Multiple breath washout: A new and promising lung function test for patients with idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is a devastating progressive lung disease affecting the parenchyma. Nitrogen multiple-breath washout (N2 -MBW) is a lung function test that measures ventilation inhomogeneity, a biomarker of small airway disease. We assessed clinical properties of N2 -MBW in IPF. METHODS: In this prospective cohort pilot study, 25 IPF patients and 25 healthy controls were assessed at baseline and 10 patients at median 6.2 months later. Outcomes included the lung clearance index (LCI) from N2 -MBW, forced vital capacity (FVC) from spirometry, diffusion capacity of the lungs for carbon monoxide (DLCO ), bronchiectasis score from computed tomography scans, the Gender-Age-Physiology (GAP score for IPF) stage and death or lung transplantation (LTx). Study end points were feasibility, repeatability, discriminative capacity and correlation with disease severity and structural lung damage. RESULTS: All patients were able to perform N2 -MBW. LCI was repeatable and reproducible. Median (interquartile range (IQR)) LCI in IPF was 11.6 (10.1-13.8) in IPF versus 7.3 (6.9-8.4) in controls (P < 0.0001). LCI correlated with DLCO corrected for haemoglobin (corrDLCO ; r = -0.49, P = 0.016), bronchiectasis score (r = 0.45, P = 0.024) and the GAP stage (r = 0.59, P = 0.002), but not with FVC. FVC was not related to bronchiectasis. During follow-up, six patients died and one received LTx. LCI correlated with the latter compound outcome: hazard ratio (95% CI) was 2.43 (1.26; 4.69) per one LCI SD from the patient population. CONCLUSION: N2 -MBW is a feasible, reliable and valid lung function test in IPF. LCI correlates with diffusion impairment, structural airway damage and clinical disease severity. LCI is a promising surveillance tool in IPF that may predict mortality.

Outcomes of Endobronchial Valve Treatment Based on the Precise Criteria of an Endobronchial Catheter for Detection of Collateral Ventilation under Spontaneous Breathing.

BACKGROUND: Endoscopic lung volume reduction with valves is a valid therapeutic option for COPD patients with severe emphysema. The exclusion of interlobar collateral ventilation (CV) is an important predictor of clinical success. OBJECTIVES: Recently, a catheter-based endobronchial in vivo measurement system (Chartis, Pulmonx, USA) has become routine in the clinical evaluation of CV status in target lobes, but the criteria for phenotyping CV by Chartis evaluation have not yet been defined. We asked the questions, how many phenotypes can be identified using Chartis, what are the exact criteria to distinguish them, and how do the Chartis phenotypes respond to valve insertion? METHODS: In a retrospective study, 406 Chartis assessments of 166 patients with severe COPD were analyzed. Four Chartis phenotypes, CV positive (CV+), CV negative (CV-), low flow (LF) and low plateau were identified. Fifty-two patients without CV were treated with valves and followed for 3 months. RESULTS: The Chartis phenotypes were discriminated with respect to decline in expiratory peak flow, increase in resistance index and change in total exhaled volume after 1, 2, 3, 4 and 5 min of measurement time (p < 0.0001, ANOVA), and the cutoff criteria were defined accordingly. To examine the application of these phenotyping criteria, students applied them to 100 Chartis assessments, and they demonstrated almost perfect inter- and intraobserver agreements (x03BA; > 0.9). Compared to baseline, CV- and LF patients with ipsilateral CV- lobe showed an improvement in FEV1 (p < 0.05), vital capacity (p < 0.05) and target lobe volume reduction (p < 0.005) after valve insertion. CONCLUSION: This study describes the most prevalent Chartis phenotypes.

Graph Node Based Interpretability Guided Sample Selection for Active Learning.

While supervised learning techniques have demonstrated state-of-the-art performance in many medical image analysis tasks, the role of sample selection is important. Selecting the most informative samples contributes to the system attaining optimum performance with minimum labeled samples, which translates to fewer expert interventions and cost. Active Learning (AL) methods for informative sample selection are effective in boosting performance of computer aided diagnosis systems when limited labels are available. Conventional approaches to AL have mostly focused on the single label setting where a sample has only one disease label from the set of possible labels. These approaches do not perform optimally in the multi-label setting where a sample can have multiple disease labels (e.g. in chest X-ray images). In this paper we propose a novel sample selection approach based on graph analysis to identify informative samples in a multi-label setting. For every analyzed sample, each class label is denoted as a separate node of a graph. Building on findings from interpretability of deep learning models, edge interactions in this graph characterize similarity between corresponding interpretability saliency map model encodings. We explore different types of graph aggregation to identify informative samples for active learning. We apply our method to public chest X-ray and medical image datasets, and report improved results over state-of-the-art AL techniques in terms of model performance, learning rates, and robustness.

