Publications by authors named "Thorsten Persigehl"

115 Publications

Robustness of dual-energy CT-derived radiomic features across three different scanner types.

Eur Radiol 2021 Sep 20. Epub 2021 Sep 20.

Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114, USA.

Objectives: To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems.

Methods: An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Radiomic features were extracted after standardized image processing, following ROI placement in phantom tissues and healthy appearing hepatic, splenic and muscular tissue of patients using virtual monoenergetic images at 65 keV (VMI) and virtual unenhanced images (VUE). In total, 774 radiomic features were extracted including 86 original features and 8 wavelet transformations hereof. Concordance correlation coefficients (CCC) and analysis of variances (ANOVA) were calculated to determine inter-scanner robustness of radiomic features with a CCC of ≥ 0.9 deeming a feature robust.

Results: None of the phantom-derived features attained the threshold for high feature robustness for any inter-scanner comparison. The proportion of robust features obtained from patients scanned on all three scanners was low both in VMI (dsDECT vs. rsDECT:16.1% (125/774), dlDECT vs. rsDECT:2.5% (19/774), dsDECT vs. dlDECT:2.6% (20/774)) and VUE (dsDECT vs. rsDECT:11.1% (86/774), dlDECT vs. rsDECT:2.8% (22/774), dsDECT vs. dlDECT:2.7% (21/774)). The proportion of features without significant differences as per ANOVA was higher both in patients (51.4-71.1%) and in the phantom (60.6-73.4%).

Conclusions: The robustness of radiomic features across different DECT scanners in patients was low and the few robust patient-derived features were not reflected in the phantom experiment. Future efforts should aim to improve the cross-platform generalizability of DECT-derived radiomics.

Key Points: • Inter-scanner robustness of dual-energy CT-derived radiomic features was on a low level in patients who underwent clinical examinations on three DECT platforms. • The few robust patient-derived features were not confirmed in our phantom experiment. • Limited inter-scanner robustness of dual-energy CT derived radiomic features may impact the generalizability of models built with features from one particular dual-energy CT scanner type.
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http://dx.doi.org/10.1007/s00330-021-08249-2DOI Listing
September 2021

[Radiological monitoring of immunotherapy in renal cell carcinoma].

Aktuelle Urol 2021 09 24;52(5):474-480. Epub 2021 Aug 24.

Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Köln, Deutschland.

The Response Evaluation Criteria in Solid Tumours (RECIST 1.1) currently represent the most widely established evaluation criteria for standardised therapy monitoring in solid tumours treated with traditional cytostatic and cytotoxic tumour therapies. The increasing use of immune checkpoint inhibitors in the therapy of metastatic renal cell carcinoma poses special challenges for radiological therapy monitoring due to the presence of atypical response patterns and immunotherapy-specific side-effects. Adapted criteria such as immune RECIST (iRECIST) can help in the follow-up assessment of renal cell carcinoma to detect atypical courses of disease under immune checkpoint inhibitor therapy both within and outside of clinical trials.
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http://dx.doi.org/10.1055/a-1489-2163DOI Listing
September 2021

Coronary artery calcification on low-dose chest CT is an early predictor of severe progression of COVID-19-A multi-center, multi-vendor study.

PLoS One 2021 21;16(7):e0255045. Epub 2021 Jul 21.

Department of Radiology, University Hospital of Cologne, Cologne, Germany.

Purpose: Cardiovascular comorbidity anticipates severe progression of COVID-19 and becomes evident by coronary artery calcification (CAC) on low-dose chest computed tomography (LDCT). The purpose of this study was to predict a patient's obligation of intensive care treatment by evaluating the coronary calcium burden on the initial diagnostic LDCT.

Methods: Eighty-nine consecutive patients with parallel LDCT and positive RT-PCR for SARS-CoV-2 were included from three centers. The primary endpoint was admission to ICU, tracheal intubation, or death in the 22-day follow-up period. CAC burden was represented by the Agatston score. Multivariate logistic regression was modeled for prediction of the primary endpoint by the independent variables "Agatston score > 0", as well as the CT lung involvement score, patient sex, age, clinical predictors of severe COVID-19 progression (history of hypertension, diabetes, prior cardiovascular event, active smoking, or hyperlipidemia), and laboratory parameters (creatinine, C-reactive protein, leucocyte, as well as thrombocyte counts, relative lymphocyte count, d-dimer, and lactate dehydrogenase levels).

Results: After excluding multicollinearity, "Agatston score >0" was an independent regressor within multivariate analysis for prediction of the primary endpoint (p<0.01). Further independent regressors were creatinine (p = 0.02) and leucocyte count (p = 0.04). The Agatston score was significantly higher for COVID-19 cases which completed the primary endpoint (64.2 [interquartile range 1.7-409.4] vs. 0 [interquartile range 0-0]).

Conclusion: CAC scoring on LDCT might help to predict future obligation of intensive care treatment at the day of patient admission to the hospital.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255045PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294495PMC
July 2021

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

J Magn Reson Imaging 2021 11 25;54(5):1608-1622. Epub 2021 May 25.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Background: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brain and up to 50% of patients develop brain metastases (BMs). Detection of BMs on MRI is challenging with an inherent risk of missed diagnosis.

Purpose: To train and evaluate a deep learning model (DLM) for fully automated detection and 3D segmentation of BMs in NSCLC on clinical routine MRI.

Study Type: Retrospective.

Population: Ninety-eight NSCLC patients with 315 BMs on pretreatment MRI, divided into training (66 patients, 248 BMs) and independent test (17 patients, 67 BMs) and control (15 patients, 0 BMs) cohorts.

Field Strength/sequence: T -/T -weighted, T -weighted contrast-enhanced (T CE; gradient-echo and spin-echo sequences), and FLAIR at 1.0, 1.5, and 3.0 T from various vendors and study centers.

Assessment: A 3D convolutional neural network (DeepMedic) was trained on the training cohort using 5-fold cross-validation and evaluated on the independent test and control sets. Three-dimensional voxel-wise manual segmentations of BMs by a neurosurgeon and a radiologist on T CE served as the reference standard.

Statistical Tests: Sensitivity (recall) and false positive (FP) findings per scan, dice similarity coefficient (DSC) to compare the spatial overlap between manual and automated segmentations, Pearson's correlation coefficient (r) to evaluate the relationship between quantitative volumetric measurements of segmentations, and Wilcoxon rank-sum test to compare the volumes of BMs. A P value <0.05 was considered statistically significant.

Results: In the test set, the DLM detected 57 of the 67 BMs (mean volume: 0.99 ± 4.24 cm ), resulting in a sensitivity of 85.1%, while FP findings of 1.5 per scan were observed. Missed BMs had a significantly smaller volume (0.05 ± 0.04 cm ) than detected BMs (0.96 ± 2.4 cm ). Compared with the reference standard, automated segmentations achieved a median DSC of 0.72 and a good volumetric correlation (r = 0.95). In the control set, 1.8 FPs/scan were observed.

