Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks

PURPOSE

 Detection and validation of the chest X-ray view position with use of convolutional neural networks to improve meta-information for data cleaning within a hospital data infrastructure.

MATERIAL AND METHODS

 Within this paper we developed a convolutional neural network which automatically detects the anteroposterior and posteroanterior view position of a chest radiograph. We trained two different network architectures (VGG variant and ResNet-34) with data published by the RSNA (26 684 radiographs, class distribution 46 % AP, 54 % PA) and validated these on a self-compiled dataset with data from the University Hospital Essen (4507, radiographs, class distribution 55 % PA, 45 % AP) labeled by a human reader. For visualization and better understanding of the network predictions, a Grad-CAM was generated for each network decision. The network results were evaluated based on the accuracy, the area under the curve (AUC), and the F1-score against the human reader labels. Also a final performance comparison between model predictions and DICOM labels was performed.

RESULTS

 The ensemble models reached accuracy and F1-scores greater than 95 %. The AUC reaches more than 0.99 for the ensemble models. The Grad-CAMs provide insight as to which anatomical structures contributed to a decision by the networks which are comparable with the ones a radiologist would use. Furthermore, the trained models were able to generalize over mislabeled examples, which was found by comparing the human reader labels to the predicted labels as well as the DICOM labels.

CONCLUSION

 The results show that certain incorrectly entered meta-information of radiological images can be effectively corrected by deep learning in order to increase data quality in clinical application as well as in research.

KEY POINTS

  · The predictions for both view positions are accurate with respect to external validation data.. · The networks based their decisions on anatomical structures and key points that were in-line with prior knowledge and human understanding.. · Final models were able to detect labeling errors within the test dataset..

CITATION FORMAT

· Hosch R, Kroll L, Nensa F et al. Differentiation Between Anteroposterior and Posteroanterior Chest X-Ray View Position With Convolutional Neural Networks. Fortschr Röntgenstr 2021; 193: 168 - 176.

A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer

Background

Recently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer.

Methods

A total of 98 treatment-naïve patients (mean 59.7 years, range 28.0–89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats.

Results

Predictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading.

Conclusion

Our preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.

Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks

Pediatric endocrinologists regularly order radiographs of the left hand to estimate the degree of bone maturation in order to assess their patients for advanced or delayed growth, physical development, and to monitor consecutive therapeutic measures. The reading of such images is a labor-intensive task that requires a lot of experience and is normally performed by highly trained experts like pediatric radiologists. In this paper we build an automated system for pediatric bone age estimation that mimics and accelerates the workflow of the radiologist without breaking it. The complete system is based on two neural network based models: on the one hand a detector network, which identifies the ossification areas, on the other hand gender and region specific regression networks, which estimate the bone age from the detected areas. With a small annotated dataset an ossification area detection network can be trained, which is stable enough to work as part of a multi-stage approach. Furthermore, our system achieves competitive results on the RSNA Pediatric Bone Age Challenge test set with an average error of 4.56 months. In contrast to other approaches, especially purely encoder-based architectures, our two-stage approach provides self-explanatory results. By detecting and evaluating the individual ossification areas, thus simulating the workflow of the Tanner-Whitehouse procedure, the results are interpretable for a radiologist.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer

ZIELSETZUNG

 Ziel dieser Studie war die Evaluierung des prädiktiven Potenzials der Radiomics-Analyse zur Bestimmung des N- und M-Stadiums des primären Zervixkarzinoms anhand multiparametrischer ¹⁸F-FDG-PET/MRT-Bildgebung.