A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans.

OBJECTIVES: The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. MATERIALS AND METHODS: The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. RESULTS: AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. CONCLUSIONS: A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI.

PURPOSE: To evaluate a fully automatic computer-assisted diagnosis (CAD) method for breast magnetic resonance imaging (MRI), which considered dynamic as well as morphologic parameters and linked those to descriptions laid down in the Breast Imaging Reporting and Data System (BI-RADS) MRI atlas. MATERIALS AND METHODS: MR images of 108 patients with 141 histologically proven mass-like lesions (88 malignant, 53 benign) were included. The CAD system automatically performed the following processing steps: 3D nonrigid motion correction, detection of lesions by a segmentation algorithm, extraction of multiple dynamic and morphologic parameters, and classification of lesions. As one final result, the lesions were categorized by defining their probability of malignancy; this so-called morpho-dynamic index (MDI) ranged from 0%-100%. The results of the CAD system were correlated with histopathologic findings. RESULTS: The CAD system had a high detection rate of the histologically proven lesions, missing only two malignancies of invasive multifocal carcinomas and four benign lesions (three fibroadenomas, one atypical ductal hyperplasia). The 86 detected malignant lesions showed a mean MDI of 86.1% (± 15.4%); the mean MDI of the 49 coded benign lesions was 41.8% (± 22.0%; P < 0.001). Based on receiver-operating characteristic analysis, the diagnostic accuracy of the CAD system was 93.5%. Using an appropriate cutoff value (MDI 50%), sensitivity was 96.5% combined with specificity of 75.5%. CONCLUSION: The fully automatic CAD technique seems to reliably distinguish between benign and malignant mass-like breast tumors. Observer-independent CAD may be a promising additional tool for the interpretation of breast MRI in the clinical routine.

Hepatocellular carcinoma: computed-tomography-guided high-dose-rate brachytherapy (CT-HDRBT) ablation of large (5-7 cm) and very large (>7 cm) tumours.

OBJECTIVES: Evaluate the clinical outcome of CT-guided high-dose-rate-brachytherapy (CT-HDRBT) of hepatocellular carcinoma (HCC) larger than 5 cm in diameter with the goal of local tumour control (LTC). METHODS: Thirty-five patients with 35 unresectable HCCs ranging in size from 5 to 12 cm (mean: 7.1 cm) were treated with CT-HDRBT. Tumours were classified into two groups according to diameter: "large lesions" (5-7 cm) and "very large lesions" (>7 cm). Tumour response was evaluated by Gd-EOB-DTPA-enhanced liver magnetic resonance imaging (MRI) performed before, 6 weeks after, and then every 3 months after treatment. Endpoints included local tumour control (LTC), progression-free survival (PFS) and overall survival (OS). RESULTS: Nineteen tumours were classified as "large" and 16 as "very large". Complete tumour enclosure was achieved in all patients after the first CT-HDRBT session. Five patients were lost to follow-up. At a mean follow-up of 12.8 months, two patients had local progression (6.7%), one in each group. Nine patients (30%) experienced distant progression, five (26.3%) in the "large" and four (25%) in the "very large" group. No patients died during the follow-up period. No major complications were recorded. CONCLUSIONS: CT-HDRBT is a promising therapy for HCCs that exceed indications for thermal ablation. KEY POINTS: • Computed Tomography guided high-dose-rate brachytherapy offers new therapeutic options for hepatocellular carcinoma • CT-HDRBT can be safely practised in HCCs exceeding 5 cm in diameter • CT-HDRBT offers high rate of local control where thermal ablation is impossible • CT-HDRBT could be a valid alternative to TACE for intermediate stage HCC.

Percutaneous computed tomography-guided high-dose-rate brachytherapy ablation of breast cancer liver metastases: initial experience with 80 lesions.

