Differentiating inflammatory and malignant pulmonary lesions on 3T lung MRI with radiomics of apparent diffusion coefficient maps and T2w derived radiomic feature maps.

Background: Differentiating inflammatory from malignant lung lesions continues to be challenging in clinical routine, frequently requiring invasive methods like biopsy. Therefore, we aimed to investigate if inflammatory and malignant pulmonary lesions could be distinguished noninvasively using radiomics of apparent diffusion coefficient (ADC) maps and radiomic feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) magnetic resonance imaging (MRI) of the lung. Methods: Fifty-four patients with an unclear pulmonary lesion on computed tomography (CT) were prospectively included and examined by 3T MRI with T2w and diffusion-weighted sequences (b values of 50 and 800). ADC maps were calculated automatically. All patients underwent biopsy or bronchoalveolar lavage (BAL). Sixteen patients were excluded (e.g., motion artifacts), leaving 19 patients each with malignant and inflammatory pulmonary lesions. Target lesions were defined by biopsy or as the largest lesion (BAL-based pathogen detection), and two readers placed volumes of interest (VOIs) around the lesions on T2w images and ADC maps. One hundred and seven features were conventionally extracted from the ADC maps using PyRadiomics. T2w images were converted to 107 parametric feature maps per patient using a PyRadiomics-based, pretested software tool developed by our group. VOIs were copied from T2w images to T2 maps for feature quantification. Features were tested for significant differences using the Mann-Whitney U-test. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and interreader agreement by intraclass correlation coefficients (ICCs). Results: Fifty-eight features derived from ADC maps differed significantly between malignant and inflammatory pulmonary lesions, with areas under the curve (AUCs) >0.90 for 5 and >0.80 for 27 features, compared with 67 features from T2 maps (5 features with AUCs >0.80). ICCs were excellent throughout. Conclusions: ADC and T2 maps differentiate inflammatory and malignant pulmonary lesions with outstanding (ADC) and excellent (T2w derived feature maps) diagnostic performance. MRI could thus guide the further diagnostic workup and a timely initiation of the appropriate therapy.

The influence of left bundle branch block on myocardial T1 mapping.

Tissue characterisation using T1 mapping has become an established magnetic resonance imaging (MRI) technique to detect myocardial diseases. This retrospective study aimed to determine the influence of left bundle branch block (LBBB) on T1 mapping at 1.5 T. Datasets of 36 patients with LBBB and 27 healthy controls with T1 mapping (Modified Look-Locker inversion-recovery (MOLLI), 5(3)3 sampling) were included. T1 relaxation times were determined on mid-cavity short-axis images. R2 maps were generated as a pixel-wise indicator for the goodness of the fit of T1 maps. R2 values were significantly lower in patients with LBBB than in healthy controls (whole myocardium/septum, 0.997, IQR, 0.00 vs. 0.998, IQR, 0.00; p = 0.008/0.998, IQR, 0.00 vs. 0.999, IQR, 0.00; p = 0.027). Manual correction of semi-automated evaluation tended to improve R2 values but not significantly. Strain analysis was performed and the systolic dyssynchrony index (SDIglobal) was calculated as a measure for left ventricular dyssynchrony. While MRI is generally prone to artefacts, lower goodness of the fit in LBBB may be mainly attributable to asynchronous contraction. Therefore, careful checking of the source data and, if necessary, manual post-processing is important. New techniques might improve the goodness of the fit of T1 mapping by reducing sampling in the motion prone diastole of LBBB patients.

The role of parametric feature maps to correct different volume of interest sizes: an in vivo liver MRI study.

BACKGROUND: Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). METHODS: T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. RESULTS: When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. CONCLUSIONS: Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. RELEVANCE STATEMENT: The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. KEY POINTS: • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack.

Chest computed tomography findings typical of COVID-19 pneumonia in Germany as early as 30 December 2019: a case report.