MATERIAL UND METHODEN

 30 Patientinnen mit einem histologisch gesicherten, primären und therapienaiven Zervixkarzinom unterzogen sich einer multiparametrischen ¹⁸F-FDG-PET/MRT-Untersuchung unter Verwendung eines dedizierten Untersuchungsprotokolls des weiblichen Beckens. Nach Segmentierung der Primärtumoren wurden quantitative Bildparameter mittels der Radiomic-Image-Processing-Toolbox bestimmt. Insgesamt wurden 45 verschiedene quantitative Bildmerkmale jeweils anhand der T2-gewichteten TSE-Sequenzen, der nativen und kontrastmittelgestützten T1-gewichteten TSE-Sequenzen, der ADC-Map, verschiedenen Perfusionsparametern (Ktrans, Kep, Ve and iAUC) und den ¹⁸F-FDG-PET-Datensätzen für jeden Tumor extrahiert. Die statistische Analyse zur Bestimmung des N- und M-Stadiums erfolgte unter der Verwendung der Python 3.5 und Scikit-learn-Software-Bibliothek für maschinelles Lernen.

ERGEBNISSE

 Insgesamt zeigte sich eine höhere Genauigkeit zur Prädiktion des korrekten M-Stadiums im Vergleich zum N-Stadium. Zur Prädiktion des korrekten M-Stadiums zeigten sich unter der Verwendung von SVM und SVM-RFE zur Feature-Auswahl die besten Ergebnisse mit einer Sensitivität von 91 %, einer Spezifität von 92 % und einer Fläche unter der Kurve (AUC) von 0,97. Die höchste Genauigkeit für die Bestimmung des N-Stadiums erfolgte unter der Verwendung von RBF-SVM und MIFS zur Feature-Auswahl mit einer Sensitivität von 83 %, einer Spezifität von 67 % und einer Fläche unter der Kurve (AUC) von 0,82.

SCHLUSSFOLGERUNG

 Die Radiomics-Analyse von multiparametrischen PET/MR-Datensätzen ermöglicht eine präzise Prädiktion des M- und N-Stadiums von Patientinnen mit primärem Zervixkarzinom und könnte damit supportiv zur nichtinvasiven Tumor-Phänotypisierung und Patientenstratifizierung eingesetzt werden.

KERNAUSSAGEN

  · Die Radiomics-Analyse der multiparametrischen PET/MRT ermöglicht die Prädiktion des Metastasierungsstatus des Zervixkarzinoms.. · Die Prädiktion des M-Stadiums ist der Prädiktion des N-Stadiums überlegen.. · Die multiparametrische PET/MRT bietet eine valide Plattform für Radiomics-Analysen..

CITATION FORMAT

· Umutlu L, Nensa F, Demircioglu A et al. Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer. Fortschr Röntgenstr 2020; 192: 754 - 763.

Cardiac PET/MRI: Current Clinical Status and Future Perspectives

Combined PET/MRI has now been in clinical routine for almost 10 years. Since then, it has not only had to face validation, comparison and research questions, it has also been increasingly used in clinical routine. A number of cardiovascular applications have become established here, whereby viability imaging and assessment of inflammatory and infiltrative processes in the heart are to be emphasized. However, further interesting applications are expected in the near future. This review summarizes the most important clinical applications on the one hand and mentions interesting areas of application in research on the other.

Variations on Branding with Text Occurrence for Optimized Body Parts Classification

The aim of this paper is to combine automatically generated image keywords with radiographs, thus enabling an enriched multi-modal image representation for body part classification. The proposed method could also be used to incorporate meta data into images for combined learning. Multi-modality is achieved by branding the radiographs via intensity markers, which denotes the occurrence of textual features. There is a need to create systems capable of adequately detecting and classifying body parts in radiology images, as the number of digital medical scans taken daily has expeditiously increased. This is a fundamental step towards computer-aided interpretation, as manual annotation is time-consuming, prone to errors and often impractical. Word embeddings are derived from keywords, automatically generated with the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) Show-and-Tell model, and incorporated by augmentation into radiographs with Word2Vec: Deep learning systems are trained with the augmented radiographs. Using the data sets, Musculoskeletal Radiographs (MURA) and ImageCLEF 2015 Medical Clustering Task, the proposed approach obtains best prediction accuracy, with 95.78 % and 83.90 %, respectively.