Data Conclusion: Deep learning provided a high detection sensitivity and good segmentation performance for BMs in NSCLC on heterogeneous scanner data while yielding a low number of FP findings. Level of Evidence 3 Technical Efficacy Stage 2.
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http://dx.doi.org/10.1002/jmri.27741DOI Listing
November 2021

Automated mapping and N-Staging of thoracic lymph nodes in contrast-enhanced CT scans of the chest using a fully convolutional neural network.

Eur J Radiol 2021 Jun 20;139:109718. Epub 2021 Apr 20.

Institute for Diagnostic and Interventional Radiology, University Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.

Purpose: To develop a deep-learning (DL)-based approach for thoracic lymph node (LN) mapping based on their anatomical location.

Method: The training-and validation-dataset included 89 contrast-enhanced computed tomography (CT) scans of the chest. 4201 LNs were semi-automatically segmented and then assigned to LN levels according to their anatomical location. The LN level classification task was addressed by a multi-class segmentation procedure using a fully convolutional neural network. Mapping was performed by firstly determining potential level affiliation for each voxel and then performing majority voting over all voxels belonging to each LN. Mean classification accuracies on the validation data were calculated separately for each level and overall Top-1, Top-2 and Top-3 scores were determined, where a Top-X score describes how often the annotated class was within the top-X predictions. To demonstrate the clinical applicability of our model, we tested its N-staging capabilities in a simulated clinical use case scenario assuming a patient diseased with lung cancer.

Results: The artificial intelligence(AI)-based assignment revealed mean classification accuracies of 86.36 % (Top-1), 94.48 % (Top-2) and 96.10 % (Top-3). Best accuracies were achieved for LNs in the subcarinal level 7 (98.31 %) and axillary region (98.74 %). The highest misclassification rates were observed among LNs in adjacent levels. The proof-of-principle application in a simulated clinical use case scenario for automated tumor N-staging showed a mean classification accuracy of up to 96.14 % (Top-1).

Conclusions: The proposed AI approach for automatic classification of LN levels in chest CT as well as the proof-of-principle-experiment for automatic N-staging, revealed promising results, warranting large-scale validation for clinical application.
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http://dx.doi.org/10.1016/j.ejrad.2021.109718DOI Listing
June 2021

Speeding up the clinical routine: Compressed sensing for 2D imaging of lumbar spine disc herniation.

Eur J Radiol 2021 Jul 29;140:109738. Epub 2021 Apr 29.

Philips Healthcare Germany, Hamburg, Germany.

Purpose: Increasing economic pressure and patient demands for comfort require an ever-increasing acceleration of scan times without compromising diagnostic certainty. This study tested the new acceleration technique Compressed SENSE (CS-SENSE) as well as different reconstruction methods for the lumbar spine.

Methods: In this prospective study, 10 volunteers and 14 patients with lumbar disc herniation were scanned using a sagittal 2D T2 turbo spin echo (TSE) sequence applying different acceleration factors of SENSE and CS-SENSE. Gradient echo (GRE), autocalibration (CS-Auto) and TSE prescans were tested for reconstruction. Images were analysed by two readers regarding anatomical delineation, diagnostic certainty (for patients only) and image quality as well as objectively calculating the root mean square error (RMSE), structural similarity index (SSIM), SNR and CNR. The Friedman test and Chi-squared were used for ordinal, ANOVA for repeated measurements and Tukey Kramer test for continuous data. Cohen's kappawas calculated for interreader reliability.

Results: CS-SENSE outperformed SENSE and CS-Auto regarding RMSE (e.g. CS-SENSE 1.5: 43.03 ± 11.64 versus SENSE 1.5: 80.41 ± 17.66; p = 0.0038) and SSIM as well as in the subjective rating for CS-SENSE 3 TSE. In the patient setting image quality was unchanged in all subjective criteria up to CS-SENSE 3 TSE (all p > 0.05) compared to standard T2 with 43 % less scan time while the GRE prescan only allowed a reduction of 32 %.

Conclusion: Combining a TSE prescan with CS-SENSE enables significant scan time reductions with unchanged ratings for lumbar spine disc herniation making this superior to the currently used SENSE acceleration or GRE reconstructions.
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http://dx.doi.org/10.1016/j.ejrad.2021.109738DOI Listing
July 2021

[Imaging in the acute abdomen - part 1 : Case examples of frequent organ-specific causes: liver, gallbladder, pancreas, spleen and vessels].

Radiologe 2021 05 16;61(5):497-510. Epub 2021 Apr 16.

Medizinische Fakultät und Universitätsklinikum Köln, Institut für Diagnostische und Interventionelle Radiologie, Universität zu Köln, Kerpener Straße 62, 50937, Köln, Deutschland.

The acute abdomen is characterized by acute abdominal pain with defensive muscular tension, can be triggered by a variety of diseases and sometimes represents a life-threatening condition. After clinical inspection, in most cases dedicated imaging should be performed immediately. The frequently causal appendicitis and cholecystitis can mostly be diagnosed with ultrasound. In other cases with unclear ultrasound findings or unclear severe symptoms, computer tomography (CT) is usually necessary without delay. In contrast, magnetic resonance imaging (MRI) is predominantly indicated in pregnant women and children with unclear ultrasound findings. Thus, the radiologist is an important gatekeeper in the diagnostics of acute abdomen. The radiologist should therefore be familiar with the correct imaging indications, the frequent and rare causes as well as the corresponding morphological imaging characteristics.
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http://dx.doi.org/10.1007/s00117-021-00843-1DOI Listing
May 2021

Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.

BMC Med Imaging 2021 04 13;21(1):69. Epub 2021 Apr 13.

Institute of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Cologne, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.

Background: In oncology, the correct determination of nodal metastatic disease is essential for patient management, as patient treatment and prognosis are closely linked to the stage of the disease. The aim of the study was to develop a tool for automatic 3D detection and segmentation of lymph nodes (LNs) in computed tomography (CT) scans of the thorax using a fully convolutional neural network based on 3D foveal patches.

Methods: The training dataset was collected from the Computed Tomography Lymph Nodes Collection of the Cancer Imaging Archive, containing 89 contrast-enhanced CT scans of the thorax. A total number of 4275 LNs was segmented semi-automatically by a radiologist, assessing the entire 3D volume of the LNs. Using this data, a fully convolutional neuronal network based on 3D foveal patches was trained with fourfold cross-validation. Testing was performed on an unseen dataset containing 15 contrast-enhanced CT scans of patients who were referred upon suspicion or for staging of bronchial carcinoma.

Results: The algorithm achieved a good overall performance with a total detection rate of 76.9% for enlarged LNs during fourfold cross-validation in the training dataset with 10.3 false-positives per volume and of 69.9% in the unseen testing dataset. In the training dataset a better detection rate was observed for enlarged LNs compared to smaller LNs, the detection rate for LNs with a short-axis diameter (SAD) ≥ 20 mm and SAD 5-10 mm being 91.6% and 62.2% (p < 0.001), respectively. Best detection rates were obtained for LNs located in Level 4R (83.6%) and Level 7 (80.4%).

