistic regression, we identified specific breath profiles as a potential tool for the diagnosis of emphysema, of airflow limitation or gas-exchange impairment. A marker for emphysema was found at m/z 87 (tentatively attributed to 2-methylbutanal). The area under the receiver operating characteristic curve (ROC) of this marker to diagnose emphysema was 0.588 (95% CI 0.453-0.662). Mass-to-charge ratios m/z 52 (most likely chloramine) and m/z 135 (alkyl benzene) were linked to obstructive disease and m/z 122 (most probably alkyl homologs) to an impaired diffusion capacity. ROC areas were 0.646 (95% CI 0.562-0.730) and 0.671 (95% CI 0.524-0.710), respectively. In the screening setting, exhaled VOCs measured by PTR-MS constitute weak markers for emphysema, pulmonary obstruction and impaired diffusion capacity.

Identification of chronic obstructive pulmonary disease in lung cancer screening computed tomographic scans

CONTEXT

Smoking is a major risk factor for both cancer and chronic obstructive pulmonary disease (COPD). Computed tomography (CT)-based lung cancer screening may provide an opportunity to detect additional individuals with COPD at an early stage.

OBJECTIVE

To determine whether low-dose lung cancer screening CT scans can be used to identify participants with COPD.

DESIGN, SETTING, AND PATIENTS

Single-center prospective cross-sectional study within an ongoing lung cancer screening trial. Prebronchodilator pulmonary function testing with inspiratory and expiratory CT on the same day was obtained from 1140 male participants between July 2007 and September 2008. Computed tomographic emphysema was defined as percentage of voxels less than -950 Hounsfield units (HU), and CT air trapping was defined as the expiratory:inspiratory ratio of mean lung density. Chronic obstructive pulmonary disease was defined as the ratio of forced expiratory volume in the first second to forced vital capacity (FEV(1)/FVC) of less than 70%. Logistic regression was used to develop a diagnostic prediction model for airflow limitation.

MAIN OUTCOME MEASURES

Diagnostic accuracy of COPD diagnosis using pulmonary function tests as the reference standard.

RESULTS

Four hundred thirty-seven participants (38%) had COPD according to lung function testing. A diagnostic model with CT emphysema, CT air trapping, body mass index, pack-years, and smoking status corrected for overoptimism (internal validation) yielded an area under the receiver operating characteristic curve of 0.83 (95% CI, 0.81-0.86). Using the point of optimal accuracy, the model identified 274 participants with COPD with 85 false-positives, a sensitivity of 63% (95% CI, 58%-67%), specificity of 88% (95% CI, 85%-90%), positive predictive value of 76% (95% CI, 72%-81%); and negative predictive value of 79% (95% CI, 76%-82%). The diagnostic model showed an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.86-0.88) for participants with symptoms and 0.78 (95% CI, 0.76-0.80) for those without symptoms.

CONCLUSION

Among men who are current and former heavy smokers, low-dose inspiratory and expiratory CT scans obtained for lung cancer screening can identify participants with COPD, with a sensitivity of 63% and a specificity of 88%.

Management of lung nodules detected by volume CT scanning

BACKGROUND

The use of multidetector computed tomography (CT) in lung-cancer screening trials involving subjects with an increased risk of lung cancer has highlighted the problem for the clinician of deciding on the best course of action when noncalcified pulmonary nodules are detected by CT.

METHODS

A total of 7557 participants underwent CT screening in years 1, 2, and 4 of a randomized trial of lung-cancer screening. We used software to evaluate a noncalcified nodule according to its volume or volume-doubling time. Growth was defined as an increase in volume of at least 25% between two scans. The first-round screening test was considered to be negative if the volume of a nodule was less than 50 mm(3), if it was 50 to 500 mm(3) but had not grown by the time of the 3-month follow-up CT, or if, in the case of those that had grown, the volume-doubling time was 400 days or more.

RESULTS

In the first and second rounds of screening, 2.6% and 1.8% of the participants, respectively, had a positive test result. In round one, the sensitivity of the screen was 94.6% (95% confidence interval [CI], 86.5 to 98.0) and the negative predictive value 99.9% (95% CI, 99.9 to 100.0). In the 7361 subjects with a negative screening result in round one, 20 lung cancers were detected after 2 years of follow-up.

CONCLUSIONS

Among subjects at high risk for lung cancer who were screened in three rounds of CT scanning and in whom noncalcified pulmonary nodules were evaluated according to volume and volume-doubling time, the chances of finding lung cancer 1 and 2 years after a negative first-round test were 1 in 1000 and 3 in 1000, respectively. (Current Controlled Trials number, ISRCTN63545820.)

Pulmonary nodules: Interscan variability of semiautomated volume measurements with multisection CT-- influence of inspiration level, nodule size, and segmentation performance

PURPOSE

To prospectively assess the precision of semiautomated volume measurements of pulmonary nodules at low-dose multi-detector row computed tomography (CT) and to investigate the influence of nodule size, segmentation algorithm, and inspiration level.