Surgery for adrenal angiomyelolipoma: an individualized concept

BACKGROUND

Because adrenal angiomyelolipoma (AAML) is rare and uniformly benign, the indications for surgery are ill defined.

METHODS

Among a series of 156 patients with adrenal pathologies surgically treated between 2013 and 2018, 12 patients were operated with the diagnosis of an AAML. The clinical as well as imaging parameters forming the individual indications for surgery were analyzed.

RESULTS

Preoperative diagnosis consistent with AAML was made in all 12 patients. The mean size of surgically removed AAML was 82.3 mm (45-150 mm). Gender and affected side were evenly distributed. Local symptoms but lack of radiological signs suspicious for malignancy or size increase were observed in 4 of 12 patients (group 1, 33%). In contrast, 4 of 12 patients (group 2, 33%) showed radiological signs suspicious for malignancy but lacked local symptoms. Additional 4 of 12 patients (group 3, 33%) showed both local symptoms and radiological signs suspicious for malignancy. Patients with local symptoms harbored significantly larger tumors compared to those patients that lacked local symptoms (93.9 mm ± 32.8 vs. 59.3 mm ± 2.7, p = 0.021). Patients with radiologically suspicious signs were older (60 years ± 9.9 vs. 53 years ± 5.4, p > 0.05), and time to surgery was shorter (4.4 months ± 3 vs. 6.0 months ± 3.0, p > 0.05). Importantly, surgical approach was not influenced by tumor size (p = 0.65). However, patients with suspicious imaging were more likely to be operated by conventional open approach (4 of 8 vs. 0 of 4, p = 0.08). The minimal invasive approach was associated with shorter hospital stay (7 days, ± 1.3 vs. 14.2 days, ± 8.8, p = 0.038). All lesions that showed radiological signs suspicious for malignancy proved benign in final histology.

CONCLUSION

Large AAML present a clinical challenge. The presence of local symptoms and/or radiological signs suspicious for malignancy identifies three groups of patients that define the concept of an individualized indication for surgery in AAML. A minimal invasive approach can be advocated even for large AAML with radiological signs suspicious for malignancy.

Impact of improved attenuation correction on 18F-FDG PET/MR hybrid imaging of the heart

Purpose

The aim of this study was to evaluate and quantify the effect of improved attenuation correction (AC) including bone segmentation and truncation correction on 18F-Fluordesoxyglucose cardiac positron emission tomography/magnetic resonance (PET/MR) imaging.

Methods

PET data of 32 cardiac PET/MR datasets were reconstructed with three different AC-maps (1. Dixon-VIBE only, 2. HUGE truncation correction and bone segmentation, 3. MLAA). The Dixon-VIBE AC-maps served as reference of reconstructed PET data. 17-segment short-axis polar plots of the left ventricle were analyzed regarding the impact of each of the three AC methods on PET quantification in cardiac PET/MR imaging. Non-AC PET images were segmented to specify the amount of truncation in the Dixon-VIBE AC-map serving as a reference. All AC-maps were evaluated for artifacts.

Results

Using HUGE + bone AC results in a homogeneous gain of ca. 6% and for MLAA 8% of PET signal distribution across the myocardium of the left ventricle over all patients compared to Dixon-VIBE AC only. Maximal relative differences up to 18% were observed in segment 17 (apex). The body volume truncation of -12.7 ± 7.1% compared to the segmented non-AC PET images using the Dixon-VIBE AC method was reduced to -1.9 ± 3.9% using HUGE and 7.8 ± 8.3% using MLAA. In each patient, a systematic overestimation in AC-map volume was observed when applying MLAA. Quantitative impact of artifacts showed regional differences up to 6% within single segments of the myocardium.