Conclusions: The proposed 3D deep learning approach achieves an overall good performance in the automatic detection and segmentation of thoracic LNs and shows reasonable generalizability, yielding the potential to facilitate detection during routine clinical work and to enable radiomics research without observer-bias.
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http://dx.doi.org/10.1186/s12880-021-00599-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045346PMC
April 2021

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data.

NPJ Digit Med 2021 Apr 12;4(1):69. Epub 2021 Apr 12.

Computational Radiology, Dept. of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint Lesion software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.
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http://dx.doi.org/10.1038/s41746-021-00439-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041811PMC
April 2021

Reduction of CT artifacts from cardiac implantable electronic devices using a combination of virtual monoenergetic images and post-processing algorithms.

Eur Radiol 2021 Sep 25;31(9):7151-7161. Epub 2021 Feb 25.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Objectives: To evaluate the reduction of artifacts from cardiac implantable electronic devices (CIEDs) by virtual monoenergetic images (VMI), metal artifact reduction (MAR) algorithms, and their combination (VMI) derived from spectral detector CT (SDCT) of the chest compared to conventional CT images (CI).

Methods: In this retrospective study, we included 34 patients (mean age 74.6 ± 8.6 years), who underwent a SDCT of the chest and had a CIED in place. CI, MAR, VMI, and VMI (10 keV increment, range: 100-200 keV) were reconstructed. Mean and standard deviation of attenuation (HU) among hypo- and hyperdense artifacts adjacent to CIED generator and leads were determined using ROIs. Two radiologists qualitatively evaluated artifact reduction and diagnostic assessment of adjacent tissue.

Results: Compared to CI, MAR and VMI ≥ 100 keV significantly increased attenuation in hypodense and significantly decreased attenuation in hyperdense artifacts at CIED generator and leads (p < 0.05). VMI ≥ 100 keV alone only significantly decreased hyperdense artifacts at the generator (p < 0.05). Qualitatively, VMI ≥ 100 keV, MAR, and VMI ≥ 100 keV provided significant reduction of hyper- and hypodense artifacts resulting from the generator and improved diagnostic assessment of surrounding structures (p < 0.05). Diagnostic assessment of structures adjoining to the leads was only improved by MAR and VMI 100 keV (p < 0.05), whereas keV values ≥ 140 with and without MAR significantly worsened diagnostic assessment (p < 0.05).

Conclusions: The combination of VMI and MAR as well as MAR as a standalone approach provides effective reduction of artifacts from CIEDs. Still, higher keV values should be applied with caution due to a loss of soft tissue and vessel contrast along the leads.

Key Points: • The combination of VMI and MAR as well as MAR as a standalone approach enables effective reduction of artifacts from CIEDs. • Higher keV values of both VMI and VMI at CIED leads should be applied with caution since diagnostic assessment can be hampered by a loss of soft tissue and vessel contrast. • Recommended keV values for CIED generators are between 140 and 200 keV and for leads around 100 keV.
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http://dx.doi.org/10.1007/s00330-021-07746-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8379133PMC
September 2021

Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives.

J Med Internet Res 2021 02 17;23(2):e24221. Epub 2021 Feb 17.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Background: Artificial intelligence (AI) is gaining increasing importance in many medical specialties, yet data on patients' opinions on the use of AI in medicine are scarce.

Objective: This study aimed to investigate patients' opinions on the use of AI in different aspects of the medical workflow and the level of control and supervision under which they would deem the application of AI in medicine acceptable.

Methods: Patients scheduled for computed tomography or magnetic resonance imaging voluntarily participated in an anonymized questionnaire between February 10, 2020, and May 24, 2020. Patient information, confidence in physicians vs AI in different clinical tasks, opinions on the control of AI, preference in cases of disagreement between AI and physicians, and acceptance of the use of AI for diagnosing and treating diseases of different severity were recorded.

Results: In total, 229 patients participated. Patients favored physicians over AI for all clinical tasks except for treatment planning based on current scientific evidence. In case of disagreement between physicians and AI regarding diagnosis and treatment planning, most patients preferred the physician's opinion to AI (96.2% [153/159] vs 3.8% [6/159] and 94.8% [146/154] vs 5.2% [8/154], respectively; P=.001). AI supervised by a physician was considered more acceptable than AI without physician supervision at diagnosis (confidence rating 3.90 [SD 1.20] vs 1.64 [SD 1.03], respectively; P=.001) and therapy (3.77 [SD 1.18] vs 1.57 [SD 0.96], respectively; P=.001).

Conclusions: Patients favored physicians over AI in most clinical tasks and strongly preferred an application of AI with physician supervision. However, patients acknowledged that AI could help physicians integrate the most recent scientific evidence into medical care. Application of AI in medicine should be disclosed and controlled to protect patient interests and meet ethical standards.
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http://dx.doi.org/10.2196/24221DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929746PMC
February 2021

Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules.

Cancer Imaging 2021 Jan 26;21(1):17. Epub 2021 Jan 26.

Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.

Background: The purpose of this study was to analyze if the use of texture analysis on spectral detector CT (SDCT)-derived iodine maps (IM) in addition to conventional images (CI) improves lung nodule differentiation, when being applied to a k-nearest neighbor (KNN) classifier.

Methods: 183 cancer patients who underwent contrast-enhanced, venous phase SDCT of the chest were included: 85 patients with 146 benign lung nodules (BLN) confirmed by either prior/follow-up CT or histopathology and 98 patients with 425 lung metastases (LM) verified by histopathology, F-FDG-PET-CT or unequivocal change during treatment. Semi-automatic 3D segmentation of BLN/LM was performed, and volumetric HU attenuation and iodine concentration were acquired. For conventional images and iodine maps, average, standard deviation, entropy, kurtosis, mean of the positive pixels (MPP), skewness, uniformity and uniformity of the positive pixels (UPP) within the volumes of interests were calculated. All acquired parameters were transferred to a KNN classifier.

Results: Differentiation between BLN and LM was most accurate, when using all CI-derived features combined with the most significant IM-derived feature, entropy (Accuracy:0.87; F1/Dice:0.92). However, differentiation accuracy based on the 4 most powerful CI-derived features performed only slightly inferior (Accuracy:0.84; F1/Dice:0.89, p=0.125). Mono-parametric lung nodule differentiation based on either feature alone (i.e. attenuation or iodine concentration) was poor (AUC=0.65, 0.58, respectively).

Conclusions: First-order texture feature analysis of contrast-enhanced staging SDCT scans of the chest yield accurate differentiation between benign and metastatic lung nodules. In our study cohort, the most powerful iodine map-derived feature slightly, yet insignificantly increased classification accuracy  compared to classification based on conventional image features only.
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http://dx.doi.org/10.1186/s40644-020-00374-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836145PMC
January 2021

An Autochthonous Mouse Model of Myd88- and BCL2-Driven Diffuse Large B-cell Lymphoma Reveals Actionable Molecular Vulnerabilities.