MATERIALS AND METHODS

This study had institutional review board approval; written informed consent was obtained from all patients. Between June 2004 and March 2005, 20 patients (15 men, five women; age range, 40-84 years; mean age, 57 years) referred for chest CT for known lung metastases underwent two additional low-dose chest CT examinations without contrast material (collimation, 16 x 0.75 mm). Between these examinations, patients got off and on the table to simulate the conditions for a follow-up examination. Noncalcified solid pulmonary nodules between 15 and 500 mm(3) that did not abut vessel or pleura were measured in both studies by using widely applied commercial semiautomated software. Interscan variability was established with the Bland and Altman approach. The impact of nodule shape (spherical or nonspherical) on measurement variability was assessed by using one-way analysis of variance, while the contributions of mean nodule volume and change in lung volume were investigated with univariate linear regression for completely (group A) and incompletely (group B) segmented nodules.

RESULTS

Two hundred eighteen eligible nodules (volume range, 16.4-472.7 mm(3); 106 spherical, 112 nonspherical) were evaluated. The 95% confidence interval for difference in measured volumes was -21.2%, 23.8% (mean difference, 1.3%). The precision of nodule segmentation was highly dependent on nodule shape (P < .001) and was weakly related to inspiration level for completely segmented nodules (r = -0.20; P < .047), while mean nodule volume did not show any effect (P = .15 and P = .81 for group A and B nodules, respectively).

CONCLUSION

Variation of semiautomated volume measurements of pulmonary nodules can be substantial. Segmentation represents the most important factor contributing to measurement variability, while change in inspiration level has only a weak effect for completely segmented nodules.

Monitoring of Smoking-induced Emphysema with CT in a Lung Cancer Screening Setting: Detection of Real Increase in Extent of Emphysema

PURPOSE

To retrospectively establish the minimum increase in emphysema score (ES) required for detection of real increased extent of emphysema with 95% confidence by using multi-detector row computed tomography (CT) in a lung cancer screening setting.

MATERIALS AND METHODS

The study was a substudy of the NELSON project that was approved by the Dutch Ministry of Health and the ethics committee of each participating hospital, with patient informed consent. For this substudy, original approval and informed consent allowed use of data for future research. Among 1684 men screened with low-dose multi-detector row CT (30 mAs, 16 detector rows, 0.75-mm section thickness) between April 2004 and March 2005, only participants who underwent repeat multi-detector row CT with the same scanner after 3 months because of an indeterminate pulmonary nodule were included. Extent of emphysema was considered to remain stable in this short period. Extent of low-attenuation areas representing emphysema was computed for repeat and baseline scans as percentage of lung volume below three attenuation threshold values (-910 HU, -930 HU, -950 HU). Limits of agreement were determined with Bland-Altman approach; upper limits were used to deduce the minimum increase in ES required for detecting increased extent of emphysema with 95% probability. Factors influencing the limits of agreement were determined.

RESULTS

In total, 157 men (mean age, 60 years) were included in the study. Limits of agreement for differences in total lung volume between repeat and baseline scans were -13.4% to +12.6% at -910 HU, -4.7% to +4.2% at -930 HU, and -1.3% to +1.1% at -950 HU. Differences in ES showed weak to moderate correlation with variation in level of inspiration (r=0.20-0.49, P<.05). Scanner calibration could be excluded as a factor contributing to variation in ES.

CONCLUSION

Increase in ES required to detect increased extent of smoking-related emphysema with 95% probability varies between 1.1% of total lung volume at -950 HU and 12.6% at -910 HU for low-dose multi-detector row CT. Clinical trial registration no. ISRCTN63545820.

Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners

The purpose of this study was to compare the accuracy of an automated volumetry software for phantom pulmonary nodules across various 16-slice multislice spiral CT (MSCT) scanners from different vendors. A lung phantom containing five different nodule categories (intraparenchymal, around a vessel, vessel attached, pleural, and attached to the pleura), with each category comprised of 7-9 nodules (total, n = 40) of varying sizes (diameter 3-10 mm; volume 6.62 mm(3)-525 mm(3)), was scanned with four different 16-slice MSCT scanners (Siemens, GE, Philips, Toshiba). Routine and low-dose chest protocols with thin and thick collimations were applied. The data from all scanners were used for further analysis using a dedicated prototype volumetry software. Absolute percentage volume errors (APE) were calculated and compared. The mean APE for all nodules was 8.4% (+/-7.7%) for data acquired with the 16-slice Siemens scanner, 14.3% (+/-11.1%) for the GE scanner, 9.7% (+/-9.6%) for the Philips scanner and 7.5% (+/-7.2%) for the Toshiba scanner, respectively. The lowest APEs were found within the diameter size range of 5-10 mm and volumes >66 mm(3). Nodule volumetry is accurate with a reasonable volume error in data from different scanner vendors. This may have an important impact for intraindividual follow-up studies.