Swiss pilot low-dose computed tomography lung cancer screening study

Objective

Low-dose computed tomography (LDCT) lung cancer screening is endorsed by US guidelines and has recently been shown effective in a large European randomized controlled trial. Nevertheless, actual realization of a lung cancer screening program is challenging and depends on country-specific factors. This pilot study aimed to evaluate implementation, execution, and performance of LDCT lung cancer screening in Switzerland.

Methods

Since October 2018, asymptomatic participants aged 55–74 years with more than 30 pack-years smoking history were enrolled at a tertiary hospital in Switzerland. Participants with history of lung cancer, major (palliative) health problems or those that had a thorax CT scan 18 months prior to enrollment were excluded. First, we evaluated lung cancer risk according to NLST guidelines. Second, we estimated lung cancer risk using the PLCOm2012 model risk calculator with threshold of 5%. Lung nodules were assessed according to Lung-RADS (Version 1.1. 2019). Participants were recruited through flyers, a newspaper article and pulmonary specialists. Screening consisted of one LDCT-scan, follow-up was recommended for suspicious nodules only. LDCT assessment was performed by two radiologists, one of them a board certified chest radiologist. Enrollment and follow-up are currently ongoing.

Results

To date, 75 participants (25 (33%) females) with a median age of 62 years (interquartile range [IQR] 56–67 years) were included. Median number of pack years smoked was 49 (IQR 41–58 pack years). Median PLCOm2012 6-year lung cancer probability was 2.7% (IQR 2.6–2.9%), 19 (26%) participants had stopped smoking before enrollment. 6 participants required follow up imaging of suspect nodules, resulting in a recall rate of 8%. At baseline, lung cancer was found in 2 (2.7%, one squamous cell (stage IIIA) and one adenocarcinoma (stage IV)) participants.

Conclusion

In this Swiss LDCT lung cancer screening pilot study using modified inclusion criteria, 2.7% were diagnosed with lung cancer to date.

OP0199 RADIOMIC SIGNATURES REFLECT TREATMENT RESPONSE TO NINTEDANIB IN PRECLINICAL LUNG FIBROSIS MODEL

Background

Responses to anti-fibrotic drugs in preclinical disease models are difficult to quantify by histological analysis of single tissue sections. Quantitative in-depth analysis of imaging data, termed “radiomics”, may represent a more reliable and accurate measure of treatment response since the pathology of the whole organ is captured.

Objectives

To study the potential of µCT-derived radiomic features to reflect response to Nintedanib in the bleomycin (BLM)-induced murine model of fibrosing interstitial lung disease.

Methods

All C57BL/6J mice from both study groups were intratracheally instilled with 2 U/kg BLM on day 0 to induce lung fibrosis. Nintedanib was administered daily by gavage at 60 mg/kg for two weeks starting from day 7 (n=15). Controls received equivalent treatment with vehicle-only (n=19). Whole lung µCT scans (SkyScan 1176, Bruker) of each animal were acquired at baseline (day 0), pre-treatment (day 7), and post-treatment (day 21). The Ashcroft score was assessed on Sirius Red stained lung sections post-treatment. Lung volumes in µCTs were defined semi-automatically in MIM Software (6.9.2), followed by extraction of radiomic features with our in-house developed software Z-Rad (7.3.1). Each data set contained 1’386 features, describing image characteristics with histogram, texture, and wavelet functions. Data pre-processing involved removal of features sensitive to intra- and interobserver delineation variability (ICC<0.75), highly correlated features (Pearson’s r>0.95), and features not significantly changing between days 0 and 7 (p>0.05). Agglomerative clustering of radiomic temporal trajectories was performed on the Nintedanib group to identify distinct feature clusters. The identified feature sets were then used to plot average feature value trajectories for both study groups in each cluster. To identify features significantly different between a) Nintedanib vs. control, and b) pre- vs. post-treatment, Mann-Whitney U and Wilcoxon signed-rank tests were used, respectively. Samples were pooled from two independent experiments.

Results

Evaluation of tissue sections did not show a significant treatment-induced reduction of fibrosis with average Ashcroft scores of 3.7 (±1.2 s.d.) and 3.4 (±1.6 s.d.) in Nintedanib and control samples, respectively (p>0.05). Radiomics data analysis revealed two feature clusters in Nintedanib samples, composed of 52 features (cluster 1) and 96 features (cluster 2), the trajectories of which were then plotted for both study groups. In cluster 1, feature value trajectories significantly decreased in both Nintedanib and control group between pre-and post-treatment (p<0.001), whereas feature values in cluster 2 remained flat (p>0.05). Importantly, Nintedanib-treated mice displayed a much more pronounced feature value decrease post-treatment in cluster 1 compared to the control group (p<0.05). Here, feature values post-treatment resembled pre-disease baseline conditions in the Nintedanib group (p>0.05), whereas the control group remained significantly different from baseline (p<0.01). Cluster 1 was composed of 6 histogram, 11 texture, and 35 wavelet features, emphasizing the role of high-dimensional metrics for the detection of differences.

Conclusion

Histological quantification of lung fibrosis accounts only for a small fraction of the whole pathology in a spatially heterogeneous disease. We demonstrated that µCT-derived radiomic features identified significant differences on imaging level following Nintedanib treatment, which we could not reliably detect on tissue level using Ashcroft scoring. These findings hold great potential for the development of novel readouts for improved stratification of anti-fibrotic treatment effects in preclinical models.

