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Institute
- Institut für diagnostische und interventionelle Radiologie (Institut für Röntgendiagnostik) (247) (remove)
Sonstige beteiligte Institutionen
- Cheng Lab, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA (1)
- Datenintegrationszentrum Würzburg (DIZ) (1)
- Interdisziplinäre Biomaterial- und Datenbank Würzburg (ibdw) (1)
- Würzburg Fabry Center for Interdisciplinary Therapy (FAZIT), University of Würzburg, Würzburg, Germany (1)
A high load of white matter lesions and enlarged basilar arteries have been shown in selected patients with Fabry disease, a disorder associated with an increased stroke risk. We studied a large cohort of patients with Fabry disease to differentially investigate white matter lesion load and cerebral artery diameters. We retrospectively analyzed cranial magnetic resonance imaging scans of 87 consecutive Fabry patients, 20 patients with ischemic stroke, and 36 controls. We determined the white matter lesion load applying the Fazekas score on fluid-attenuated inversion recovery sequences and measured the diameters of cerebral arteries on 3D-reconstructions of the time-of-flight-MR-angiography scans. Data of different Fabry patient subgroups (males – females; normal – impaired renal function) were compared with data of patients with stroke and controls. A history of stroke or transient ischemic attacks was present in 4/30 males (13%) and 5/57 (9%) females with Fabry disease, all in the anterior circulation. Only one man with Fabry disease showed confluent cerebral white matter lesions in the Fazekas score assessment (1%). Male Fabry patients had a larger basilar artery (p<0.01) and posterior cerebral artery diameter (p<0.05) compared to male controls. This was independent of disease severity as measured by renal function and did not lead to changes in arterial blood flow properties. A basilar artery diameter of >3.2 mm distinguished between men with Fabry disease and controls (sensitivity: 87%, specificity: 86%, p<0.001), but not from stroke patients. Enlarged arterial diameters of the posterior circulation are present only in men with Fabry disease independent of disease severity.
Utilizing 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT), we performed this pilot study to evaluate the link between cytogenetic/genomic markers and imaging patterns in relapsed/refractory (RR) multiple myeloma (MM). We retrospectively analyzed data of 24 patients with RRMM who were treated at our institution between November 2018 and February 2020. At the last relapse/progression, patients had been treated with a median of three (range 1–10) lines of therapy. Six (25%) patients showed FDG avid extramedullary disease without adjacency to bone. We observed significantly higher maximum standardized uptake values (SUV\(_{max}\)) in patients harboring del(17p) compared with those without del(17p) (p = 0.025). Moreover, a high SUV\(_{max}\) of >15 indicated significantly shortened progression-free survival (PFS) (p = 0.01) and overall survival (OS) (p = 0.0002). One female patient exhibited biallelic TP53 alteration, i.e., deletion and mutation, in whom an extremely high SUV\(_{max}\) of 37.88 was observed. In summary, this pilot study suggested a link between del(17p)/TP53 alteration and high SUV\(_{max}\) on 18F-FDG PET/CT in RRMM patients. Further investigations are highly warranted at this point.
This study aimed to explore the correlation between imaging patterns and clinical features in patients with smoldering multiple myeloma (SMM) who simultaneously underwent 18F-FDG, 11C-Methionine, and 68Ga-Pentixafor positron emission tomography/computed tomography (PET/CT). We retrieved and analyzed clinical characteristics and PET imaging data of 10 patients with SMM. We found a significant correlation between bone marrow (BM) plasma cell (PC) infiltration and mean standardized uptake values (SUV\(_{mean}\)) of lumbar vertebrae L2-L4 on 11C-Methionine PET/CT scans (r = 0.676, p = 0.031) and 68Ga-Pentixafor PET/CT scans (r = 0.839, p = 0.002). However, there was no significant correlation between BM involvement and SUV\(_{mean}\) of lumbar vertebrae L2-L4 on 18F-FDG PET/CT scans (r = 0.558, p = 0.093). Similarly, mean target-to-background ratios (TBR\(_{mean}\)) of lumbar vertebrae L2-L4 also correlated with bone marrow plasma cell (BMPC) infiltration in 11C-Methionine PET/CT (r = 0.789, p = 0.007) and 68Ga-Pentixafor PET/CT (r = 0.724, p = 0.018) PET/CT. In contrast, we did not observe a significant correlation between BMPC infiltration rate and TBR\(_{mean}\) in 18F-FDG PET/CT (r = 0.355, p = 0.313). Additionally, on 11C-Methionine PET/CT scans, we found a significant correlation between BMPC infiltration and TBR\(_{max}\) of lumbar vertebrae L2-L4 (r = 0.642, p = 0.045). In conclusion, 11C-Methionine and 68Ga-Pentixafor PET/CT demonstrate higher sensitivity than 18F-FDG PET/CT in detecting BM involvement in SMM.