Blood Cancer Discov 2021 Jan;2(1):70-91

Center for Integrated Oncology, University of Cologne, Cologne, Germany.

Based on gene expression profiles, diffuse large B cell lymphoma (DLBCL) is sub-divided into germinal center B cell-like (GCB) and activated B cell-like (ABC) DLBCL. Two of the most common genomic aberrations in ABC-DLBCL are mutations in , as well as copy number gains. Here, we employ immune phenotyping, RNA-Seq and whole exome sequencing to characterize a and -driven mouse model of ABC-DLBCL. We show that this model resembles features of human ABC-DLBCL. We further demonstrate an actionable dependence of our murine ABC-DLBCL model on BCL2. This BCL2 dependence was also detectable in human ABC-DLBCL cell lines. Moreover, human ABC-DLBCLs displayed increased expression, compared to GCB-DLBCL. experiments in our ABC-DLBCL model showed that combined venetoclax and RMP1-14 significantly increased the overall survival of lymphoma bearing animals, indicating that this combination may be a viable option for selected human ABC-DLBCL cases harboring and aberrations.
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http://dx.doi.org/10.1158/2643-3230.BCD-19-0059DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806186PMC
January 2021

Lymph Node Assessment in Prostate Cancer: Evaluation of Iodine Quantification With Spectral Detector CT in Correlation to PSMA PET/CT.

Clin Nucl Med 2021 04;46(4):303-309

From the Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Cologne and University Hospital Cologne, Cologne, Germany.

Purpose: The aims of this study were to evaluate spectral detector CT (SDCT)-derived iodine concentration (IC) of lymph nodes diagnosed as metastatic and benign in prostate-specific membrane antigen (PSMA) PET/CT and to assess its potential use for lymph node assessment in prostate cancer.

Patients And Methods: Thirty-four prostate cancer patients were retrospectively included: 16 patients with and 18 without lymph node metastases as determined by PSMA PET/CT. Patients underwent PSMA PET/CT as well as portal venous phase abdominal SDCT for clinical cancer follow-up. Only scan pairs with a stable nodal status indicated by constant size as well as comparable prostate-specific antigen (PSA) levels were included. One hundred benign and 96 suspected metastatic lymph nodes were annotated and correlated between SDCT and PSMA PET/CT. Iodine concentration in SDCT-derived iodine maps and SUVmax in ultra-high definition reconstructions from PSMA PET/CT were acquired based on the region of interest.

Results: Metastatic lymph nodes as per PSMA PET/CT showed higher IC than nonmetastatic nodes (1.9 ± 0.6 mg/mL vs 1.5 ± 0.5 mg/mL, P < 0.05) resulting in an AUC of 0.72 and sensitivity/specificity of 81.3%/58.5%. The mean short axis diameter of metastatic lymph nodes was larger than that of nonmetastatic nodes (6.9 ± 3.6 mm vs 5.3 ± 1.3 mm; P < 0.05); a size threshold of 1 cm short axis diameter resulted in a sensitivity/specificity of 12.8%/99.0%. There was a significant yet weak positive correlation between SUVmax and IC (rs = 0.25; P < 0.001).

Conclusions: Spectral detector CT-derived IC was increased in lymph nodes diagnosed as metastatic in PSMA PET/CT yet showed considerable data overlap. The correlation between IC and SUVmax was weak, highlighting the role of PSMA PET/CT as important reference imaging modality for detection of lymph node metastases in prostate cancer patients.
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http://dx.doi.org/10.1097/RLU.0000000000003496DOI Listing
April 2021

Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19.

PLoS One 2020 23;15(12):e0244267. Epub 2020 Dec 23.

Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany.

Background: Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation of the arterial vessels. The amount of aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification of AWC on the low-dose chest CT, which is initially performed for the diagnosis of COVID-19, to screen for patients at risk of severe COVID-19.

Methods: Seventy consecutive patients (46 in center 1, 24 in center 2) with parallel low-dose chest CT and positive RT-PCR for SARS-CoV-2 were included in our multi-center, multi-vendor study. The outcome was rated moderate (no hospitalization, hospitalization) and severe (ICU, tracheal intubation, death), the latter implying a requirement for intensive care treatment. The amount of AWC was quantified with the CT vendor's software.

Results: Of 70 included patients, 38 developed a moderate, and 32 a severe COVID-19. The average volume of AWC was significantly higher throughout the subgroup with severe COVID-19, when compared to moderate cases (771.7 mm3 (Q1 = 49.8 mm3, Q3 = 3065.5 mm3) vs. 0 mm3 (Q1 = 0 mm3, Q3 = 57.3 mm3)). Within multivariate regression analysis, including AWC, patient age and sex, as well as a cardiovascular comorbidity score, the volume of AWC was the only significant regressor for severe COVID-19 (p = 0.004). For AWC > 3000 mm3, the logistic regression predicts risk for a severe progression of 0.78. If there are no visually detectable AWC risk for severe progression is 0.13, only.

Conclusion: AWC seems to be an independent biomarker for the prediction of severe progression and intensive care treatment of COVID-19 already at the time of patient admission to the hospital; verification in a larger multi-center, multi-vendor study is desired.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0244267PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757863PMC
January 2021

Defining and managing COVID-19-associated pulmonary aspergillosis: the 2020 ECMM/ISHAM consensus criteria for research and clinical guidance.

Lancet Infect Dis 2021 06 14;21(6):e149-e162. Epub 2020 Dec 14.

Faculty of Medicine, University of Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany; Department I of Internal Medicine, European Excellence Center for Medical Mycology (ECMM), University Hospital Cologne, Cologne, Germany; Clinical Trials Centre Cologne, ZKS Köln, Cologne, Germany; German Center for Infection Research, Partner Site Bonn-Cologne, Cologne, Germany. Electronic address:

Severe acute respiratory syndrome coronavirus 2 causes direct damage to the airway epithelium, enabling aspergillus invasion. Reports of COVID-19-associated pulmonary aspergillosis have raised concerns about it worsening the disease course of COVID-19 and increasing mortality. Additionally, the first cases of COVID-19-associated pulmonary aspergillosis caused by azole-resistant aspergillus have been reported. This article constitutes a consensus statement on defining and managing COVID-19-associated pulmonary aspergillosis, prepared by experts and endorsed by medical mycology societies. COVID-19-associated pulmonary aspergillosis is proposed to be defined as possible, probable, or proven on the basis of sample validity and thus diagnostic certainty. Recommended first-line therapy is either voriconazole or isavuconazole. If azole resistance is a concern, then liposomal amphotericin B is the drug of choice. Our aim is to provide definitions for clinical research and up-to-date recommendations for clinical management of the diagnosis and treatment of COVID-19-associated pulmonary aspergillosis.
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http://dx.doi.org/10.1016/S1473-3099(20)30847-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7833078PMC
June 2021

Clinical application of free-breathing 3D whole heart late gadolinium enhancement cardiovascular magnetic resonance with high isotropic spatial resolution using Compressed SENSE.