Acknowledgements

This study received funding support from the Swiss Lung Association.

Disclosure of Interests

David Lauer Shareholder of: Roche (no relation to project), Employee of: Former employee of Roche (no relation to project), Janine Schniering: None declared, Hubert Gabrys: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Oliver Distler Speakers bureau: Speaker fees in the area of systemic sclerosis and related complications from Bayer, Boehringer Ingelheim, Janssen, Medscape, Consultant of: Consultancies in the area of systemic sclerosis and its complications with Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, 4P Science, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Roivant, Sanofi and Topadur, Grant/research support from: Grant/research support from Kymera, Mitsubishi Tanabe, Boehringer Ingelheim, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Received speaker fees from Boehringer-Ingelheim as well as congress support from Medtalk, Pfizer, Roche, Actelion, Mepha, and MSD, Consultant of: Consultancies with Novartis, Boehringer Ingelheim, Janssen-Cilag. Has a patent mir-29 for the treatment of systemic sclerosis issued (US8247389, EP2331143), Grant/research support from: Had grant/research support from AbbVie, Protagen, Novartis Biomedical Research.

COVID-19 pneumonia imaging follow-up: when and how? A proposition from ESTI and ESR

Abstract

This document from the European Society of Thoracic Imaging (ESTI) and the European Society of Radiology (ESR) discusses the role of imaging in the long-term follow-up of COVID-19 patients, to define which patients may benefit from imaging, and what imaging modalities and protocols should be used. Insights into imaging features encountered on computed tomography (CT) scans and potential pitfalls are discussed and possible areas for future review and research are also included.

Key Points

• Post-COVID-19 pneumonia changes are mainly consistent with prior organizing pneumonia and are likely to disappear within 12 months of recovery from the acute infection in the majority of patients.

• At present, with the longest series of follow-up examinations reported not exceeding 12 months, the development of persistent or progressive fibrosis in at least some individuals cannot yet be excluded.

• Residual ground glass opacification may be associated with persisting bronchial dilatation and distortion, and might be termed “fibrotic-like changes” probably consistent with prior organizing pneumonia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08317-7.

OP0150 MACHINE LEARNING APPROACHES FOR RISK MODELLING IN INTERSTITIAL LUNG DISEASE ASSOCIATED WITH SYSTEMIC SCLEROSIS USING HIGH DIMENSIONAL IMAGE ANALYSIS

Background:

The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).

Objectives:

  •bTo develop robust, machine learning (ML) workflow for “radiomics” data in SSc-ILD to select optimal methods for prediction.

  •oTo predict the time to individual lung function decline defined as defined by the time to a relative decline of ≥ 15% in Forced Vital Capacity (FVC)% as previously (3), using workflow.

Methods:

We investigated two cohorts of SSc-ILD: 90 patients (76.7% female, median age 57.5 years) from the University Hospital Zurich and 66 patients (75.8% female, median age 61.0 years) from Oslo University Hospital’s. Patients were retrospectively selected if (3): a) diagnosed with early/mild SSc according to the Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria, b) presence of ILD on HRCT as determined by a senior radiologist. For every subject, we defined 1,355 robust radiomic features from HRCT images. The follow-up period was defined as the time interval between baseline visit and the last available follow-up visit.

We have developed a systematic computational workflow to build predictive ML models. To reduce the number of redundant radiomic features, we applied correlation thresholds. We applied distinct methods including 1) Lasso Penalized Regression for feature selection, and 2) Random Forest (RF) for modeling using the R package ‘caret’. To select the optimal ML model, we randomly divided derivation cohort into Training (70%) and Holdout (30%) sets and applied fivefold cross-validation (5kCV) for feature and classifier selection on Training set only.

Results:

We have investigated various methods to select the optimal set of predictive radiomic features. Since the ML model performance is affected by both, feature, and classifier selection, we assessed these factors first.

Results from feature filtering and selection, suggested that the combination of correlation threshold of 0.9 with Lasso regression proved best. As we perform feature selection in 5k CV workflow, features present in at least 2 sets entered model optimization step.

During model selection, we selected RF classifier. We detected positive correlation between actual and predicted values with Spearman’s rho = 0.313, p = 0.167 and Spearman’s rho = 0.341, p = 0.015 in Oslo and Holdout sets respectively, as shown on Figure 1. The percentage of variance remained modest for both Holdout (Rsq = 0.104) and Oslo (Rsq = 0.126) datasets.

Figure 1.

Performance of the best, RF classifier shown as scatterplot between actual and predicted values of individual time to lung decline.

Conclusion:

In summary, we: (1) developed ML workflow that allowed to select o optimal methodology for modeling (i.e., feature and classifier selection), and (2) provide models that predicted time to individual lung function decline, characterized by significant correlation between predicted and actual values.

References:

[1]Hansell DM, Goldin JG, King TE, Jr., Lynch DA, Richeldi L, Wells AU. CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015;3(6):483-96.

[2]Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

[3]Schniering J. et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. https://www.medrxiv.org/content/10.1101/2020.06.09.20124800v1

Disclosure of Interests:

None declared

POS0866 TWO-DIMENSIONAL HRCT-BASED RADIOMIC FEATURES IN SSC-ILD DISTINGUISH DRUG RESPONDERS FROM NON-RESPONDERS

Background:

Management of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) is complicated by high inter-patient variability. To date, no validated predictors of treatment response are available for routine use. High resolution computed tomography (HRCT)-based radiomics, i.e. the high-dimensional, quantitative analysis of imaging metadata, have previously been shown to be successful in discriminating (SSc-)ILD phenotypes in preclinical and clinical studies1. Since HRCT is an integral part of the routine work-up in SSc, HRCT-based radiomic features may hold potential as non-invasive biomarkers.