Conclusions

Improved AC including bone segmentation and truncation correction in cardiac PET/MR imaging is important to ensure best possible diagnostic quality and PET quantification. The results exhibited an overestimation of AC-map volume using MLAA, while HUGE resulted in a more realistic body contouring. Incorporation of bone segmentation into the Dixon-VIBE AC-map resulted in homogeneous gain in PET signal distribution across the myocardium. The majority of observed AC-map artifacts did not significantly affect the quantitative assessment of the myocardium.

Cardiac PET/MRI-an update

It is now about 8 years since the first whole-body integrated PET/MRI has been installed. First, reports on technical characteristics and system performance were published. Early after, reports on the first use of PET/MRI in oncological patients were released. Interestingly, the first article on the application in cardiology was a review article, which was published before the first original article was put out. Since then, researchers have gained a lot experience with the PET/MRI in various cardiovascular diseases and an increasing number on auspicious indications is appearing. In this review article, we give an overview on technical updates within these last years with potential impact on cardiac imaging and summarize those scenarios where PET/MRI plays a pivotal role in cardiovascular medicine.

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection

BACKGROUND

Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but their drawback is the need of large annotated datasets. Finetuning pre-trained networks is a viable alternative, but it is not clear a priori if training with small annotated datasets will be successful, as it depends on the problem at hand. In this paper, we show that pre-trained networks can be utilized to produce an effective detector of ossification areas in pediatric X-ray images of hands.

METHODS AND FINDINGS

A publicly available Faster R-CNN network, pre-trained on the COCO dataset, was utilized and finetuned with 240 manually annotated radiographs from the RSNA Pediatric Bone Age Challenge, which comprises over 14.000 pediatric radiographs. The validation is done on another 89 radiographs from the dataset and the performance is measured by Intersection-over-Union (IoU). To understand the effect of the data size on the pre-trained network, subsampling was applied to the training data and the training was repeated. Additionally, the network was trained from scratch without any pre-trained weights. Finally, to understand whether the trained model could be useful, we compared the inference of the network to an annotation of an expert radiologist. The finetuned network was able to achieve an average precision (mAP@0.5IoU) of 92.92 ± 1.93. Apart from the wrist region, all ossification areas were able to benefit from more data. In contrast, the network trained from scratch was not able to produce any correct results. When compared to the annotations of the expert radiologist, the network was able to localize the regions quite well, as the F1-Score was on average 91.85 ± 1.06.

CONCLUSIONS

By finetuning a pre-trained deep neural network, with 240 annotated radiographs, we were able to successfully detect ossification areas in prediatric hand radiographs.

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is impractical, time-consuming and prone to errors. This paper presents modeling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD). Different image preprocessing and enhancement techniques were applied to augment grayscale radiographs by virtually adding two extra layers. The Image Retrieval in Medical Applications (IRMA) Code, a mono-hierarchical multi-axial code, served as a basis for this work. To extensively evaluate the image enhancement techniques, five classification schemes including the complete IRMA code were adopted. The deep convolutional neural network systems Inception-v3 and Inception-ResNet-v2, and Random Forest models with 1000 trees were trained using extracted Bag-of-Keypoints visual representations. The classification model performances were evaluated using the ImageCLEF 2009 Medical Annotation Task test set. The applied visual enhancement techniques proved to achieve better annotation accuracy in all classification schemes.

Assessment of Ileocolonic Inflammation in Crohn's Disease: Which Surrogate Marker Is Better-MaRIA, Clermont, or PET/MR Index? Initial Results of a Feasibility Trial