J Cardiovasc Magn Reson 2020 12 17;22(1):89. Epub 2020 Dec 17.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) represents the gold standard for assessment of myocardial viability. The purpose of this study was to investigate the clinical potential of Compressed SENSE (factor 5) accelerated free-breathing three-dimensional (3D) whole heart LGE with high isotropic spatial resolution (1.4 mm acquired voxel size) compared to standard breath-hold LGE imaging.

Methods: This was a retrospective, single-center study of 70 consecutive patients (45.8 ± 18.1 years, 27 females; February-November 2019), who were referred for assessment of left ventricular myocardial viability and received free-breathing and breath-hold LGE sequences at 1.5 T in clinical routine. Two radiologists independently evaluated global and segmental LGE in terms of localization and transmural extent. Readers scored scans regarding image quality (IQ), artifacts, and diagnostic confidence (DC) using 5-point scales (1 non-diagnostic-5 excellent/none). Effects of heart rate and body mass index (BMI) on IQ, artifacts, and DC were evaluated with ordinal logistic regression analysis.

Results: Global LGE (n = 33) was identical for both techniques. Using free-breathing LGE (average scan time: 04:33 ± 01:17 min), readers detected more hyperenhanced lesions (28.2% vs. 23.5%, P < .05) compared to breath-hold LGE (05:15 ± 01:23 min, P = .0104), pronounced at subepicardial localization and for 1-50% of transmural extent. For free-breathing LGE, readers graded scans with good/excellent IQ in 80.0%, with low-impact/no artifacts in 78.6%, and with good/high DC in 82.1% of cases. Elevated BMI was associated with increased artifacts (P = .0012) and decreased IQ (P = .0237). Increased heart rate negatively influenced artifacts (P = .0013) and DC (P = .0479) whereas IQ (P = .3025) was unimpaired.

Conclusions: In a clinical setting, free-breathing Compressed SENSE accelerated 3D high isotropic spatial resolution whole heart LGE provides good to excellent image quality in 80% of scans independent of heart rate while enabling improved depiction of small and particularly non-ischemic hyperenhanced lesions in a shorter scan time than standard breath-hold LGE.
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http://dx.doi.org/10.1186/s12968-020-00673-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7745391PMC
December 2020

Value of spectral detector CT for pretherapeutic, locoregional assessment of esophageal cancer.

Eur J Radiol 2021 Jan 21;134:109423. Epub 2020 Nov 21.

University Cologne, Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, Kerpener Straße 62, 50937, Cologne, Germany; Department of Radiology, Division of Abdominal Imaging and Intervention, Massachusetts General Hospital, Harvard Medical School, 55 Fruit St, White 270, Boston, MA, 02114, USA. Electronic address:

Purpose: To investigate the diagnostic value of spectral detector dual-energy CT-derived low-keV virtual monoenergetic images (VMI) and iodine overlays (IO) for locoregional, pretherapeutic assessment of esophageal cancer.

Method: 74 patients with biopsy-proven esophageal cancer who underwent pre-therapeutic, portal-venous-phase staging examinations of the chest and abdomen were retrospectively included. Quantitative image analysis was performed ROI-based within the tumor, healthy esophageal wall, peri-esophageal lymph nodes, azygos vein, aorta, liver, diaphragm, and mediastinal fat. Two radiologists evaluated delineation of the primary tumor and locoregional lymph nodes, assessment of the celiac trunk and diagnostic certainty regarding tumor infiltration in conventional images (CI), VMI from 40 to 70 keV and IO. Moreover, presence/absence of advanced tumor infiltration (T3/T4) was determined binary using all available images.

Results: VMI showed significantly higher attenuation and signal-to-noise ratio compared to CI for all assessed ROIs, peaking at VMI (p < 0.05). Contrast-to-noise ratio of tumor/esophagus (VMI/CI: 7.7 ± 4.7 vs. 2.3 ± 1.5), tumor/diaphragm (VMI/CI: 9.0 ± 5.5 vs. 2.2 ± 1.7) and tumor/liver (4.3 ± 5.5 vs. 1.9 ± 2.1) were all significantly higher compared to CI (p < 0.05). Qualitatively, lymph node delineation and diagnostic certainty regarding tumor infiltration received highest ratings both in IO and VMI, whereas vascular assessment was rated highest in VMI and primary tumor delineation in IO. Sensitivity/Specificity/Accuracy for detecting advanced tumor infiltration using the combination of CI, VMI and IO was 42.4 %/82.0 %/56.3 %.

Conclusions: IO and VMI improve qualitative assessment of the primary tumor and depiction of lymph nodes and vessels at pretherapeutic SDCT of esophageal cancer patients yet do not mitigate the limitations of CT in determining tumor infiltration.
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http://dx.doi.org/10.1016/j.ejrad.2020.109423DOI Listing
January 2021

Early extrapulmonary prognostic features in chest computed tomography in COVID-19 pneumonia: Bone mineral density is a relevant predictor for the clinical outcome - A multicenter feasibility study.

Bone 2021 03 7;144:115790. Epub 2020 Dec 7.

Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany. Electronic address:

Background: Besides throat-nose swab polymerase chain reaction (PCR), unenhanced chest computed tomography (CT) is a recommended diagnostic tool for early detection and quantification of pulmonary changes in COVID-19 pneumonia caused by the novel corona virus. Demographic factors, especially age and comorbidities, are major determinants of the outcome in COVID-19 infection. This study examines the extra pulmonary parameter of bone mineral density (BMD) from an initial chest computed tomography as an associated variable of pre-existing comorbidities like chronic lung disease or demographic factors to determine the later patient's outcome, in particular whether treatment on an intensive care unit (ICU) was necessary in infected patients.

Methods: We analyzed 58 PCR-confirmed COVID-19 infections that received an unenhanced CT at admission at one of the included centers. In addition to the extent of pulmonary involvement, we performed a phantomless assessment of bone mineral density of thoracic vertebra 9-12.

Results: In a univariate regression analysis BMD was found to be a significant predictor of the necessity for intensive care unit treatment of COVID-19 patients. In the subgroup requiring intensive care treatment within the follow-up period a significantly lower BMD was found. In a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor.

Conclusion: Phantomless assessed BMD provides prognostic information on the necessity for ICU treatment in course of COVID-19 pneumonia. We recommend using the measurement of BMD in an initial CT image to facilitate a potentially better prediction of severe patient outcomes within the 22 days after an initial CT scan. Consequently, in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects.

Clinical Relevance: While clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future.
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http://dx.doi.org/10.1016/j.bone.2020.115790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720732PMC
March 2021

Quantitative accuracy of virtual non-contrast images derived from spectral detector computed tomography: an abdominal phantom study.

Sci Rep 2020 12 9;10(1):21575. Epub 2020 Dec 9.

Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Strasse 62, 50937, Cologne, Germany.