Objectives:

To predict treatment response using two-dimensional (2D) HRCT-based radiomics in SSc-ILD patients from a prospectively followed cohort.

Methods:

Inclusion criteria were diagnosis of SSc-ILD in HRCT, availability of a suitable chest HRCT scan within 12 months prior to initiation of a new treatment, and availability of clinical baseline and follow-up information. Treatment response was defined as the absence of all of the following over a follow-up period of 12-24 months: relative decrease in forced vital capacity (FVC) ≥5%, increase of ILD in HRCT as assessed by a radiologist, change in treatment regimen due to insufficient response, ILD-related death or lung transplantation. Of each pre-treatment HRCT, 6 slices (15±5 mm apart, starting from the basal lung margin) were manually segmented and 1513 2D radiomic features were extracted using the in-house software Z-Rad (Python 2.7). Features were Z-score transformed and pre-filtered for inter- and intra-reader robustness (intraclass correlation coefficient >0.85) and inter-feature correlation (Spearman’s rho <0.9). A categorical linear regression model was created using 3-fold cross-validated elastic nets for feature selection. Features were then summarized and divided by their number. For generation of a score cut-off, Youden’s score was used. For two-group analyses of continuous variables, Wilcoxon’s test was performed, whereas categorical data was assessed using Fisher’s exact test.

Results:

A total of 64 pre-treatment HRCTs from 54 patients were analyzed. In 9 patients, >1 asynchronous treatments were assessed, while 45 patients had only 1 eligible treatment approach. The response rate within the assessed follow-up period was 45.3% (n=29). For score generation, 13 radiomic features were selected and an optimal cut-off value of -0.1589 was determined. Univariate linear regression showed significant association between our categorical radiomics-based score and treatment response (p=0.007, area under the curve = 0.65 (0.51-0.79), sensitivity=0.90, specificity=0.43), whereby a high score was predictive for treatment response.

No differences between patients with high (n=46) or low (n=18) scores were detected for baseline age (mean±SD=55.5±12.0 and 55.5±13.6 years, p=0.84), duration of SSc (mean±SD=6.2±8.4 and 4.7±4.4 years, p=0.79), time since ILD diagnosis (2.7±2.9 and 2.4±3.1 years, p=0.59), FVC (77.6±20.6 and 80.1±17.9, p=0.41) or DLco (54.4±21.0 and 57.6±18.9, p=0.40). Distribution of anti-Scl-70 positivity (45.7% vs. 55.6%, p=0.58) and diffuse cutaneous disease (47.7% vs. 61.1%, p=0.41) was not significantly different between patients with high and low scores, respectively, although a trend towards higher percentages in the high score group was observed.

Conclusion:

Our results indicate that, following validation in external cohorts, radiomics may be a promising tool for future pre-treatment patient stratification. Moreover, our radiomics-based score seems not to be associated with commonly studied clinical predictors such as anti-Scl-70 positivity or lung function, underlining a possible additive value to ‘traditional’ clinical parameters.

References:

[1]Schniering, J., et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. medRxiv [Preprint] doi:10.1101/2020.06.09.20124800 (2020).

Disclosure of Interests:

Chantal Meier: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Janine Schniering: None declared, Hubert Gabrys: None declared, Anja Kühnis: None declared, Oliver Distler Speakers bureau: Speaker fee on Scleroderma and related complications: Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche. Speaker fee on rheumatology topic other than Scleroderma: MSD, iQone, Novartis, Pfizer, Roche, Consultant of: Consultancy fee for Scleroderma and its complications: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Bayer, Baecon Discovery, Boehringer, CSL Behring, ChemomAb, Corbus Pharmaceuticals, Horizon Pharmaceuticals, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, Kymera, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Roivant Sciences, Sanofi, UCB. Consultancy fee for rheumatology topic other than Scleroderma: Abbvie, Amgen, Lilly, Pfizer, Grant/research support from: Research Grants to investigate the pathophysiology and potential treatment of Scleroderma and its complications: Kymera Therapeutics, Mitsubishi Tanabe, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Speaker fees from Boehringer-Ingelheim, Grant/research support from: Grant/research support from AbbVie, Protagen, Novartis Biomedical Research, congress support from Pfizer, Roche, Actelion, mepha, and MSD

FDG PET versus CT radiomics to predict outcome in malignant pleural mesothelioma patients

BACKGROUND

Careful selection of malignant pleural mesothelioma (MPM) patients for curative treatment is of highest importance, as the multimodal treatment regimen is challenging for patients and harbors a high risk of substantial toxicity. Radiomics-a quantitative method for image analysis-has shown its prognostic ability in different tumor entities and could therefore play an important role in optimizing patient selection for radical cancer treatment. So far, radiomics as a prognostic tool in MPM was not investigated.

MATERIALS AND METHODS

This study is based on 72 MPM patients treated with surgery in a curative intent at our institution between 2009 and 2017. Pre-treatment Fluorine-18 fluorodeoxyglucose (FDG) PET and CT scans were used for radiomics outcome modeling. After extraction of 1404 CT and 1410 FDG PET features from each image, a preselection by principal component analysis was performed to include only robust, non-redundant features for the cox regression to predict the progression-free survival (PFS) and the overall survival (OS). Results were validated on a separate cohort. Additionally, SUVmax and SUVmean, and volume were tested for their prognostic ability for PFS and OS.