BACKGROUND: The first cases of coronavirus disease 2019 were officially confirmed in Germany and its European neighbors in late January 2020. In France and Italy, there is evidence that coronavirus disease 2019 was spreading as early as December 2019. CASE PRESENTATION: We report on a 71-year-old male patient from Germany who was admitted to our hospital on 30 December 2019 with pneumonia of unclear etiology and chest computed tomography findings typical of COVID-19 pneumonia. CONCLUSION: This case may indicate that coronavirus disease 2019 was already spreading in Germany as early as December 2019.

Editor's Choice - Single Centre Midterm Experience with Primary Fenestrated Endovascular Aortic Aneurysm Repair for Short Neck, Juxtarenal, and Suprarenal Aneurysms.

OBJECTIVE: The use of fenestrated stent grafts to treat short neck, juxta- and suprarenal aortic aneurysms is increasing worldwide, but midterm outcome reports are scarce. This study aimed to report peri-operative results and midterm outcomes after five years from a single centre. METHODS: Patients treated with primary fenestrated endovascular aortic aneurysm repair (FEVAR) for short neck, juxta- or suprarenal aortic aneurysms within the period January 2010 to May 2020 with follow up in the centre were included. Early (technical success, operative mortality, spinal cord ischaemia) and five year outcomes (cumulative survival, freedom from aortic related death, target vessel patency, target vessel instability [TVI], re-interventions) were analysed. RESULTS: A total of 349 patients (313 male, mean age 72.3 ± 7.7 years) were included in the study. Technical success was 98% (342/349). The thirty day mortality rate was 0.9% (3/349). Estimated survival at five years was 69.3 ± 3.1%. Freedom from aneurysm related death at five years was 98.8% ± 0.7%. Estimated target vessel patency at five years was 98.7 ± 0.4%. Estimated freedom from TVI at five years was 97.2 ± 0.6%. Estimated freedom from re-intervention at five years was 86.5 ± 2.3%. Survival did not differ significantly between patients with and without re-interventions (p = .088). CONCLUSION: Midterm results of FEVAR remain good as indicated by sustained target vessel patency and low aortic related mortality rates. An important proportion of patients require re-interventions, which do not have a negative impact on midterm survival.

Elevation of cardiac biomarkers in stroke is associated with pathological findings on cardiac MRI-results of the HEart and BRain interfaces in Acute Stroke study.

INTRODUCTION: Cardiac biomarkers, such as high-sensitivity cardiac troponin T (hs-cTnT), are frequently elevated in ischemic stroke patients but the mechanisms underlying this elevation are insufficiently understood. We determined the presence of cardiac damage, assessed using cardiac magnetic resonance imaging (CMR), in stroke patients with elevated hs-cTnT and brain natriuretic peptide (BNP). METHODS: This is a post hoc analysis of the prospective, investigator-initiated, cross-sectional HEart and BRain interfaces in Acute Stroke (HEBRAS) study. All patients underwent the measurement of hs-cTnT and BNP as well as gadolinium-enhanced CMR in the acute phase of ischemic stroke. We performed unadjusted and adjusted logistic regression models to assess the association between hs-cTnT and BNP elevation and the presence of pathological CMR findings. RESULTS: Two hundred and thirty-three stroke patients (median age 67 years, 33% female) were included, of whom 43 (21%) had elevated hs-cTnT and 109 (47%) had elevated BNP. Hundred of the 233 (43%) patients had pathological findings on CMR had focal fibrosis as detected by late-gadolinium enhancement (LGE) in 51 (23%), left-ventricular hypertrophy (LVH) in 38 (16%), reduced LVEF in 32 (14%), and left-atrial dilatation in 34 (15%). After adjustment for potential confounders, both hs-cTnT (adjOR 5.0 (95%CI 2.1-11.7), p < 0.001) and BNP (adjOR 4.1 (95%CI 2.3-7.3), p < 0.001) were significantly associated with pathological findings on CMR. Hs-cTnT was associated with LGE, LVEF, and LVH, whereas BNP was associated with left-atrial dilatation and LVEF, LVH. CONCLUSION: Elevated cardiac biomarkers in acute stroke including CMR are strongly associated with pathological findings on CMR. In acute stroke patients, the elevation of cardiac biomarkers may identify patients who require a more thorough cardiology work-up.