PURPOSE: To analyze initial experience with computed tomography-guided high-dose-rate brachytherapy (CT-HDRBT) ablation of breast cancer liver metastases (BCLM). MATERIALS AND METHODS: Between January 2008 and December 2010, 37 consecutive women with 80 liver metastases were treated with CT-HDRBT in 56 sessions. Mean age was 58.6 years (range, 34-83 y). Treatment was performed by CT-guided applicator placement and high-dose-rate brachytherapy with an iridium-192 source. The mean radiation dose was 18.57 Gy (standard deviation 2.27). Tumor response was evaluated by gadoxetic acid-enhanced liver magnetic resonance (MR) imaging performed before treatment, 6 weeks after treatment, and every 3 months thereafter. RESULTS: Two patients were lost to follow-up; the remaining 35 patients were available for MR imaging evaluation for a mean follow-up time of 11.6 months (range 3-32 mo). Mean tumor diameter was 25.5 mm (range 8-74 mm). Two (2.6%) local recurrences were observed after local tumor control for 10 months and 12 months. Both local progressions were successfully retreated. Distant tumor progression (new metastases or enlargement of nontreated metastases) occurred during the follow-up period in 11 (31.4%) patients. Seven (20%) patients died during the follow-up period. Overall survival ranged from 3-39 months (median 18 months). CONCLUSIONS: CT-HDRBT is a safe and effective ablative therapy, providing a high rate of local tumor control in patients with BCLM.

Interpretability-Guided Inductive Bias For Deep Learning Based Medical Image.

Deep learning methods provide state of the art performance for supervised learning based medical image analysis. However it is essential that trained models extract clinically relevant features for downstream tasks as, otherwise, shortcut learning and generalization issues can occur. Furthermore in the medical field, trustability and transparency of current deep learning systems is a much desired property. In this paper we propose an interpretability-guided inductive bias approach enforcing that learned features yield more distinctive and spatially consistent saliency maps for different class labels of trained models, leading to improved model performance. We achieve our objectives by incorporating a class-distinctiveness loss and a spatial-consistency regularization loss term. Experimental results for medical image classification and segmentation tasks show our proposed approach outperforms conventional methods, while yielding saliency maps in higher agreement with clinical experts. Additionally, we show how information from unlabeled images can be used to further boost performance. In summary, the proposed approach is modular, applicable to existing network architectures used for medical imaging applications, and yields improved learning rates, model robustness, and model interpretability.

Computer-aided diagnosis through medical image retrieval in radiology.

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability.

The Octopus Sign-A New HRCT Sign in Pulmonary Langerhans Cell Histiocytosis.

Background: Fibrosis in pulmonary Langerhans cell histiocytosis (PLCH) histologically comprises a central scar with septal strands and associated airspace enlargement that produce an octopus-like appearance. The purpose of this study was to identify the octopus sign on high-resolution computed tomography (HRCT) images to determine its frequency and distribution across stages of the disease. Methods: Fifty-seven patients with confirmed PLCH were included. Two experienced chest radiologists assessed disease stages as early, intermediate, or late, as well as the lung parenchyma for nodular, cystic, or fibrotic changes and for the presence of the octopus sign. Statistical analysis included Cohen's kappa for interrater agreement and Fisher's exact test for the frequency of the octopus sign. Results: Interobserver agreement was substantial for the octopus sign (kappa = 0.747). Significant differences in distribution of the octopus sign between stages 2 and 3 were found with more frequent octopus signs in stage 2 and fewer in stage 3. In addition, we only found the octopus sign in cases of nodular und cystic lung disease. Conclusions: The octopus sign in PLCH can be identified not only on histological images, but also on HRCT images. Its radiological presence seems to depend on the stage of PLCH.

Breast cancer: early- and late-fluorescence near-infrared imaging with indocyanine green--a preliminary study.

PURPOSE: To assess early- and late-fluorescence near-infrared imaging, corresponding to the vascular (early-fluorescence) and extravascular (late-fluorescence) phases of indocyanine green (ICG) enhancement, for breast cancer detection and benign versus malignant breast lesion differentiation. MATERIALS AND METHODS: The study was approved by the ethical review board; all participants provided written informed consent. Twenty women with 21 breast lesions were examined with near-infrared imaging before, during, and after intravenous injection of ICG. Absorption and fluorescence projection mammograms were recorded simultaneously on a prototype near-infrared imaging unit. Two blinded readers independently assessed the images and assigned visibility scores to lesions seen on the absorption and absorption-corrected fluorescence mammograms. Imaging results were compared with histopathologic findings. Lesion contrast and diameter on the fluorescence mammograms were measured, and Cohen κ, Mann-Whitney U, and Spearman ρ tests were conducted. RESULTS: The absorption-corrected fluorescence ratio mammograms showed high contrast (contrast value range, 0.25-0.64) between tumors and surrounding breast tissue. Malignant lesions were correctly defined in 11 (reader 1) and 12 (reader 2) of 13 cases, and benign lesions were correctly defined in six (reader 1) and five (reader 2) of eight cases with late-fluorescence imaging. Lesion visibility scores for malignant and benign lesions were significantly different on the fluorescence ratio mammograms (P = .003) but not on the absorption mammograms (P = .206). Mean sensitivity and specificity reached 92% ± 8 (standard error of mean) and 75% ± 16, respectively, for fluorescence ratio imaging compared with 100% ± 0 and 25% ± 16, respectively, for conventional mammography alone. CONCLUSION: Preliminary data suggest that early- and late-fluorescence ratio imaging after ICG administration can be used to distinguish malignant from benign breast lesions.

Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation.

In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and with minimal expert interactions (e.g. label querying in an active learning setup). In this article we propose a novel sample selection methodology based on deep features leveraging information contained in interpretability saliency maps. In the absence of ground truth labels for informative samples, we use a novel self supervised learning based approach for training a classifier that learns to identify the most informative sample in a given batch of images. We demonstrate the benefits of the proposed approach, termed Interpretability-Driven Sample Selection (IDEAL), in an active learning setup aimed at lung disease classification and histopathology image segmentation. We analyze three different approaches to determine sample informativeness from interpretability saliency maps: (i) an observational model stemming from findings on previous uncertainty-based sample selection approaches, (ii) a radiomics-based model, and (iii) a novel data-driven self-supervised approach. We compare IDEAL to other baselines using the publicly available NIH chest X-ray dataset for lung disease classification, and a public histopathology segmentation dataset (GLaS), demonstrating the potential of using interpretability information for sample selection in active learning systems. Results show our proposed self supervised approach outperforms other approaches in selecting informative samples leading to state of the art performance with fewer samples.

Uterine artery embolization for leiomyomas: percentage of infarction predicts clinical outcome.

PURPOSE: To determine the effect of partial versus complete leiomyoma infarction on relief of leiomyoma-related symptoms and freedom from invasive reinterventions and to assess if patient age, location of the dominant leiomyoma, number of leiomyomas, or baseline uterine and dominant leiomyoma volume were associated with clinical failure. MATERIALS AND METHODS: Study protocol was approved by the institutional review board, and informed consent was obtained. One hundred fifteen consecutive women (median age, 42 years; range, 34-61 years) with symptomatic uterine leiomyomas underwent contrast material-enhanced magnetic resonance (MR) imaging at baseline and 24-72 hours after uterine artery embolization (UAE) to determine the percentage of infarction of leiomyoma tissue (complete = 100%, almost complete = 90%-99%, and partial = 0%-89%). Clinical outcome and frequency of reinterventions were compared for up to 36 months. RESULTS: One hundred thirteen patients completed at least one clinical follow-up. Twenty-four months after UAE, 50% +/- 15.2 (standard error) of the patients with partial infarction and 80% +/- 13.4 (standard error) of patients with almost complete infarction had undergone no reintervention. No patient with complete infarction needed a second treatment (P < .001). The hazard ratios for reintervention between the complete infarction group and the almost complete and partial infarction groups were 15.88 (95% confidence interval [CI]: 1.22, 2225.54; P = .034) and 73.08 (95% CI: 8.33, 9636.35; P < .001), respectively. There were significant differences in hazard ratios between patients with partial and those with complete infarction for persistence or recurrence of menorrhagia (hazard ratio, 7.45; 95% CI: 2.08, 28.31; P = .002) and bulk-related symptoms (hazard ratio, 5.90; 95% CI: 1.66, 21.92; P = .007). There was no significant correlation between patient age, number of leiomyomas, location of the dominant leiomyoma, or baseline uterine and dominant leiomyoma volume and clinical failure. CONCLUSION: Women with leiomyoma infarction above 90% on contrast-enhanced MR images after UAE show significantly better symptom control and fewer reinterventions than do patients with a lower infarction rate.

Penetrating chest trauma after attempted suicide: An extraordinary behavior of a posttraumatic pulmonary artery pseudoaneurysm.

Posttraumatic pulmonary artery pseudoaneurysm is a very rare, yet potentially lethal complication after thoracic trauma. Pulmonary artery pseudoaneurysm is associated with high mortality. Still literature highlights that untreated, lesions can enlarge, rupture, and lead to exsanguination and death. We present a case of a posttraumatic peripheral pulmonary artery pseudoaneurysm with complete disappearance after one year. This case confirms that conservative treatment can be an effective option in asymptomatic and stable patients.