RESULTS

For the PFS a concordance index (c-index) of 0.67 (95% CI 0.52-0.82) and 0.66 (95% CI 0.57-0.78) for the training cohort (n = 36) and internal validation cohort (n = 36), respectively, were obtained for the PET radiomics model. The PFS advantage of the low-risk group translated also into an OS advantage. On CT images, no radiomics model could be trained. SUV max and SUV mean were also not prognostic in terms of PFS and OS.

CONCLUSION

We were able to build a successful FDG PET radiomics model for the prediction of PFS in MPM. Radiomics could serve as a tool to aid clinical decision support systems for treatment of MPM in future.

SAT0569 “IMAGES ARE MORE THAN PICTURES, THEY ARE DATA” [1] – EXPLORATION OF RADIOMICS ANALYSIS FOR SYSTEMIC SCLEROSIS-ASSOCIATED INTERSTITIAL LUNG DISEASE

Background:

Interstitial lung disease (ILD) affects 60% of patients with systemic sclerosis (SSc) and is the primary cause of death. Medical imaging is an integral part of the routine work-up for diagnosis and monitoring of SSc-ILD and includes high-resolution computed tomography (HRCT). Radiomics is a novel research area that describes the in-depth analysis of tissue phenotypes in medical images with computational retrieval of quantitative, mineable metadata appropriate for statistical analyses.

Objectives:

To explore the performance of HRCT-derived radiomic features for the assessment of SSc-associated ILD (i.e. diagnosis, staging, and lung function).

Methods:

Radiomics analysis was performed on HRCT scans from 98 SSc patients, including n=33 SSc patients without ILD, n=33 with limited and n=32 with extensive ILD as defined by 0%, <20% and ≥20% visual extent of fibrosis on HRCT, respectively. Following semi-automated segmentation of lung tissue on 3D reconstructed HRCT scans, 1386 radiomic features, including 17 intensity, 137 texture, and 1232 wavelet features were extracted using the in-house developed software Z-Rad (Python 2.7). In order to identify robust features, we conducted intra- and inter-reader correlation analysis (ICC) in a subgroup of patients. Only features with good reproducibility (ICC ≥ 0.75) entered subsequent analyses. We applied the Wilcoxon test, followed by Receiver Operating Characteristic ROC) curve analyses, to identify features significantly different between a) ILD and non-ILD and b) limited vs. extensive ILD patients. Spearman rank correlation was performed to reveal significant associations of radiomic features from a) and b) with lung function as measured by percentage of predicted forced vital capacity (FVC% predicted).

Results:

In total, 1355/1386 radiomic features passed the test of robustness and were eligible for further, exploratory analyses. Radiomic features with good performance (area under the ROC curve (AUC) ≥ 0.7 and p-value ≤ 0.05) were considered as potential candidate discriminators. Under this criterion, we identified 288/1355 (21.3%) radiomic features that were significantly different between ILD and non-ILD patients and 409/1355 (30.2%) features that significantly discriminated between limited and extensive ILD (Fig. 1). For diagnosis, the texture featuredependence count entropywas the top parameter to distinguish ILD patients from healthy controls (AUC = 0.89, p = 1.83x10-10), whereas for staging the wavelet featureHHH long run high grey level emphasisproved to be best suited to separate limited from extensive ILD (AUC = 0.88, p = 7.76x10-9).

Fig 1.

Correlation analysis of the most significant (best performing) discriminative radiomic features with lung function revealed a significant negative correlation ofdependence count entropy(rho = -0.51, p = 9.89x10-8) andHHH long run high grey level emphasis(rho = -0.51, p = 1.73x10-5) with FVC% predicted.

Conclusion:

Our study adds novelty to the field of SSc-ILD showing that radiomic features have great potential as quantitative imaging biomarkers for diagnosis and staging of SSc-ILD and that they may reflect lung function. As the next step, we are planning to build predictive models, using machine learning, for diagnosis, staging, and lung function and validate them in external patient cohorts. If validated such models will pave the way for computer-aided management in SSc-ILD and thus improve patients’ outcome.

References:

[1]Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 278, 563-577, doi:10.1148/radiol.2015151169 (2016).

Disclosure of Interests:

Janine Schniering: None declared, Malgorzata Maciukiewicz: None declared, Hubert Gabrys: None declared, Matthias Brunner: None declared, Christian Blüthgen: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Matthias Guckenberger: None declared, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

P2270Clinical characteristics and outcomes after TAVI in patients reclassified to moderate aortic stenosis by integration of multimodality imaging and pressure recovery

Background

Accurate assessment of aortic stenosis (AS) severity is critical for the correct management of patients. This has become particularly important because the introduction of transcatheter aortic valve implantation (TAVI) has markedly increased the number of patients eligible for aortic valve replacement

Aims

To assess whether reclassification of aortic stenosis (AS) grading by integration of fusion imaging using data from transthoracic echocardiography (TTE) and multidetector computed tomography (MDCT) under consideration of the energy loss index (ELI) predicts outcome in patients undergoing transcatheter aortic valve implantation (TAVI).

Methods

197 consecutive patients with symptomatic severe AS undergoing TAVI at our University Heart Center were included in this study. AS severity was determined according to current guidelines.