Cardiac magnetic resonance imaging in patients with left bundle branch block: Patterns of dyssynchrony and implications for late gadolinium enhancement imaging.

Background: Left bundle branch block (LBBB) is a ventricular conduction delay with high prevalence. Aim of our study is to identify possible recurring patterns of artefacts in late gadolinium enhancement (LGE) imaging in patients with LBBB who undergo cardiac magnetic resonance imaging (MRI) and to define parameters of mechanical dyssynchrony associated with artefacts in LGE images. Materials and methods: Fifty-five patients with LBBB and 62 controls were retrospectively included. Inversion time (TI) scout and LGE images were reviewed for artefacts. Dyssynchrony was identified using cardiac MRI by determining left ventricular systolic dyssynchrony indices (global, septal segments, and free wall segments) derived from strain analysis and features of mechanical dyssynchrony (apical rocking and septal flash). Results: Thirty-seven patients (67%) with LBBB exhibited inhomogeneous myocardial nulling in TI scout images. Among them 25 (68%) patients also showed recurring artefact patterns in the septum or free wall on LGE images and artefacts also persisted in 18 (72%) of those cases when utilising phase sensitive inversion recovery. Only the systolic dyssynchrony index of septal segments allowed differentiation of patient subgroups (artefact/no artefact) and healthy controls (given as median, median ± interquartile range); LBBB with artefact: 10.44% (0.44-20.44%); LBBB without artefact: 6.82% (-2.18-15.83%); controls: 4.38% (1.38-7.38%); p < 0.05 with an area under the curve of 0.863 (81% sensitivity, 89% specificity). Septal flash and apical rocking were more frequent in the LBBB with artefact group than in the LBBB without artefact group (70 and 62% versus 33 and 17%; p < 0.05). Conclusion: Patients with LBBB show recurring artefact patterns in LGE imaging. Use of strain analysis and evaluation of mechanical dyssynchrony may predict the occurrence of such artefacts already during the examination and counteract misinterpretation.

CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations.

18F-FDG-PET/CT is standard to assess response in Hodgkin lymphoma by quantifying metabolic activity with the Deauville score. PET/CT, however, is time-consuming, cost-extensive, linked to high radiation and has a low availability. As an alternative, we investigated radiomics from non-contrast-enhanced computed tomography (NECT) scans. 75 PET/CT examinations of 43 patients on two different scanners were included. Target lesions were classified as Deauville score 4 positive (DS4+) or negative (DS4-) based on their SUVpeak and then segmented in NECT images. From these segmentations, 107 features were extracted with PyRadiomics. All further statistical analyses were then performed scanner-wise: differences between DS4+ and DS4- manifestations were assessed with the Mann-Whitney-U-test and single feature performances with the ROC-analysis. To further verify the reliability of the results, the number of features was reduced using different techniques. The feature median showed a high sensitivity for DS4+ manifestations on both scanners (scanner A: 0.91, scanner B: 0.85). It furthermore was the only feature that remained in both datasets after applying different feature reduction techniques. The feature median from NECT concordantly has a high sensitivity for DS4+ Hodgkin manifestations on two different scanners and thus could provide a surrogate for increased metabolic activity in PET/CT.

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.

Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (n = 99), a validation (n = 25) and a test cohort (n = 25). In the first stage, the spleen was automatically segmented using a modified U-Net architecture. In the second stage, the CT images were classified into two groups using a 3D DenseNet to discriminate between the causes of splenomegaly, first using the whole abdominal CT, and second using only the spleen segmentation mask. The classification performances were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Occlusion sensitivity maps were applied to the whole abdominal CT images, to illustrate which regions were important for the prediction. When trained on the whole abdominal CT volume, the DenseNet was able to differentiate between the lymphoma and liver cirrhosis in the test cohort with an AUC of 0.88 and an ACC of 0.88. When the model was trained on the spleen segmentation mask, the performance decreased (AUC = 0.81, ACC = 0.76). Our model was able to accurately segment splenomegaly and recognize the underlying cause. Training on whole abdomen scans outperformed training using the segmentation mask. Nonetheless, considering the performance, a broader and more general application to differentiate other causes for splenomegaly is also conceivable.

Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study.

BACKGROUND: In radiomics studies, differences in the volume of interest (VOI) are often inevitable and may confound the extracted features. We aimed to correct this confounding effect of VOI variability by applying parametric maps with a fixed voxel size. METHODS: Ten scans of a cup filled with sodium chloride solution were scanned using a multislice computed tomography (CT) unit. Sphere-shaped VOIs with different diameters (4, 8, or 16 mm) were drawn centrally into the phantom. A total of 93 features were extracted conventionally from the original images using PyRadiomics. Using a self-designed and pretested software tool, parametric maps for the same 93 features with a fixed voxel size of 4 mm3 were created. To retrieve the feature values from the maps, VOIs were copied from the original images to preserve the position. Differences in feature quantities between the VOI sizes were tested with the Mann-Whitney U-test and agreement with overall concordance correlation coefficients (OCCC). RESULTS: Fifty-five conventionally extracted features were significantly different between the VOI sizes, and none of the features showed excellent agreement in terms of OCCCs. When read from the parametric maps, only 8 features showed significant differences, and 3 features showed an excellent OCCC (≥ 0.85). The OCCCs for 89 features substantially increased using the parametric maps. CONCLUSIONS: This phantom study shows that converting CT images into parametric maps resolves the confounding effect of VOI variability and increases feature reproducibility across VOI sizes.

Differentiation of Pulmonary Lymphoma Manifestations and Nonlymphoma Infiltrates in Possible Invasive Fungal Disease Using Fast T1-weighted Magnetic Resonance Imaging at 3 T Comparison of Texture Analysis, Mapping, and Signal Intensity Quotients.

PURPOSE: This study aimed to evaluate the diagnostic performance of texture analysis (TA), T1 mapping, and signal intensity quotients derived from fast T1-weighted gradient echo (T1w GRE) sequences for differentiating pulmonary lymphoma manifestations and nonlymphoma infiltrates in possible invasive fungal disease in immunocompromised hematological patients. MATERIALS AND METHODS: Twenty patients with hematologic malignancies and concomitant immunosuppression (including 10 patients with pulmonary lymphoma manifestations and 10 patients with nonlymphoma infiltrates) prospectively underwent 3 T magnetic resonance imaging using a conventional T1w GRE sequence and a T1w GRE mapping sequence with variable flip angle. A region of interest was placed around the most representative lesion in each patient. TA was performed using PyRadiomics. T1 relaxation times were extracted from precompiled maps and calculated manually. Signal intensity quotients (lesion/muscle) were calculated from conventional T1w GRE sequences. RESULTS: Of all TA features, variance, mean absolute deviation, robust mean absolute deviation, interquartile range, and minimum were significantly different between the 2 entities (P<0.05), with excellent diagnostic performance in receiver operating characteristic analysis (area under the curve [AUC] >80%). Neither T1 relaxation times from precompiled maps (AUC=63%; P=0.353) nor manual calculation (AUC=63%; P=0.353) nor signal intensity quotients (AUC=70%; P=0.143) yielded significant differences. CONCLUSIONS: TA from fast T1w GRE images can differentiate pulmonary lymphoma manifestations and nonlymphoma infiltrates in possible invasive fungal disease with excellent diagnostic performance using the TA features variance, mean absolute deviation, robust mean absolute deviation, interquartile range, and minimum. Combining a fast T1w GRE sequence with TA seems to be a promising tool to differentiate these 2 entities noninvasively.

Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study.

We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V-VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann-Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen.

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen.

Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features.

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.

Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study.