Cloverleaf Sign in Pelvic Magnetic Resonance Imaging for Deep Infiltrating Endometriosis: Association With Longer Operation Times, Greater Blood Loss, and Higher Rates of Bowel Resection.

OBJECTIVES: The aim of this study was to evaluate the significance of a new imaging sign, the "cloverleaf sign," in diagnosing deep infiltrating endometriosis (DIE) with magnetic resonance imaging (MRI) in concordance to intraoperative findings. MATERIALS AND METHODS: This retrospective study included 103 patients operated during the January 2016 to June 2018 period with preoperative 1.5 T and 3 T MRI, with or without vaginal and rectal gel filling. Magnetic resonance imaging scans were read blinded to intraoperative findings by a specialized gynecologic radiologist and a junior radiologist, and then compared with intraoperative findings by looking at the operation report, postoperative diagnosis, and intraoperative images and videos by an experienced gynecologist surgeon specialized in endometriosis surgery. All endometriosis lesions were confirmed by pathology. The "cloverleaf sign" was defined as a cloverleaf-like figure in imaging morphology; the "leaves" formed by at least 3 different organs come together in the center of the figure formed by constrictive adhesions including T2-weighted (T2W) hypointense DIE. Operation times, intraoperative blood loss, and the frequency of DIE and bowel resections were analyzed in cloverleaf and noncloverleaf groups. The 2-sample Wilcoxon rank-sum (Mann-Whitney U) test and multivariate analysis of variance were used to calculate the significance of an overall impact of cloverleaf sign on operation time, blood loss, and the amount of the bowel resection rate. P < 0.05 was considered statistically significant. RESULTS: The prevalence of DIE in the study population was 79.6%. A total of 11.5% of the patients had no endometriosis, 32.6% had rASRM I and II, and 55.9% had rASRM III and IV. Forty-six patients (45%) had received rectal and vaginal gel opacification before scanning, 57 (55%) did not. A cloverleaf sign on MRI was detected in 34 patients (15 in gel filling and 19 in nonfilling group). The interreader agreement was almost perfect 0.91 (κ). The median operation time in the cloverleaf group was 248 minutes (interquartile range [IQR], 165-330) compared with 145 minutes in the noncloverleaf group (IQR, 90-210), that is, significantly higher (P < 0.001). Intraoperative blood loss was also significantly higher in the conglomerate group (125 vs 50 mL; IQR, 100-300 vs 50-100; P < 0.001). Of the bowel resections in our study population, 41% (14/34) were performed on patients with a cloverleaf sign in the MRI, compared with 13% (9/69) in patients without the cloverleaf sign. CONCLUSIONS: The "cloverleaf" MRI sign was associated with significantly longer operation time, increased intraoperative blood loss, and higher rates of bowel resection in DIE patients.

Late-fluorescence mammography assesses tumor capillary permeability and differentiates malignant from benign lesions.

Using scanning time-domain instrumentation we recorded fluorescence projection mammograms on few breast cancer patients prior, during and after infusion of indocyanine green (ICG), while monitoring arterial ICG concentration by transcutaneous pulse densitometry. Late-fluorescence mammograms recorded after ICG had been largely cleared from the blood by the liver, showed invasive carcinomas at high contrast over a rather homogeneous background, whereas benign lesions did not produce (focused) fluorescence contrast. During infusion, tissue concentration contrast and hence fluorescence contrast is determined by intravascular contributions, whereas late-fluorescence mammograms are dominated by contributions from protein-bound ICG extravasated into the interstitium, reflecting relative microvascular permeabilities of carcinomas and normal breast tissue. We simulated intravascular and extravascular contributions to ICG tissue concentration contrast within a two-compartment unidirectional pharmacokinetic model.

Selecting Patients for Lobar Lung Volume Reduction Therapy: What Quantitative Computed Tomography Parameters Matter?