Our objective was to define an ¹⁸F-FDG PET/MR enterography index as a hybrid surrogate marker for active ileocolonic inflammation in Crohn's disease (CD) and assess its diagnostic performance in comparison to validated MR indices (MR index of activity [MaRIA], Clermont score). Methods: Fifty-two CD patients with recurrent symptoms underwent ileocolonoscopy and ¹⁸F-FDG PET/MR enterography. Three hundred three ileocolonic segments were assessed for inflammation using MaRIA and the Clermont score as well as the newly defined PET/MR index. On the basis of tobit regression, the PET/MR index was defined as (0.87 × wall thickness) + (1.97 × edema) + (0.83 × ulceration) + (0.55 × SUVmax ratio) + 1.14. The endoscopic activity of inflammation was determined by the simplified endoscopic activity score for CD (SES-CD). Receiver-operating-characteristic curves for each surrogate marker were created and tested against each other using the DeLong test, and diagnostic accuracies were compared using the McNemar test. Correlations between surrogate markers and SES-CD were tested with the Spearman rank correlation test. Results: The PET/MR index showed a comparable sensitivity but a significantly higher specificity and accuracy than MaRIA and the Clermont score in predicting both active and severe inflammation (active inflammation: specificities of 0.933, 0.711, and 0.707 and accuracies of 0.921, 0.739, and 0.736, P < 0.001; severe inflammation: specificities of 0.91, 0.81, and 0.785 and accuracies of 0.914, 0.818, and 0.795, P < 0.01, respectively). All surrogate markers correlated moderately with SES-CD on a segmental basis and a global level (0.5 < ρ < 0.7, all P < 0.001). Conclusion: As a hybrid surrogate marker comprising MR parameters and the PET component, the PET/MR index yielded significantly improved specificity and diagnostic accuracy compared with conventional MR indices (MaRIA and the Clermont score), demonstrating its high potential for noninvasive assessment of CD.

Hybrid cardiac imaging using PET/MRI: a joint position statement by the European Society of Cardiovascular Radiology (ESCR) and the European Association of Nuclear Medicine (EANM)

Positron emission tomography (PET) and magnetic resonance imaging (MRI) have both been used for decades in cardiovascular imaging. Since 2010, hybrid PET/MRI using sequential and integrated scanner platforms has been available, with hybrid cardiac PET/MR imaging protocols increasingly incorporated into clinical workflows. Given the range of complementary information provided by each method, the use of hybrid PET/MRI may be justified and beneficial in particular clinical settings for the evaluation of different disease entities. In the present joint position statement, we critically review the role and value of integrated PET/MRI in cardiovascular imaging, provide a technical overview of cardiac PET/MRI and practical advice related to the cardiac PET/MRI workflow, identify cardiovascular applications that can potentially benefit from hybrid PET/MRI, and describe the needs for future development and research. In order to encourage its wide dissemination, this article is freely accessible on the European Radiology and European Journal of Hybrid Imaging web sites.

KEY POINTS

• Studies and case-reports indicate that PET/MRI is a feasible and robust technology. • Promising fields of application include a variety of cardiac conditions. • Larger studies are required to demonstrate its incremental and cost-effective value. • The translation of novel radiopharmaceuticals and MR-sequences will provide exciting new opportunities.

Feasibility of aortic valve planimetry at 7 T ultrahigh field MRI: Comparison to aortic valve MRI at 3 T and 1.5 T

Introduction

This study examined the feasibility of aortic valve planimetry at 7 T ultrahigh field MRI in intraindividual comparison to 3 T and 1.5 T MRI.

Material and methods

Aortic valves of eleven healthy volunteers (mean age, 26.4 years) were examined on a 7 T, 3 T, and 1.5 T MR system using FLASH and TrueFISP sequences. Two experienced radiologists evaluated overall image quality, the presence of artefacts, tissue contrast ratios, identifiability, and image details of the aortic valve opening area (AVOA). Furthermore, AVOA was quantified twice by reader 1 and once by reader 2. Correlation analysis between artefact severity and employed magnetic field strength was performed by modified Fisher’s exact-test. Paired t-test was used to analyse for AVOA differences, and Bland-Altman plots were used to analyse AVOA intra-rater and inter-rater variability.