Results

Left ventricular outflow tract (LVOT) area derived from TTE was smaller than the planimetric area in MDCT due the ovoid shape of the LVOT (3.4±0.12 cm2 vs. 4.5±0.23 cm2; p<0.01). The sinotubular junction (ST-junction) diameter measured in TTE was similar to maximal, minimal, and mean diameters determined by MDCT. The sphericity index confirmed an almost circular anatomy of ST-junction, and its area derived from TTE was similar to the planimetric area in MDCT. Fusion aortic valve area index (fusion AVAi) assessed by inserting MDCT derived planimetric LVOT area in the continuity equation was significantly higher in all patients compared to conventional AVAi. 62 patients were reclassified from severe to moderate AS because fusion AVAi was >0.6 cm2/m2. ELI was calculated for conventional AVAi and fusion AVAi each with ST-junction area determined by both TTE and MDCT. Calculating ELI with fusion AVAi resulted in significantly larger effective orifice area, with values >0.6 cm2/m2 in 83 patients (ST-junction area from echo) and 85 patients (ST-junction area from MDCT). Similarly, calculating ELI with conventional AVAi resulted in significantly larger effective orifice area as compared to AVAi alone. Reclassified patients had lower mean transvalvular pressure gradients, lower myocardial mass, less symptoms according to NYHA classification, and lower proBNP levels at baseline. While both groups exhibited improvement of functional status at 1 year of follow-up, the survival rate at 3 years after TAVI was higher in patients reclassified to moderate AS (81% versus 66%; p=0.02).

Conclusion

Integration of TTE and MDCT derived values for calculation of ELI reclassifies the severity of AS in 43% of patients initially diagnosed with severe AS.Although reclassified patients display less advanced valve disease at baseline, TAVI results in functional improvement in all patients. Furthermore, patients reclassified to moderate AS exhibit higher survival rates at 3 years after aortic valve replacement.

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study

OBJECTIVE

To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients.

MATERIALS AND METHODS

In this prospective, observational study, patients with previously diagnosed TB were enrolled. Photographs of their CXRs were taken using a consumer-grade digital still camera. The images were stratified by pathological patterns into classes: cavity, consolidation, effusion, interstitial changes, miliary pattern or normal examination. Image analysis was performed with commercially available Deep Learning software in two steps. Pathological areas were first localised; detected areas were then classified. Detection was assessed using receiver operating characteristics (ROC) analysis, and classification using a confusion matrix.

RESULTS

The study cohort was 138 patients with human immunodeficiency virus (HIV) and TB co-infection (median age 34 years, IQR 28-40); 54 patients were female. Localisation of pathological areas was excellent (area under the ROC curve 0.82). The software could perfectly distinguish pleural effusions from intraparenchymal changes. The most frequent misclassifications were consolidations as cavitations, and miliary patterns as interstitial patterns (and vice versa).

CONCLUSION

Deep Learning analysis of CXR photographs is a promising tool. Further efforts are needed to build larger, high-quality data sets to achieve better diagnostic performance.

Volumetric dynamic oxygen-enhanced MRI (OE-MRI): comparison with CT Brody score and lung function in cystic fibrosis patients

OBJECTIVES

To demonstrate, in patients with cystic fibrosis (CF), the correlation between three-dimensional dynamic oxygen-enhanced magnetic resonance imaging (OE-MRI) measurements and computed tomography Brody score (CF-CT) and lung function testing (LFT).

METHODS

Twenty-one patients (median age, 25 years; female, n = 8) with a range of CF lung disease and five healthy volunteers (median age, 31 years; female, n = 2) underwent OE-MRI performed on a 1.5-T MRI scanner. Coronal volumes were acquired while patients alternately breathed room air and 100% oxygen. Pre-oxygen T₁ was measured. Dynamic series of T₁-weighted volumes were then obtained while breathing oxygen. T₁-parameter maps were generated and the following OE-MRI parameters were measured: oxygen uptake (ΔPO_2max), wash-in time and wash-out time. High-resolution CT and LFT were performed. The relationship between CF-CT, LFT and OE-MRI parameters were evaluated using Pearson correlation for the whole lung and regionally.

RESULTS

Mean CF-CT was 24.1±17.1. Mean ΔPO_2max and mean wash-in as well as skewness of wash-out showed significant correlation with CF-CT (ΔPO_2max: r = -0.741, p < 0.001; mean wash-in: r = 0.501, p = 0.017; skewness of wash-out: r = 0.597, p = 0.001). There was significant correlation for the whole lung and regionally between LFT parameters and OE-MR (ΔPO_2max: r = 0.718, p < 0.001; wash-in: r = -0.576, p = 0.003; wash-out skewness: r = -0.552, p = 0.004).

CONCLUSIONS

Functional lung imaging using OE-MRI has the capability to assess the severity of CF lung disease and shows a significant correlation with LFT and CF-CT.

KEY POINTS

• Oxygen-enhanced MRI might play a future role in evaluation and follow-up of cystic fibrosis. • Heterogeneity of parameter maps reflects localised functional impairment in cystic fibrosis. • Avoidance of cumulative radiation burden in CF is feasible using OE-MRI.

Water-fat Dixon sequences in the evaluation of breast implants: proposal of a time effective rapid approach in the clinical practice

AIM

To evaluate the diagnostic accuracy achieved from a fat-water Dixon sequence alone compared to a combination of a silicone-specific magnetic resonance imaging (MRI) sequence and a water-specific MRI sequence in the assessment of breast implants.