Dual-energy CT allows for the reconstruction of virtual non-contrast (VNC) images. VNC images have the potential to replace true non-contrast scans in various clinical applications. This study investigated the quantitative accuracy of VNC attenuation images considering different parameters for acquisition and reconstruction. An abdomen phantom with 7 different tissue types (different combinations of 3 base materials and 5 iodine concentrations) was scanned using a spectral detector CT (SDCT). Different phantom sizes (S, M, L), volume computed tomography dose indices (CTDIvol 10, 15, 20 mGy), kernel settings (soft, standard, sharp), and denoising levels (low, medium, high) were tested. Conventional and VNC images were reconstructed and analyzed based on regions of interest (ROI). Mean and standard deviation were recorded and differences in attenuation between corresponding base materials and VNC was calculated (VNCerror). Statistic analysis included ANOVA, Wilcoxon test and multivariate regression analysis. Overall, the VNC was - 1.4 ± 6.1 HU. While radiation dose, kernel setting, and denoising level did not influence VNC significantly, phantom size, iodine content and base material had a significant effect (e.g. S vs. M: - 1.2 ± 4.9 HU vs. - 2.1 ± 6.0 HU; 0.0 mg/ml vs. 5.0 mg/ml: - 4.0 ± 3.5 HU vs. 5.1 ± 5.0 HU and 35-HU-base vs. 54-HU-base: - 3.5 ± 4.4 HU vs. 0.7 ± 6.5; all p ≤ 0.05). The overall accuracy of VNC images from SDCT is high and independent from dose, kernel, and denoising settings; however, shows a dependency on patient size, base material, and iodine content; particularly the latter results in small, yet, noticeable differences in VNC attenuation.
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http://dx.doi.org/10.1038/s41598-020-78518-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7725817PMC
December 2020

Relaxation-Enhanced Angiography Without Contrast and Triggering (REACT) for Fast Imaging of Extracranial Arteries in Acute Ischemic Stroke at 3 T.

Clin Neuroradiol 2021 Sep 7;31(3):815-826. Epub 2020 Oct 7.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Purpose: To evaluate a novel flow-independent 3D isotropic REACT sequence compared with CE-MRA for the imaging of extracranial arteries in acute ischemic stroke (AIS).

Methods: This was a retrospective study of 35 patients who underwent a stroke protocol at 3 T including REACT (fixed scan time: 2:46 min) and CE-MRA of the extracranial arteries. Three radiologists evaluated scans regarding vessel delineation, signal, and contrast and assessed overall image noise and artifacts using 5-point scales (5: excellent delineation/no artifacts). Apparent signal- and contrast-to-noise ratios (aSNR/aCNR) were measured for the common carotid artery (CCA), internal carotid artery (ICA, C1 segment), and vertebral artery (V2 segment). Two radiologists graded the degree of proximal ICA stenosis.

Results: Compared to REACT, CE-MRA showed better delineation for the CCA and ICA (C1 and C2 segments) (median 5, range 2-5 vs. 4, range 3-5; P < 0.05). For the ICA (C1 and C2 segments), REACT provided a higher signal (5, range 3-5; P < 0.05/4.5, range 3-5; P > 0.05 vs. 4, range 2-5) and contrast (5, range 3-5 vs. 4, range 2-5; P > 0.05) than CE-MRA. The remaining segments of the blood-supplying vessels showed equal medians. There was no significant difference regarding artifacts, whereas REACT provided significantly lower image noise (4, range 3-5 vs. 4 range 2-5; P < 0.05) with a higher aSNR (P < 0.05) and aCNR (P < 0.05) for all vessels combined. For clinically relevant (≥50%) ICA stenosis, REACT achieved a detection sensitivity of 93.75% and a specificity of 100%.

Conclusion: Given its fast acquisition, comparable image quality to CE-MRA and high sensitivity and specificity for the detection of ICA stenosis, REACT was proven to be a clinically applicable method to assess extracranial arteries in AIS.
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http://dx.doi.org/10.1007/s00062-020-00963-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8463375PMC
September 2021

Magnetic Resonance Imaging-Guided Transurethral Ultrasound Ablation of Prostate Cancer.

J Urol 2021 Mar 6;205(3):769-779. Epub 2020 Oct 6.

University of Chicago.

Purpose: Magnetic resonance imaging-guided transurethral ultrasound ablation uses directional thermal ultrasound under magnetic resonance imaging thermometry feedback control for prostatic ablation. We report 12-month outcomes from a prospective multicenter trial (TACT).

Materials And Methods: A total of 115 men with favorable to intermediate risk prostate cancer across 13 centers were treated with whole gland ablation sparing the urethra and apical sphincter. The co-primary 12-month endpoints were safety and efficacy.

Results: In all, 72 (63%) had grade group 2 and 77 (67%) had NCCN® intermediate risk disease. Median treatment delivery time was 51 minutes with 98% (IQR 95-99) thermal coverage of target volume and spatial ablation precision of ±1.4 mm on magnetic resonance imaging thermometry. Grade 3 adverse events occurred in 9 (8%) men. The primary endpoint (U.S. Food and Drug Administration mandated) of prostate specific antigen reduction ≥75% was achieved in 110 of 115 (96%) with median prostate specific antigen reduction of 95% and nadir of 0.34 ng/ml. Median prostate volume decreased from 37 to 3 cc. Among 68 men with pretreatment grade group 2 disease, 52 (79%) were free of grade group 2 disease on 12-month biopsy. Of 111 men with 12-month biopsy data, 72 (65%) had no evidence of cancer. Erections (International Index of Erectile Function question 2 score 2 or greater) were maintained/regained in 69 of 92 (75%). Multivariate predictors of persistent grade group 2 at 12 months included intraprostatic calcifications at screening, suboptimal magnetic resonance imaging thermal coverage of target volume and a PI-RADS™ 3 or greater lesion at 12-month magnetic resonance imaging (p <0.05).

Conclusions: The TACT study of magnetic resonance imaging-guided transurethral ultrasound whole gland ablation in men with localized prostate cancer demonstrated effective tissue ablation and prostate specific antigen reduction with low rates of toxicity and residual disease.
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http://dx.doi.org/10.1097/JU.0000000000001362DOI Listing
March 2021

Quantitative distribution of iodinated contrast media in body computed tomography: data from a large reference cohort.

Eur Radiol 2021 Apr 30;31(4):2340-2348. Epub 2020 Sep 30.

University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany.

Objectives: Dual-energy computed tomography allows for an accurate and reliable quantification of iodine. However, data on physiological distribution of iodine concentration (IC) is still sparse. This study aims to establish guidance for IC in abdominal organs and important anatomical landmarks using a large cohort of individuals without radiological tumor burden.

Methods: Five hundred seventy-one oncologic, portal venous phase dual-layer spectral detector CT studies of the chest and abdomen without tumor burden at time point of imaging confirmed by > 3-month follow-up were included. ROI were placed in parenchymatous organs (n = 25), lymph nodes (n = 6), and vessels (n = 3) with a minimum of two measurements per landmark. ROI were placed on conventional images and pasted to iodine maps to retrieve absolute IC. Normalization to the abdominal aorta was conducted to obtain iodine perfusion ratios. Bivariate regression analysis, t tests, and ANOVA with Tukey-Kramer post hoc test were used for statistical analysis.