Results

Aortic valve imaging at 7 T, 3 T, and 1.5 T with using FLASH was less hampered by artefacts than TrueFISP imaging at 3 T and 1.5 T. Tissue contrast and image details were rated best at 7 T. AVOA was measured slightly smaller at 7 T compared to 3 T (TrueFISP, p-value = 0.057; FLASH, p-value = 0.016) and 1.5 T (TrueFISP, p-value = 0.029; FLASH, p-value = 0.018). Intra-rater and inter-rater variability of AVOA tended to be slightly smaller at 7 T than at 3 T and 1.5 T.

Conclusion

Aortic valve planimetry at 7 T ultrahigh field MRI is technically feasible and in healthy volunteers offers an improved tissue contrast and a slightly better reproducibility than MR planimetry at 1.5 T and 3 T.

Prospective comparison of 18F-FDG PET/MRI and 18F-FDG PET/CT for thoracic staging of non-small cell lung cancer

OBJECTIVES

To compare the diagnostic performance of ¹⁸F-FDG PET/MRI and ¹⁸F-FDG PET/CT for primary and locoregional lymph node staging in non-small cell lung cancer (NSCLC).

METHODS

In this prospective study, a total of 84 patients (51 men, 33 women, mean age 62.5 ± 9.1 years) with histopathologically confirmed NSCLC underwent ¹⁸F-FDG PET/CT followed by ¹⁸F-FDG PET/MRI in a single injection protocol. Two readers independently assessed T and N staging in separate sessions according to the seventh edition of the American Joint Committee on Cancer staging manual for ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI, respectively. Histopathology as a reference standard was available for N staging in all 84 patients and for T staging in 39 patients. Differences in staging accuracy were assessed by McNemars chi² test. The maximum standardized uptake value (SUV_max) and longitudinal diameters of primary tumors were correlated using Pearson's coefficients.

RESULTS

T stage was categorized concordantly in ¹⁸F-FDG PET/MRI and ¹⁸F-FDG PET/CT in 38 of 39 (97.4%) patients. Herein, ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI correctly determined the T stage in 92.3 and 89.7% of patients, respectively. N stage was categorized concordantly in 83 of 84 patients (98.8%). ¹⁸F-FDG PET/CT correctly determined the N stage in 78 of 84 patients (92.9%), while ¹⁸F-FDG PET/MRI correctly determined the N stage in 77 of 84 patients (91.7%). Differences between ¹⁸F-FDG PET/CT and ¹⁸F-FDG PET/MRI in T and N staging accuracy were not statistically significant (p > 0.5, each). Tumor size and SUV_max measurements derived from both imaging modalities exhibited excellent correlation (r = 0.963 and r = 0.901, respectively).

CONCLUSION

¹⁸F-FDG PET/MRI and ¹⁸F-FDG PET/CT show an equivalently high diagnostic performance for T and N staging in patients suffering from NSCLC.

Feasibility of FDG-PET in myocarditis: Comparison to CMR using integrated PET/MRI

OBJECTIVE

Besides cardiac sarcoidosis, FDG-PET is rarely used in the diagnosis of myocardial inflammation, while cardiac MRI (CMR) is the actual imaging reference for the workup of myocarditis. Using integrated PET/MRI in patients with suspected myocarditis, we prospectively compared FDG-PET to CMR and the feasibility of integrated FDG-PET/MRI in myocarditis.

METHODS

A total of 65 consecutive patients with suspected myocarditis were prospectively assessed using integrated cardiac FDG-PET/MRI. Studies comprised T2-weighted imaging, late gadolinium enhancement (LGE), and simultaneous PET acquisition. Physiological glucose uptake in the myocardium was suppressed using dietary preparation.

RESULTS

FDG-PET/MRI was successful in 55 of 65 enrolled patients: two patients were excluded due to claustrophobia and eight patients due to failed inhibition of myocardial glucose uptake. Compared with CMR (LGE and/or T2), sensitivity and specificity of PET was 74% and 97%. Overall spatial agreement between PET and CMR was κ = 0.73. Spatial agreement between PET and T2 (κ = 0.75) was higher than agreement between PET and LGE (κ = 0.64) as well as between LGE and T2 (κ = 0.56).