MATERIALS AND METHODS

In this institutional review board (IRB)-approved study the integrity of breast implants was assessed retrospectively in 27 patients undergoing breast MRI at 3 T. A qualitative evaluation of (set 1) a silicon-selective water-saturated short tau inversion recovery (STIR) sequence in combination with a water-only Dixon dataset (total acquisition time 7 minutes 17 seconds), and of (set 2) fat-only and water-only Dixon datasets (4 minutes 8 seconds) was performed by two readers independently evaluating the following features: margin definition of the implant, water suppression homogeneity, image quality, presence of artefacts and their effects on the imaging interpretation, and diagnostic confidence. Diagnostic accuracy in implant rupture detection was determined and either surgical confirmation or diagnosis from the radiological report was used as a standard of reference.

RESULTS

In both sequences, margin definition of the implant wall, water suppression homogeneity, and overall image quality were rated good-excellent in most of cases. Water suppression homogeneity was moderate-poor in a greater number of cases in set 1. Movement artefacts were more frequent in set 1 whereas five cases (18.5%) exhibited swap artefacts between silicone and water in set 2. Diagnostic confidence was rated high-very high with both sequences in most of cases. Diagnostic accuracy was 100% for both readers using set 1 and 96.2% and 100% using set 2.

CONCLUSION

A single Dixon sequence allows an accurate diagnostic evaluation of breast implants and concomitant shortening of the overall acquisition time.

Prospective Randomized Comparison of High-pitch CT at 80 kVp Under Free Breathing with Standard-pitch CT at 100 kVp Under Breath-Hold for Detection of Pulmonary Embolism

RATIONALE AND OBJECTIVES

To prospectively compare high-pitch computed tomography (HPCT) under free breathing (FB) with standard-pitch CT (SPCT) under breath-hold (BH) for detection of pulmonary embolism (PE).

MATERIALS AND METHODS

One hundred consecutive patients (47 females; mean age 58.7 ± 16.6) randomly underwent HPCT-FB (n = 50) or SPCT-BH (n = 50). Radiation doses were documented. One reader measured pulmonary artery attenuation and noise; mean signal-to-noise ratio (SNR) was calculated. Two readers assessed image quality, diagnostic confidence for detection of PE, motion artifacts, assessability of anatomical structures, and presence of transient interruption of contrast as sign of Valsalva maneuver. Inter-reader agreement was calculated.

RESULTS

Radiation dose was significantly lower in HPCT compared to SPCT (2.68 ± 0.60 mGy vs 6.01 ± 2.26 mGy; P < .001). Mean pulmonary artery attenuation and image noise were significantly higher in HPCT (attenuation: 479 Hounsfield unit (HU) vs 343HU; P < .001; noise: 16 HU vs 10 HU; P < .001) whereas SNR was similar between groups (34 HU vs 38 HU; P = .258). HPCT had significantly higher diagnostic confidence for PE detection (P = .048), less cardiac and breathing artifacts (P < .001), better assessability of anatomical structures, and fewer cases of transient interruption of contrast (P < .001) compared to the SPCT.

CONCLUSIONS

HPCT-FB allows for a significant reduction of breathing and motion artifacts compared to SPCT-BH. Diagnostic confidence, assessability of vascular and bronchial structures, as well as SNR are maintained.

Easy Pulsatile Phantom for Teaching and Validation of Flow Measurements in Ultrasound

PURPOSE

To build a simple model to teach and validate non-pulsatile and pulsatile flow quantification in ultrasound.

MATERIALS AND METHODS

The setting consists of the following connected components: (1) medical syringe pump producing an adjustable constant flow (ml/min), (2) modulator modifying constant flow to a reproducible pulsatile flow, (3) water tank containing a diagonal running silicone tube (0.5 mm inner diameter), and (4) a fixated ultrasound probe (L9 Linear Array 9 MHz, GE Logiq E9) measuring the flow inside the tube. Commercially available microbubbles suspended with physiological saline solution were used for ultrasonic visibility. Spectral Doppler of different flow profiles is performed.

RESULTS

The syringe pump produces an adjustable, constant flow and serves as the reference standard. The filling volume of the tube system is 1.2 ml. Microbubbles are very well detected by ultrasound and can be used as an easy and clean blood mimicking substance. The modulator generates different physiological and pathological flow profiles. Velocities are similar to those found within human blood vessels. Thus, it is possible to train and validate flow measurements in ultrasound.

CONCLUSION

The model produces non-pulsatile and various pulsatile flow profiles and allows validation of flow measurements. The compact size permits easy and economic setup for flow measurements in research, skills lab and continuing education.

Whole-body CT-based imaging algorithm for multiple trauma patients: radiation dose and time to diagnosis

OBJECTIVE

To determine the number of imaging examinations, radiation dose and the time to complete trauma-related imaging in multiple trauma patients before and after introduction of whole-body CT (WBCT) into early trauma care.

METHODS

120 consecutive patients before and 120 patients after introduction of WBCT into the trauma algorithm of the University Hospital Zurich were compared regarding the number and type of CT, radiography, focused assessment with sonography for trauma (FAST), additional CT examinations (defined as CT of the same body regions after radiography and/or FAST) and the time to complete trauma-related imaging.