PURPOSE: Evaluation of emphysema distribution with quantitative computed tomography (qCT) prior to endoscopic lung volume reduction (ELVR) is recommended. The aim of this study was to determine which of the commonly assessed qCT parameters prior to endoscopic lung volume reduction (ELVR) best predicts outcome of treatment. MATERIALS AND METHODS: 50 patients who underwent technically successful ELVR at our institution were retrospectively analyzed. We performed quantitative analysis of the CT scans obtained prior to ELVR and carried out Mann-Whitney U-tests and a logistic regression analysis to identify the qCT parameters that predict successful outcome of ELVR in terms of improved forced expiratory volume in 1 second (FEV1). RESULTS: In the Mann-Whitney U-test, the interlobar emphysema heterogeneity index (p = 0.008) and the pulmonary emphysema score (p = 0.022) showed a statistically significant difference between responders and non-responders. In multiple logistic regression analysis only the interlobar emphysema heterogeneity index (p = 0.008) showed a statistically significant impact on the outcome of ELVR, while targeted lobe volume, total lung volume, targeted lobe emphysema score and total lung emphysema score did not. CONCLUSION: Of all commonly assessed quantitative CT parameters, only the heterogeneity index definitely allows prediction of ELVR outcome in patients with advanced chronic obstructive pulmonary disease (COPD). KEY POINTS: · Quantitative CT is recommended prior to ELVR.. · The relevance of the obtained parameters from quantitative CT remains controversial.. · This study confirms that only the emphysema heterogeneity index has a definite impact.. CITATION FORMAT: · Theilig DC, Huebner R, Neumann K et al. Selecting Patients for Lobar Lung Volume Reduction Therapy: What Quantitative Computed Tomography Parameters Matter?. Fortschr Röntgenstr 2019; 191: 40 - 47.

A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over.

PURPOSE: To evaluate the opinion and assessment of radiologists, surgeons and medical students on a number of important topics regarding the future of radiology, such as artificial intelligence (AI), turf battles, teleradiology and 3D-printing. METHOD: An online questionnaire was created using the SurveyMonkey platform targeting radiologists, students and surgeons throughout the German speaking part of Switzerland. A total of 170 people participated in the survey (59 radiologists, 56 surgeons and 55 students). Statistical analysis was carried out using the Kruskal-Wallis test with Dunn's multiple comparison post-hoc tests. RESULTS: While the majority of participants agreed that AI should be included as a support system in radiology (Likert scale 0-10: Median value 8), surgeons were less supportive than radiologists (p = 0.001). Students saw a potential threat of AI as more likely than radiologists did (p = 0.041). When asked whether they were concerned about "turf losses" from radiology to other disciplines, radiologists were much more likely to agree than students (p < 0.001). Of the students that do not intend to specialize in radiology, 26 % stated that AI was one of the reasons. Surgeons advocate the use of teleradiology. CONCLUSIONS: With regard to AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, whereas medical students and surgeons tend to be more skeptical towards this technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are rather afraid of turf losses.

Increased Expiratory Computed Tomography Density Reveals Possible Abnormalities in Radiologically Preserved Lung Parenchyma in Idiopathic Pulmonary Fibrosis.

OBJECTIVES: Idiopathic pulmonary fibrosis (IPF) is a progressive lethal chronic lung disease with unclear pathogenesis. Radiological hallmark is the pattern of usual interstitial pneumonia accentuated in peripheral and basal areas with otherwise preserved lung structure. One hypothesis is that alveolar collapse and consequent induration lead to fibrotic transformation of lung tissue. The aim of the study was to investigate normal-appearing tissue during expiration for signs of collapsibility and differences from other diseases or controls. MATERIALS AND METHODS: We retrospectively assessed a total of 43 patients (15 IPFs, 13 chronic obstructive pulmonary diseases, and 15 controls) with nonenhanced computed tomography (CT) in inspiration and expiration, performed for routine clinical workup. Densitometry of visually unaffected lung tissue was conducted in all lung lobes with a region of interest of 15-mm in diameter on soft tissue kernel reconstruction (slice thickness, 1 mm) during inspiration and expiration. RESULTS: One-factor analysis of variance analysis yielded significant difference in attenuation changes between inspiration and expiration of unaffected lung parenchyma among all subject groups in all lung lobes. For IPF patients, the highest differences in densities were observed in the lower lobes, which is the predominantly affected site of usual interstitial pneumonia. In the chronic obstructive pulmonary disease group, the density remained rather equal in the entire lung. CONCLUSIONS: High CT attenuation changes between inspiration and expiration in IPF patients might suggest altered lung parenchyma in normal-appearing tissue on CT. Density changes during the respiratory cycle might be explained by alveolar collapse of radiologically unaffected lung tissue possibly preceding fibrosis. These results support the concept of alveolar collapse preceding lung fibrosis in IPF.