CONCLUSION

In patients with suspected myocarditis, FDG-PET is in good agreement with CMR findings.

MET Expression in Advanced Non-Small-Cell Lung Cancer: Effect on Clinical Outcomes of Chemotherapy, Targeted Therapy, and Immunotherapy

BACKGROUND

The receptor tyrosine kinase MET is implicated in malignant transformation, tumor progression, metastasis, and acquired treatment resistance. We conducted an analysis of the effect of MET expression and MET genomic aberrations on the outcome of patients with advanced or metastatic pulmonary adenocarcinomas prospectively enrolled in an institutional precision oncology program.

PATIENTS AND METHODS

Standardized immunohistochemistry (IHC) analyses of MET and markers of pathway activation were available in 384 patients, and next-generation sequencing-based MET hotspot mutation analyses were available from 892 patients. Clinical data were retrieved with a median follow-up from initial diagnosis of 37 months.

RESULTS

High MET expression, defined as MET IHC 3+ or MET H-Score in the upper quartile, was observed in 102 of 384 patients (26.6%). MET exon 14 mutations were only detected in 7 of 892 patients (0.78%). High MET expression correlated with activation markers of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathways only in cases without Kirsten rat sarcoma viral oncogene homolog (KRAS), epidermal growth factor receptor (EGFR), v-Raf murine sarcoma viral oncogene homolog B (BRAF), anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) aberrations. There was no association of MET expression with outcome during chemotherapy. High MET expression negatively affected the outcome during EGFR-targeting therapy but was associated with more favorable results with programmed death 1/programmed death ligand 1 (PD-L1)-directed therapy, independent of smoking history, PD-L1 expression or KRAS mutation. Two patients with MET exon 14 mutation and high PD-L1 expression failed to respond to pembrolizumab.

CONCLUSION

MET expression affects the outcomes of targeted therapies in non-small-cell lung cancer, thus supporting the development of biomarker-informed combination strategies. The interaction of MET expression and MET mutation with immune checkpoint inhibitor therapy is novel and merits further investigation.

Clinical use of cardiac PET/MRI: current state-of-the-art and potential future applications

Combined PET/MRI is a novel imaging method integrating the advances of functional and morphological MR imaging with PET applications that include assessment of myocardial viability, perfusion, metabolism of inflammatory tissue and tumors, as well as amyloid deposition imaging. As such, PET/MRI is a promising tool to detect and characterize ischemic and non-ischemic cardiomyopathies. To date, the greatest benefit may be expected for diagnostic evaluation of systemic diseases and cardiac masses that remain unclear in cardiac MRI, as well as for clinical and scientific studies in the setting of ischemic cardiomyopathies. Diagnosis and therapeutic monitoring of cardiac sarcoidosis has the potential of a possible 'killer-application' for combined cardiac PET/MRI. In this article, we review the current evidence and discuss current and potential future applications of cardiac PET/MRI.

Spirometry or Body Plethysmography for the Assessment of Bronchial Hyperresponsiveness?