Results: Absolute IC showed a broad scatter and varied with body mass index, between different age groups and between the sexes in parenchymatous organs, lymph nodes, and vessels (range 0.0 ± 0.0 mg/ml-6.6 ± 1.3 mg/ml). Unlike absolute IC, iodine perfusion ratios did not show dependency on body mass index; however, significant differences between the sexes and age groups persisted, showing a tendency towards decreased perfusion ratios in elderly patients (e.g., liver 18-44 years/≥ 64 years: 0.50 ± 0.11/0.43 ± 0.10, p ≤ 0.05).

Conclusions: Distribution of IC obtained from a large-scale cohort is provided. As significant differences between sexes and age groups were found, this should be taken into account when obtaining quantitative iodine concentrations and applying iodine thresholds.

Key Points: • Absolute iodine concentration showed a broad variation and differed between body mass index, age groups, and between the sexes in parenchymatous organs, lymph nodes, and vessels. • The iodine perfusion ratios did not show dependency on body mass index while significant differences between sexes and age groups persisted. • Provided guidance values may serve as reference when aiming to differentiate healthy and abnormal tissue based on iodine perfusion ratios.
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http://dx.doi.org/10.1007/s00330-020-07298-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7979665PMC
April 2021

Body composition on low dose chest CT is a significant predictor of poor clinical outcome in COVID-19 disease - A multicenter feasibility study.

Eur J Radiol 2020 Nov 9;132:109274. Epub 2020 Sep 9.

Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany. Electronic address:

Purpose: Low-dose computed tomography (LDCT) of the chest is a recommended diagnostic tool in early stage of COVID-19 pneumonia. High age, several comorbidities as well as poor physical fitness can negatively influence the outcome within COVID-19 infection. We investigated whether the ratio of fat to muscle area, measured in initial LDCT, can predict severe progression of COVID-19 in the follow-up period.

Method: We analyzed 58 individuals with confirmed COVID-19 infection that underwent an initial LDCT in one of two included centers due to COVID-19 infection. Using the ratio of waist circumference per paravertebral muscle circumference (FMR), the body composition was estimated. Patient outcomes were rated on an ordinal scale with higher numbers representing more severe progression or disease associated complications (hospitalization/ intensive care unit (ICU)/ tracheal intubation/ death) within a follow-up period of 22 days after initial LDCT.

Results: In the initial LDCT a significantly higher FMR was found in patients requiring intensive care treatment within the follow-up period. In multivariate logistic regression analysis, FMR (p < .001) in addition to age (p < .01), was found to be a significant predictor of the necessity for ICU treatment of COVID-19 patients.

Conclusion: FMR as potential surrogate of body composition and obesity can be easily determined in initial LDCT of COVID-19 patients. Within the multivariate analysis, in addition to patient age, low muscle area in proportion to high fat area represents an additional prognostic information for the patient outcome and the need of an ICU treatment during the follow-up period within the next 22 days. This multicentric pilot study presents a method using an initial LDCT to screen opportunistically for obese patients who have an increased risk for the need of ICU treatment. While clinical capacities, such as ICU beds and ventilators, are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used for a cost-effective way to help determine the amount of these rare clinical resources required in the near future.
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http://dx.doi.org/10.1016/j.ejrad.2020.109274DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7480673PMC
November 2020

Intraindividual Consistency of Iodine Concentration in Dual-Energy Computed Tomography of the Chest and Abdomen.

Invest Radiol 2021 03;56(3):181-187

From the Faculty of Medicine, University Cologne, and Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Germany.

Objectives: Dual-energy computed tomography (DECT)-derived quantification of iodine concentration (IC) is increasingly used in oncologic imaging to characterize lesions and evaluate treatment response. However, only limited data are available on intraindividual consistency of IC and its variation. This study investigates the longitudinal reproducibility of IC in organs, vessels, and lymph nodes in a large cohort of healthy patients who underwent repetitive DECT imaging.

Materials And Methods: A total of 159 patients, who underwent a total of 469 repetitive (range, 2-4), clinically indicated portal-venous phase DECT examinations of the chest and abdomen, were retrospectively included. At time of imaging, macroscopic tumor burden was excluded by follow-up imaging (≥3 months). Iodine concentration was measured region of interest-based (N = 43) in parenchymatous organs, vessels, lymph nodes, and connective tissue. Normalization of IC to the aorta and to the trigger delay as obtained from bolus tracking was performed. For statistical analysis, intraclass correlation coefficient and modified variation coefficient (MVC) were used to assess intraindividual agreement of IC and its variation between different time points, respectively. Furthermore, t tests and analysis of variance with Tukey-Kramer post hoc test were used.

Results: The mean intraclass correlation coefficient over all regions of interest was good to excellent (0.642-0.936), irrespective of application of normalization or the normalization technique. Overall, MVC ranged from 1.8% to 25.4%, with significantly lower MVC in data normalized to the aorta (5.8% [1.8%-15.8%]) in comparison with the MVC of not normalized data and data normalized to the trigger delay (P < 0.01 and P = 0.04, respectively).

Conclusions: Our study confirms intraindividual, longitudinal variation of DECT-derived IC, which varies among vessels, lymph nodes, organs, and connective tissue, following different perfusion characteristics; normalizing to the aorta seems to improve reproducibility when using a constant contrast media injection protocol.
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http://dx.doi.org/10.1097/RLI.0000000000000724DOI Listing
March 2021

[Does SARS-CoV-2 cause lung inflammation even in mild clinical courses? : A multicenter report from outpatient care].

Radiologe 2020 Oct;60(10):943-948

Rhein-Maas Klinikum Würselen, Würselen, Deutschland.

Objective: In spring 2020 imaging findings of the lungs were found in several radiological practices and in outpatient clinic patients, which indicated acute or previous viral pneumonia. It was striking that many of the patients affected had only mild symptoms. In this case study it was investigated to what extent SARS-CoV‑2 can cause lung involvement even with minor symptoms.

Material And Methods: In this study five outpatient radiological centers and two inpatient hospitals in North Rhine-Westphalia and Baden-Württemberg in Germany were involved. The retrospective analysis included outpatients with radiologically detected viral pneumonia, who were examined in March or April 2020. The clinical symptoms were divided into severity levels 1-5 using a simplified clinical score. The lung images were evaluated with respect to features specific for COVID-19 . The presence of a SARS-CoV‑2 infection was verified using PCR, antibody testing and/or typical computed tomography (CT) morphology.

Results: A total of 50 patients were included, all of whom had radiological signs of viral pneumonia. The majority had no or only few non-specific symptoms (26/50). This was followed by mild symptoms of a flu-like infection (17/50). Severe forms were rare in outpatients (7/50). Detection of COVID-19 was successful in 30/50 cases using PCR and in 4/50 cases using an antibody test. In 16/50 cases the diagnosis was based on typical CT criteria and on the typical COVID patient history.