Functional magnetic resonance imaging (fMRI) has become a powerful and influential method to non-invasively study neuronal brain activity. For this purpose, the blood oxygenation level-dependent (BOLD) effect is most widely used. T2* weighted echo planar imaging (EPI) is BOLD sensitive and the prevailing fMRI acquisition technique. Here, we present an alternative to its standard Cartesian recordings, i.e. k-space density weighted EPI, which is expected to increase the signal-to-noise ratio in fMRI data. Based on in vitro and in vivo pilot measurements, we show that fMRI by k-space density weighted EPI is feasible and that this new acquisition technique in fact boosted spatial and temporal SNR as well as the detection of local fMRI activations. Spatial resolution, spatial response function and echo time were identical for density weighted and conventional Cartesian EPI. The signal-to-noise ratio gain of density weighting can improve activation detection and has the potential to further increase the sensitivity of fMRI investigations.
Das Signal-zu-Rausch-Verhältnis (SNR) stellt bei modernen Bildgebungstechniken in der Magnetresonanz-Tomographie heutzutage oftmals die entscheidende Limitation dar. Eine Verbesserung durch Modifikation der Hardware ist kostspielig und führt meistens zu einer Verstärkung anderer Probleme, wie zum Beispiel erhöhte Energiedeposition ins Gewebe. Im Gegensatz dazu ist Dichtegewichtung eine Methode, die eine SNR-Erhöhung durch Modifikation der Aufnahmetechnik ermöglicht. In der MR-Bildgebung erfolgt oftmals eine retrospektive Filterung des aufgenommenen Signalverlaufs, beispielsweise zur Artefaktreduktion. Damit einhergehend findet eine Veränderung der Modulationstransferfunktion (MTF) bzw. ihrer Fouriertransformierten, der räumlichen Antwortfunktion (SRF), statt. Optimales SNR wird nach dem Matched Filter-Theorem erzielt, wenn die nachträgliche Filterung dem aufgenommenen Signalverlauf proportional ist. Dies steht dem Ziel der Artefaktreduktion entgegen. Bei Dichtegewichtung steht durch nicht-kartesische Abtastung des k-Raums mit der k-Raum-Dichte ein zusätzlicher Freiheitsgrad zur Verfügung. Dieser ermöglicht es, im Falle eines konstanten Signalverlaufs eine gewünschte MTF ohne Filterung zu erreichen. Bei veränderlichem Signalverlauf kann ein SNR Matched Filter angewendet werden, dessen negative Einflüsse auf die MTF durch Dichtegewichtung kompensiert werden. Somit ermöglicht Dichtegewichtung eine vorgegebene MTF und gleichzeitig ein optimales SNR. In der vorliegenden Arbeit wurde Dichtegewichtung erstmals bei den schnellen Multi-Echo-Sequenzen Turbo-Spin-Echo und Echoplanar-Bildgebung (EPI) angewendet. Im Gegensatz zu bisherigen Implementierungen muss hier der Signalabfall durch T2- bzw. T2*-Relaxation berücksichtigt werden. Dies führt dazu, dass eine prospektiv berechnete dichtegewichtete Verteilung nur bei einer Relaxationszeit optimal ist. Bei Geweben mit abweichenden Relaxationszeiten können sich wie auch bei den kartesischen Varianten dieser Sequenzen Änderungen an SRF und SNR ergeben. Bei dichtegewichteter Turbo-Spin-Echo-Bildgebung des Gehirns konnte mit den gewählten Sequenzparametern ein SNR-Vorteil von 43 % gegenüber der kartesischen Variante erzielt werden. Die Akquisition wurde dabei auf die T2-Relaxationszeit von weißer Substanz optimiert. Da die meisten Gewebe im Gehirn eine ähnliche Relaxationszeit aufweisen, blieb der visuelle Gesamteindruck identisch zur kartesischen Bildgebung. Der SNR-Gewinn konnte in der dichtegewichteten Implementierung zur Messzeithalbierung genutzt werden. Dichtegewichtete EPI weist eine hohe Anfälligkeit für geometrische Verzerrungen, welche durch Inhomogenitäten des Hauptmagnetfeldes verursacht werden, auf. Die Verzerrungen konnten erfolgreich mit einer Conjugate Phase-Methode korrigiert werden. Dazu muss die räumliche Verteilung der Feldinhomogenitäten bekannt sein. Dazu ist zusätzlich zur eigentlichen EPI-Aufnahme die zeitaufwendige Aufnahme einer sogenannten Fieldmap erforderlich. Im Rahmen dieser Arbeit konnte eine Methode entwickelt werden, welche die zur Erlangung einer Fieldmap notwendige Aufnahmedauer auf wenige Sekunden reduziert. Bei dieser Art der Fieldmap-Aufnahme müssen jedoch durch Atmung hervorgerufene Effekte auf die Bildphase berücksichtigt werden. Die