Methacholine testing is one of the standard tools for the diagnosis of mild asthma, but there is little information about optimal outcome measures. In this study a total of 395 college students were tested by the ATS dosimeter protocol for methacholine testing, with minor modification. Body plethysmography and spirometry were measured after each inhalation step. The end-of-test-criteria were (i) decrease in forced expiratory volume in 1 s (FEV1) of ≥ 20 % and (ii) doubling of specific airway resistance and its increase to ≥ 2.0 kPa∙s. The results were expressed by receiver operating characteristic (ROC) plots using questionnaire answers as a reference. The areas under the ROC curves were iteratively calculated for a wide range of thresholds for both measures. We found that ROC plots showed maximal sensitivities of about 0.5-0.6 for FEV1 and about 0.7 for specific airway conductance (sGt), with similar specificities of about 0.7-0.8 taking questions with the known high specificity as references. Accordingly, larger maximal areas under the ROC curve were observed for body plethysmography, but the differences were small. A decrease in FEV1 of about 15 % and a decrease of sGt of about 60 % showed the largest areas under the ROC curves. In conclusion, body plethysmography yielded better sensitivity than spirometry, with similar specificity. However, replacing the common spirometric criterium for a positive test (20 % decrease in FEV1 from baseline) by the optimal body plethysmographic criterium would result in an increase of false positive tests from about 4 to 8 % in healthy young adults.

Is integrated 18F-FDG PET/MRI superior to 18F-FDG PET/CT in the differentiation of incidental tracer uptake in the head and neck area?

PURPOSE

We aimed to investigate the accuracy of 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) compared with contrast-enhanced 18F-FDG PET/computed tomography (PET/CT) for the characterization of incidental tracer uptake in examinations of the head and neck.

METHODS

A retrospective analysis of 81 oncologic patients who underwent contrast-enhanced 18F-FDG PET/CT and subsequent PET/MRI was performed by two readers for incidental tracer uptake. In a consensus reading, discrepancies were resolved. Each finding was either characterized as most likely benign, most likely malignant, or indeterminate. Using all available clinical information including results from histopathologic sampling and follow-up examinations, an expert reader classified each finding as benign or malignant. McNemar's test was used to compare the performance of both imaging modalities in characterizing incidental tracer uptake.

RESULTS

Forty-six lesions were detected by both modalities. On PET/CT, 27 lesions were classified as most likely benign, one as most likely malignant, and 18 as indeterminate; on PET/MRI, 31 lesions were classified as most likely benign, one lesion as most likely malignant, and 14 as indeterminate. Forty-three lesions were benign and one lesion was malignant according to the reference standard. In two lesions, a definite diagnosis was not possible. McNemar's test detected no differences concerning the correct classification of incidental tracer uptake between PET/CT and PET/MRI (P = 0.125).

CONCLUSION

In examinations of the head and neck area, incidental tracer uptake cannot be classified more accurately by PET/MRI than by PET/CT.

Diagnostic impact of integrated 18F-FDG PET/MRI in cerebral staging of patients with non-small cell lung cancer

Background Integrated positron emission tomography/magnetic resonance imaging (PET/MRI) systems are increasingly being available and used for staging examinations. Brain metastases (BM) are frequent in patients with non-small cell lung cancer (NSCLC) and decisive for treatment strategy. Purpose To assess the diagnostic value of integrated 18F-2-fluoro-2-deoxy-D glucose (18F-FDG) PET/MRI in initial staging in patients with NSCLC for BM in comparison to MRI alone. Material and Methods Eighty-three patients were prospectively enrolled for an integrated 18F-FDG PET/MRI examination. The 3 T MRI protocol included a fluid-attenuated inversion-recovery sequence (FLAIR) and a contrast-enhanced three-dimensional magnetization prepared rapid acquisition GRE sequence (MPRAGE). Two neuroradiologists evaluated the datasets in consensus regarding: (i) present lesions; (ii) size of lesions; and (iii) number of lesions detected in MRI alone, compared to the PET component when reading the 18F-FDG PET/MRI. Results Based on MRI alone, BM were detected in 15 out of the 83 patients, comprising a total of 39 metastases. Based on PET alone, six patients out of the 83 patients were rated positive for metastatic disease, revealing a total of 15 metastases. PET detected no additional BM. The size of the BM correlated positively with sensitivity of detection in PET. Conclusion The sensitivity of PET in detection of BM depends on their size. 18F-FDG PET/MRI does not lead to an improvement in diagnostic accuracy in cerebral staging of NSCLC patients, as MRI alone remains the gold standard.