Conclusion: A SARS-CoV‑2 infection leads to lung involvement more often than previously assumed, namely not only in severely ill hospitalized patients but also in cases with only mild or even non-specific symptoms.
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http://dx.doi.org/10.1007/s00117-020-00746-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471637PMC
October 2020

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

J Magn Reson Imaging 2021 01 13;53(1):259-268. Epub 2020 Jul 13.

Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.

Background: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-consuming and impeded by intra- and interrater variabilities.

Purpose: To investigate the performance of a deep-learning model (DLM) to automatically detect and segment primary central nervous system lymphoma (PCNSL) on clinical MRI.

Study Type: Retrospective.

Population: Sixty-nine scans (at initial and/or follow-up imaging) from 43 patients with PCNSL referred for clinical MRI tumor assessment.

Field Strength/sequence: T -/T -weighted, T -weighted contrast-enhanced (T CE), and FLAIR at 1.0, 1.5, and 3.0T from different vendors and study centers.

Assessment: Fully automated voxelwise segmentation of tumor components was performed using a 3D convolutional neural network (DeepMedic) trained on gliomas (n = 220). DLM segmentations were compared to manual segmentations performed in a 3D voxelwise manner by two readers (radiologist and neurosurgeon; consensus reading) from T CE and FLAIR, which served as the reference standard.

Statistical Tests: Dice similarity coefficient (DSC) for comparison of spatial overlap with the reference standard, Pearson's correlation coefficient (r) to assess the relationship between volumetric measurements of segmentations, and Wilcoxon rank-sum test for comparison of DSCs obtained in initial and follow-up imaging.

Results: The DLM detected 66 of 69 PCNSL, representing a sensitivity of 95.7%. Compared to the reference standard, DLM achieved good spatial overlap for total tumor volume (TTV, union of tumor volume in T CE and FLAIR; average size 77.16 ± 62.4 cm , median DSC: 0.76) and tumor core (contrast enhancing tumor in T CE; average size: 11.67 ± 13.88 cm , median DSC: 0.73). High volumetric correlation between automated and manual segmentations was observed (TTV: r = 0.88, P < 0.0001; core: r = 0.86, P < 0.0001). Performance of automated segmentations was comparable between pretreatment and follow-up scans without significant differences (TTV: P = 0.242, core: P = 0.177).

Data Conclusion: In clinical MRI scans, a DLM initially trained on gliomas provides segmentation of PCNSL comparable to manual segmentation, despite its complex and multifaceted appearance. Segmentation performance was high in both initial and follow-up scans, suggesting its potential for application in longitudinal tumor imaging.

Level Of Evidence: 3 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27288DOI Listing
January 2021

Virtual monoenergetic images preserve diagnostic assessability in contrast media reduced abdominal spectral detector CT.

Br J Radiol 2020 Sep 24;93(1113):20200340. Epub 2020 Jul 24.

Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpener Straße 62, 50937, Cologne, Germany.

Objectives: To investigate if low-keV virtual monoenergetic images (VMI) from abdominal spectral detector CT (SDCT) with reduced intravenous contrast media application (RCM) provide abdominal assessment similar to conventional images with standard contrast media (SCM) dose.

Methods: 78 patients with abdominal SDCT were retrospectively included: 41 patients at risk for adverse reactions who received 44 RCM examinations with 50 ml and 37 patients who underwent 44 SCM examinations with 100 ml of contrast media (CM) and who were matched for effective body diameters. RCM, SCM images and RCM-VMI were reconstructed. Attenuation and signal-to-noise ratio (SNR) of liver, pancreas, kidneys, lymph nodes, psoas muscle, aorta and portal vein were assessed ROIs-based. Contrast-to-noise ratios (CNR) of lymph nodes aorta/portal vein were calculated. Two readers evaluated organ/vessel contrast, lymph node delineation, image noise and overall assessability using 4-point Likert scales.

Results: RCM were inferior to SCM images in all quantitative/qualitative criteria. RCM-VMI and SCM images showed similar lymph node and muscle attenuation ( = 0.83,0.17), while for all other ROIs, RCM-VMI showed higher attenuation ( ≤ 0.05). SNR was comparable between RCM-VMI and SCM images (p range: 0.23-0.99). CNR of lymph nodes was highest in RCM-VMI ( ≤ 0.05). RCM-VMI received equivalent or higher scores than SCM in all criteria except for organ contrast, overall assessability and image noise, where SCM were superior ( ≤ 0.05). However, RCM-VMI received proper or excellent scores in 88.6/94.2/95.4% of the referring cases.

Conclusions: VMI counteract contrast deterioration in CM reduced abdominal SDCT, facilitating diagnostic assessment.

Advances In Knowledge: SDCT-derived VMI provide adequate depiction of vessels, organs and lymph nodes even at notable CM reduction.
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http://dx.doi.org/10.1259/bjr.20200340DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465856PMC
September 2020

Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG).

Rofo 2020 Jul 2;192(7):641-656. Epub 2020 Jul 2.

DRG, German Roentgen Society "Deutsche Röntgengesellschaft", Berlin, Germany.

Background:  Radiological reports of pancreatic lesions are currently widely formulated as free texts. However, for optimal characterization, staging and operation planning, a wide range of information is required but is sometimes not captured comprehensively. Structured reporting offers the potential for improvement in terms of completeness, reproducibility and clarity of interdisciplinary communication.

Method:  Interdisciplinary consensus finding of structured report templates for solid and cystic pancreatic tumors in computed tomography (CT) and magnetic resonance imaging (MRI) with representatives of the German Society of Radiology (DRG), German Society for General and Visceral Surgery (DGAV), working group Oncological Imaging (ABO) of the German Cancer Society (DKG) and other radiologists, oncologists and surgeons.

Results:  Among experts in the field of pancreatic imaging, oncology and pancreatic surgery, as well as in a public online survey, structured report templates were developed by consensus. These templates are available on the DRG homepage under www.befundung.drg.de and will be regularly revised to the current state of scientific knowledge by the participating specialist societies and responsible working groups.

Conclusion:  This article presents structured report templates for solid and cystic pancreatic tumors to improve clinical staging (cTNM, ycTNM) in everyday radiology.

Key Points:   · Structured report templates offer the potential of optimized radiological reporting with regard to completeness, reproducibility and differential diagnosis.. · This article presents consensus-based, structured reports for solid and cystic pancreatic lesions in CT and MRI.. · These structured reports are available open source on the homepage of the German Society of Radiology (DRG) under www.befundung.drg.de..

Citation Format: · Persigehl T, Baumhauer M, Baeßler B et al. Structured Reporting of Solid and Cystic Pancreatic Lesions in CT and MRI: Consensus-Based Structured Report Templates of the German Society of Radiology (DRG). Fortschr Röntgenstr 2020; 192: 641 - 655.
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http://dx.doi.org/10.1055/a-1150-8217DOI Listing
July 2020
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