RESULTS

In the WBCT cohort, significantly more patients underwent CT of the head, neck, chest and abdomen (p < 0.001) than in the non-WBCT cohort, whereas the number of radiographic examinations of the cervical spine, chest and pelvis and of FAST examinations were significantly lower (p < 0.001). There were no significant differences between cohorts regarding the number of radiographic examinations of the upper (p = 0.56) and lower extremities (p = 0.30). We found significantly higher effective doses in the WBCT (29.5 mSv) than in the non-WBCT cohort (15.9 mSv; p < 0.001), but fewer additional CT examinations for completing the work-up were needed in the WBCT cohort (p < 0.001). The time to complete trauma-related imaging was significantly shorter in the WBCT (12 min) than in the non-WBCT cohort (75 min; p < 0.001).

CONCLUSION

Including WBCT in the initial work-up of trauma patients results in higher radiation doses, but fewer additional CT examinations are needed, and the time for completing trauma-related imaging is shorter.

ADVANCES IN KNOWLEDGE

WBCT in trauma patients is associated with a high radiation dose of 29.5 mSv.

[Pulmonary complications in chemotherapy]

Viele Chemotherapeutika wirken direkt oder indirekt toxisch auf die Lunge und Atemwege. Dies kann zu potenziell tödlichen Nebenwirkungen führen. Die diagnostische Herausforderung liegt darin, die Veränderungen in der Bildgebung als solche frühzeitig zu erkennen, da sie oft unspezifisch sind. Zudem können sie mit pulmonaler Stauung, Infektionen, Strahlungspneumonitis oder einer primären Grunderkrankung verwechselt werden. In diesem Übersichtsartikel werden die morphologischen Veränderungen im Bereich der Lunge und der Atemwege, eingeteilt nach anatomischer Lokalisation, dargestellt und einige häufig verwendete Chemotherapeutika und ihre Nebenwirkungen aufgeführt.

Semiautomatic superimposition improves radiological assessment of curve flexibility in scoliosis

OBJECTIVE

Assessment of scoliotic curve flexibility and stiffness is essential for planning surgical treatment in adolescent idiopathic scoliosis (AIS). Measurement of curve flexibility is currently insufficiently precise. The purpose of this study was to introduce and validate a novel method of superimposing radiographs for more reliable measurement of curve flexibility.

MATERIAL AND METHODS

Two independent radiologists measured Cobb angles separately on standard anterior-posterior (AP) (n = 48) and supine bending radiographs (n = 48), in patients with AIS, who were randomly included from a surgical database. The same readers repeated the measurements after the bending radiographs were semi-automatically superimposed on the AP radiographs by fusing the caudad end vertebra. Curve flexibility was calculated. Inter-reader agreement between the two independent readers was calculated using interclass correlation coefficient (ICC).

RESULTS

A moderate inter-reader agreement was achieved in the upper curve (ICC = 0.57) and a good agreement in the lower curve (ICC = 0.72) with the standard method of assessing curve flexibility. With the use of the semiautomatic superimposition, however, almost perfect agreement was achieved for both the upper and the lower curves flexibilities (ICC = 0.93 and 0.97, respectively).

CONCLUSION

The introduced semi-automatic superimposition technique for measurement of scoliotic curve flexibility in AIS is more precise and reliable than the current standard method.

Volumetry: an alternative to assess therapy response for malignant pleural mesothelioma?

The purpose of our study was to assess robustness of volumetric measurement of malignant pleural mesothelioma (MPM) before and after chemotherapy to modified RECIST (response evaluation criteria in solid tumours) criteria. 30 patients with digitally available chest computed tomography (CT) scans before and after three cycles of chemotherapy were included. Three readers independently assessed tumour response using two different methods: 1) the modified RECIST criteria; and 2) the tumour volumetric approach using dedicated software (Myrian; Intrasense, Paris, France). Inter-rater reliability of unidimensional and volumetric measurements was assessed using intraclass correlation. Tumour response classification for modified RECIST was compared to the volumetric approach applying unidimensional RECIST volumetric equivalent criteria. The determination of unidimensional tumour measurement (RECIST) revealed a low inter-rater reliability (0.55) and a low interobserver agreement for tumour response classification (general κ 0.33). Only 14 patients were classified equally. A high inter-rater reliability (0.99) and interobserver agreement (general κ 0.9) were found for absolute tumour volumes (volumetric measurements). 27 cases were classified equally. The number of cases classified as "stable disease" was higher for the volumetric approach using tumour-equivalent criteria compared to modified RECIST. Volumetric measurement of MPM on CT using Myrian software is a reliable, reproducible and sensitive method to measure tumour volume and, thus, therapy response after induction chemotherapy.

Unenhanced versus multiphase MDCT in patients with hematuria, flank pain, and a negative ultrasound

OBJECTIVES

To compare the impact of unenhanced and contrast-enhanced multi-detector computed tomography (MDCT) for the detection of urinary stones and urinary obstruction in patients with suspected renal colic.

METHODS

95 patients with suspected renal colic underwent a three-phase MDCT for evaluation of the urinary tract. The unenhanced scan and the multiphase examination were reviewed retrospectively by two radiologists for the characterization of urinary stones and signs of obstruction. Results of unenhanced MDCT were compared with those obtained during the second review of the entire multiphase examination.

RESULTS

Overall diagnosis of urinary stones revealed an accuracy of 97.0% for unenhanced, and 98.9% for multiphase MDCT with a significant difference between both protocols (mixed-effects logistic regression: odds ratio 3.3; p=0.019). With 3 versus 15 false positive ratings, multiphase MDCT was superior to unenhanced MDCT for the diagnosis of urinary stones. There was no significant difference in detecting signs of obstruction. Inter-reader agreement for overall stone detection was excellent on both unenhanced (kappa 0.84) and multiphase (kappa 0.88) MDCT.