We aimed to evaluate radiomic features' stability across different region of interest (ROI) sizes in CT and MR images. We chose a phantom with a homogenous internal structure so no differences for a feature extracted from ROIs of different sizes would be expected. For this, we scanned a plastic cup filled with sodium chloride solution ten times in CT and per MR sequence (T1-weighted-gradient-echo and T2-weighted-turbo-inversion-recovery-magnitude). We placed sphere-shaped ROIs of different diameters (4, 8, and 16 mm, and 4, 8, and 16 pixels) into the phantom's center. Features were extracted using PyRadiomics. We assessed feature stability across ROI sizes with overall concordance correlation coefficients (OCCCs). Differences were tested for significance with the Mann-Whitney U-test. Of 93 features, 87 T1w-derived, 87 TIRM-derived, and 70 CT-derived features were significantly different between ROI sizes. Among MR-derived features, OCCCs showed excellent (>0.90) agreement for mean, median, and root mean squared for ROI sizes between 4 and 16 mm and pixels. We further observed excellent agreement for 10th and 90th percentile in T1w and 10th percentile in T2w TIRM images. There was no excellent agreement among the OCCCs of CT-derived features. In summary, many features indicated significant differences and only few showed excellent agreement across varying ROI sizes, although we examined a homogenous phantom. Since we considered a small phantom in an experimental setting, further studies to investigate this size effect would be necessary for a generalization. Nevertheless, we believe knowledge about this effect is crucial in interpreting radiomics studies, as features that supposedly discriminate disease entities may only indicate a systematic difference in ROI size.

Single Center Experience with Endovascular Repair of Acute Thoracoabdominal Aortic Aneurysms.

PURPOSE: To investigate feasibility and outcomes of endovascular repair for acute thoracoabdominal aortic aneurysms (TAAA). MATERIALS AND METHODS: Data from a single center were retrospectively analyzed. Patients who underwent endovascular repair for acute TAAA between January 2010 and April 2020 were included. Perioperative and mid-term follow-up outcomes were analyzed. Survival, freedom from reintervention, and target vessel patency were calculated by Kaplan-Meier analysis. RESULTS: A total of 30 patients (18 men, 67.5 ± 6.9 years) underwent endovascular repair for acute symptomatic (n = 15) or contained ruptured (n = 15) TAAA. An off-the-shelf four-branched stent-graft (T-Branch) was used in 19 (63.3%) patients, a custom-made device (CMD) with expedite order in 5 (16.7%) patients, a CMD with short anticipated delivery time in 3 (10.0%) patients, and a CMD available in the hospital in 3 (10.0%) patients. Technical success was 90.0% (n = 27). Thirty-day mortality was 10% (n = 3). There was no complete persistent paraplegia, but one (3.3%) patient suffered permanent limb weakness. Estimated survival at 1 and 2 years was 86.3% ± 6.4%, and 82.3% ± 7.2%, respectively. Estimated freedom from reintervention at 1 and 2 years was 81.4% ± 7.6% and 73% ± 8.8%. Estimated target vessel patency at 1 and 2 years was 96.6% ± 2% and 92.6% ± 2.9%. CONCLUSION: Endovascular treatment of acute TAAA in this selected group of patients was associated with low early mortality and excellent mid-term survival. The off-the-shelf stent-graft option (T-Branch) was used in the majority of patients. Endovascular repair should be considered the first option for suitable acute TAAA.

Enhancing the differentiation of pulmonary lymphoma and fungal pneumonia in hematological patients using texture analysis in 3-T MRI.