Fieldmap-Genauigkeit kann durch Aufnahme unter Atempause, Mittelung oder retrospektiver Phasenkorrektur erhöht werden. Für die gewählten EPI-Sequenzparameter wurde mit Dichtegewichtung gegenüber der kartesischen Variante ein SNR-Gewinn von 14 % erzielt. Anhand einer funktionellen MRT (fMRI)-Fingertapping-Studie konnte demonstriert werden, dass die SNR-Steigerung auch zu einer signifikant erhöhten Aktivierungsdetektion in Teilen der Hirnareale führt, die bei der Fingerbewegung involviert sind. Die Verwendung von zusätzlicher EPI-Phasenkorrektur und iterativer Optimierung der dichtegewichteten k-Raum-Abtastung führt zu weiteren Verbesserungen der dichtegewichteten Bildgebung mit Multi-Echo-Sequenzen.
Background:
Clinical reasoning in Neurology is based on general associations which help to deduce the site of the lesion. However, even “golden principles” may occasionally be deceptive. Here, we describe the case of subacute flaccid tetraparesis due to motor cortical lesions. To our knowledge, this is the first report to include an impressive illustration of nearly symmetric motor cortical involvement of encephalitis on brain MRI.
Case presentation:
A 51 year old immunocompromized man developed a high-grade pure motor flaccid tetraparesis over few days. Based on clinical presentation, critical illness polyneuromyopathy was suspected. However, brain MRI revealed symmetrical hyperintensities strictly limited to the subcortical precentral gyrus. An encephalitis, possibly due to CMV infection, turned out to be the most likely cause.
Conclusion:
While recognition of basic clinical patterns is indispensable in neurological reasoning, awareness of central conditions mimicking peripheral nervous disease may be crucial to detect unsuspected, potentially treatable conditions.
Background. Missed or delayed detection of progressive neuronal damage after traumatic brain injury (TBI) may have negative impact on the outcome. We investigated whether routine follow-up CT is beneficial in sedated and mechanically ventilated trauma patients. Methods. The study design is a retrospective chart review. A routine follow-up cCT was performed 6 hours after the admission scan. We defined 2 groups of patients, group I: patients with equal or recurrent pathologies and group II: patients with new findings or progression of known pathologies. Results. A progression of intracranial injury was found in 63 patients (42%) and 18 patients (12%) had new findings in cCT 2 (group II). In group II a change in therapy was found in 44 out of 81 patients (54%). 55 patients with progression or new findings on the second cCT had no clinical signs of neurological deterioration. Of those 24 patients (44%) had therapeutic consequences due to the results of the follow-up cCT. Conclusion. We found new diagnosis or progression of intracranial pathology in 54% of the patients. In 54% of patients with new findings and progression of pathology, therapy was changed due to the results of follow-up cCT. In trauma patients who are sedated and ventilated for different reasons a routine follow-up CT is beneficial.
(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48–74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657–0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617–0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.
Objectives
Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies.
Methods
We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse.
Results
We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset.
Conclusion
RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics.
Critical relevance statement
This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models.
Key points
- Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research.
- A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled.
- Most datasets can be shared, used, and built upon freely under a Creative Commons license.
- All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.
Purpose
Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.
Methods
The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets—Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx.
Results
In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77.
Conclusion
AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.