CONCLUSION

Contrast-enhanced multiphase MDCT offers distinct advantages compared to an unenhanced approach for the assessment of urinary stone disease, and therefore should be considered as a complementary examination for patients with inconclusive findings.

[Use of multislice CT in the evaluation of patients with acute chest pain]

Acute chest pain is a common symptom but triage decisions in these patients remain a challenge. Patient's history, cardiac enzyme levels, or electrocardiograms often are unspecific. Nowadays, multidetector-row computed tomography (CT) currently represents the imaging modality of choice for diagnosing or excluding pulmonary embolism (PE) or acute aortic syndrome (AAS). Furthermore, recent studies have demonstrated advantages for non-invasive imaging of the coronary arteries by CT. The so called triple rule-out CT allows the depiction of the pulmonary arteries, the thoracic aorta, and the coronary arteries within a single examination with a simultaneous attenuation of these three vessel territories. This enables the detection of life-threatening such as PE, AAS and ACS, as well as of non-life-threatening diseases causing acute chest pain.

CT- and MRI-based volumetry of resected liver specimen: comparison to intraoperative volume and weight measurements and calculation of conversion factors

OBJECTIVE

To compare virtual volume to intraoperative volume and weight measurements of resected liver specimen and calculate appropriate conversion factors to reach better correlation.

METHODS

Preoperative (CT-group, n=30; MRI-group, n=30) and postoperative MRI (n=60) imaging was performed in 60 patients undergoing partial liver resection. Intraoperative volume and weight of the resected liver specimen was measured. Virtual volume measurements were performed by two readers (R1,R2) using dedicated software. Conversion factors were calculated.

RESULTS

Mean intraoperative resection weight/volume: CT: 855 g/852 mL; MRI: 872 g/860 mL. Virtual resection volume: CT: 960 mL(R1), 982 mL(R2); MRI: 1112 mL(R1), 1115 mL(R2). Strong positive correlation for both readers between intraoperative and virtual measurements, mean of both readers: CT: R=0.88(volume), R=0.89(weight); MRI: R=0.95(volume), R=0.92(weight). Conversion factors: 0.85(CT), 0.78(MRI).

CONCLUSION

CT- or MRI-based volumetry of resected liver specimen is accurate and recommended for preoperative planning. A conversion of the result is necessary to improve intraoperative and virtual measurement correlation. We found 0.85 for CT- and 0.78 for MRI-based volumetry the most appropriate conversion factors.

Interactive visuo-haptic surgical planning tool for pelvic and acetabular fractures

Treatment of pelvic and acetabular fractures still poses a major challenge to trauma surgeons. We present a tool for intervention planning for such injuries using patient-specific models built from Computed Tomography data. The presented tool has three main parts: (1) the virtual reduction of the bone fragments, (2) the virtual adaptation of the osteosynthesis implants and (3) Finite Element Analysis (FEA) for testing mechanical behavior of the resulting intervention plan. Our tool provides an intuitive visuo-hapic interface designed to be used by trauma surgeons. The type and size of the osteosynthesis material can be determined and measurements like distances and angles relative to landmarks can be taken. First results of prospectively planned interventions show an excellent correlation and a significant gain in operation time.

[Simulation of blood flow within the abdominal aorta. Computational fluid dynamics in abdominal aortic aneurysms before and after interventions]

The goal of numeric analysis of aortic blood flow is to evaluate the mechanisms leading to an aortic aneurysm with regard to the risk of a rupture and to describe the effect of interventional therapy. Numeric analysis is based on virtual models of vascular structures and the physical characteristics of the vessel wall, of blood as fluidum, and the blood flow. Using this information, numeric analysis solves the appropriate equations. The results can be displayed quantitatively and qualitatively. The results of numeric flow simulation show that in abdominal aortic aneurysms the wall pressure, which is of vital importance for the risk of rupture, depends on several factors, one being the location of the intraluminal thrombus. In models of aneurysms after stent grafting, numeric analysis can be used to evaluate factors leading to stent migration. Although numeric analysis of aortic blood flow still has several limitations, recent studies have shown that this method has the potential for improved estimation of the rupture risk of aortic aneurysms in the near future.

State of the art 3D imaging of abdominal organs

Since its introduction in the late 70's 3D imaging has assumed growing importance for the visualization of abdominal pathologies. Main clinical applications include the preoperative planning for resection of liver, pancreas and kidney tumors, living-donor liver and renal transplantation, and tumor radiofrequency ablation of liver and kidney. The advantages of 3D imaging are the exact visualization of vessels in areas with complex and variable vascular anatomy, demonstration of possible resection margins, and predicting operative risks, especially when dealing with complex or minimal invasive surgery. 3D imaging is an important visual aid for the surgeon in order to facilitate crucial decisions and to lower the operative risk. In future, 3D planning and simulation of operations will be become standard procedures. Therefore an efficient use of 3D imaging is not possible without a close collaboration between radiologist and surgeons.

Adenine arabinoside in the management of herpetic keratitis

Ara-A is an effective antiviral agent in the treatment of herpetic ulceration. Its use leads to a quick and high cure rate, and clinical resistance is low. It can successfully be used in cases showing complications after treatment with other antiviral agents.