OBJECTIVES: To evaluate texture analysis in nonenhanced 3-T MRI for differentiating pulmonary fungal infiltrates and lymphoma manifestations in hematological patients and to compare the diagnostic performance with that of signal intensity quotients ("nonenhanced imaging characterization quotients," NICQs). METHODS: MR scans were performed using a speed-optimized imaging protocol without an intravenous contrast medium including axial T2-weighted (T2w) single-shot fast spin-echo and T1-weighted (T1w) gradient-echo sequences. ROIs were drawn within the lesions to extract first-order statistics from original images using HeterogeneityCAD and PyRadiomics. NICQs were calculated using signal intensities of the lesions, muscle, and fat. The standard of reference was histology or clinical diagnosis in follow-up. Statistical testing included ROC analysis, clustered ROC analysis, and DeLong test. Intra- and interrater reliability was tested using intraclass correlation coefficients (ICC). RESULTS: Thirty-three fungal infiltrates in 16 patients and 38 pulmonary lymphoma manifestations in 19 patients were included. Considering the leading lesion in each patient, diagnostic performance was excellent for T1w entropy (AUC 80.2%; p < 0.005) and slightly inferior for T2w energy (79.9%; p < 0.005), T1w uniformity (79.6%; p < 0.005), and T1w energy (77.0%; p < 0.01); the best AUC for NICQs was 72.0% for T2NICQmean (p < 0.05). Intra- and interrater reliability was good to excellent (ICC > 0.81) for these parameters except for moderate intrarater reliability of T1w energy (ICC = 0.64). CONCLUSIONS: T1w entropy, uniformity, and energy and T2w energy showed the best performances for differentiating pulmonary lymphoma and fungal pneumonia and outperformed NICQs. Results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters. KEY POINTS: • Texture analysis in nonenhanced pulmonary MRI improves the differentiation of pulmonary lymphoma and fungal pneumonia compared with signal intensity quotients. • T1w entropy, uniformity, and energy along with T2w energy show the best performances for differentiating pulmonary lymphoma from fungal pneumonia. • The results of the texture analysis should be checked for their intrinsic consistency to identify possible incongruities of single parameters.

Evaluation of left ventricular function in patients with acute ischaemic stroke using cine cardiovascular magnetic resonance imaging.

AIMS: Heart failure (HF) is frequent in patients with acute ischaemic stroke (AIS) and associated with higher morbidity and mortality. Assessment of cardiac function in AIS patients using cardiovascular MRI (CMR) may help to detect HF. We report the rate of systolic and diastolic dysfunction in a cohort of patients with AIS using CMR and compare cine real-time (CRT) sequences with the reference of segmented cine steady-state free precession sequences. METHODS AND RESULTS: Patients with AIS without known atrial fibrillation were prospectively enrolled in the HEart and BRain Interfaces in Acute Ischemic Stroke (HEBRAS) study (NCT02142413) and underwent CMR at 3 Tesla within 7 days after AIS. Validity of CRT sequences was determined in 50 patients. A total of 229 patients were included in the analysis (mean age 66 years; 35% women; HF 2%). Evaluation of cardiac function was successful in 172 (75%) patients. Median time from stroke onset to CMR was 82 h (interquartile range 56-111) and 54 h (interquartile range 31-78) from cerebral MRI to CMR. Systolic dysfunction was observed in 43 (25%) and diastolic dysfunction in 102 (59%) patients. Diagnostic yield was similar using CRT or segmented cine imaging (no significant difference in left ventricular ejection fraction, myocardial mass, time to peak filling rate, and peak filling rate ratio E/A). Intraobserver and interobserver agreement was high (κ = 0.78-1.0 for all modalities). CONCLUSIONS: Cardiovascular MRI at 3 Tesla is an appropriate method for the evaluation of cardiac function in a selected cohort of patients with AIS. Systolic and diastolic dysfunction is frequent in these patients. CRT imaging allows reliable assessment of systolic and diastolic function.

Diagnosis of Left Ventricular Diastolic Dysfunction Using Cardiac Magnetic Resonance Imaging: Comparison of Volume-Time Curves Derived from Long- and Short-Axis Cine Steady-State Free Precession Datasets.

PURPOSE: To evaluate the diagnostic performance of diastolic function parameters derived from long-axis (LAX) planimetry compared with short-axis (SAX) volumetry in cardiac magnetic resonance imaging. MATERIALS AND METHODS: Cine steady-state free precession (SSFP) datasets of 15 healthy participants (8 young and 7 middle aged) and 25 patients with echocardiographically proven diastolic dysfunction (9 mild, 9 moderate, and 7 severe) were retrospectively included. Volume-time curves for assessing left ventricular (LV) function were obtained by manually contouring the LV endocardial borders in SAX and LAX datasets. The time needed for contouring was recorded for each dataset. The following LV parameters were determined: end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), myocardial mass (MM), time to peak filling rate (TPFR), normalized peak filling rate (nPFR), and the ratio of early to late peak filling rate (E/A ratio). A Wilcoxon signed-rank test was used to compare subgroups based on age and severity of diastolic dysfunction for statistical differences. Intraclass correlation coefficients were used to assess intermethod and interobserver reliability. RESULTS: Accuracy for the diagnosis of diastolic dysfunction was highest for E/A (mild diastolic dysfunction) and nPFR (any stage of diastolic dysfunction) derived from LAX datasets (E/A: area under the curve (AUC) = 0.97, sensitivity of 68 % and specificity of 100 %; nPFR: AUC = 0.84, sensitivity of 84 % and specificity of 80 %). Diastolic parameters showed a moderate to good intraclass correlation between both methods. The mean differences in EDV, ESV, EF, and MM were 5.3 ml, 1.9 ml, 3.5 %, and 11 g, respectively (each p < 0.001). Significantly less time was needed to derive volume-time curves from LAX images (median 14:45 min, interquartile range 14:15-15:53 min versus median 29:25 min, interquartile range 28:12-32:22 min; p = 0.001). The interobserver reliability was generally good to excellent. CONCLUSION: Diastolic function parameters derived from left ventricular LAX planimetry have high diagnostic performance and can be obtained in significantly less time compared with SAX volumetry. These findings may pave the way for routine use of LAX planimetry in the clinical diagnosis of diastolic dysfunction. KEY POINTS: · Diastolic function parameters derived from long-axis datasets have high diagnostic performance.. · Generation of volume-time curves using long-axis datasets requires significantly less time.. · This time savings may allow use of cardiac MRI for the diagnosis of diastolic dysfunction in the clinical routine.. CITATION FORMAT: · Schaafs LA, Wyschkon S, Elgeti M et al. Diagnosis of Left Ventricular Diastolic Dysfunction Using Cardiac Magnetic Resonance Imaging: Comparison of Volume-Time Curves Derived from Long- and Short-Axis Cine Steady-State Free Precession Datasets. Fortschr Röntgenstr 2020; 192: 764 - 775.

Vascular pattern and diagnostic accuracy of contrast-enhanced ultrasound (CEUS) in spleen alterations.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) has been used as an additional imaging technique in order to clarify rare focal splenic lesions (FSL). CEUS is a safe and cost-effective modality for assessment of perfusion. OBJECTIVE: To validate contrast enhancement pattern and evaluate the diagnostic accuracy of CEUS in unclear FSL. METHODS: CEUS examinations of the spleen in 50 patients between 2012 and 2018 were included in the study. Examinations were performed using B-mode, colour-coded Doppler ultrasound (CCDS) and CEUS after injection of sulphur hexafluoride microbubbles and interpreted in consensus by two experienced radiologists. Reference standard was defined as histopathological report and clinical course (treatment response, long term follow up). RESULTS: All patients were successfully examined by CEUS without an adverse reaction. CEUS presented the correct differentiation of benign and malignant alterations in 49/50 (98%). Lesion washout was found in all malignant but also 16.7% of all benign lesions. Matched to the histopathological report and clinical follow up, CEUS represented a sensitivity of 100% (95% -CI, 57-100), a specificity of 98% (95% -CI, 88-100), a positive predictive value (PPV) of 83% (95% -CI, 44-97) and a negative predictive value (NPV) of 100% (95% -CI, 92-100). CONCLUSION: CEUS may provide additional information by visualization of dynamic contrast enhancement pattern to differentiate benign and malignant lesions. Nevertheless, established criteria for malignancy (early enhancement or washout) in FSL should be considered with caution since they are also found in benign lesions.