Automatische Klassifizierung von Gliomen des menschlichen Gehirns auf Basis quantitativer Emissionstomographie und multimodaler Bildgebung

Cerebral gliomas represent a common type of cancer of the human brain with many tumor grades which express a huge diversity in growth characteristics and have a highly varying malignancy. The optimal treatment for a cerebral glioma is only ensured if the underlying tumor grade is known. One very common grading scheme is the World Health Organization (WHO) Classification of tumors of the central nervous system, which differentiates four grades. The de facto standard of grading a glioma is based on bioptic samples which are obtained in invasive interventions. These interventions pose significant risks for the patients and add more time delays between an initial evidence of the tumor, usually found by X-ray computed tomography (CT) or magnetic resonance imaging (MRI) and the initiation of a treatment. On the other side, versatile imaging modalities like CT, MRI and from the field of nuclear medicine, positron emission tomography (PET) cover various aspects of the morphology and physiology of a tumor. The information gained from medical imaging thus can indicate the grade of a cerebral glioma without any invasive intervention. The multimodal imaging often results in a high complexity that makes if difficult to diagnose the malignancy solely based on the visual interpretation of medical images. In this thesis, we present approaches for an extensive pattern recognition pipeline for the grading of cerebral gliomas based on tomographic datasets from MRI, CT, and PET. More specifically, we use gadolinium contrast-enhanced T1-weighted MRI, T2-weighted fluid attenuated inversion recovery MRI, diffusion-weighted MRI, non contrast-enhanced low-dose X-ray CT, and dynamic (multiple acquired time frames) [18F]-Fluor-Ethyl-Tyrosine (FET) PET. Our setup includes image preprocessing, feature extraction and calculation, feature normalization, and finally fully automatic classification. We propose the imaging modalities and the classifiers which performed best for our patient population and show that inter-dataset normalization as a preprocessing step helps to improve the classification rate for cerebral gliomas. As the PET is acquired over a lengthy time period which can lead to substantial patient motion, we present a retrospective motion correction technique based on image registration, which improves the image quality of the PET data. The presented approaches underline that diagnostic statements can be gained from highly complex, multimodal image data in an automated fashion. We can differentiate not only low- and high-grade tumors, but also aid in distinguishing between the four WHO grades within some limitations.Gliome repräsentieren eine häufige Krebserkrankung des menschlichen Gehirns. Es werden mehrere Gliomgrade unterschieden, die wiederum große Variabilität bezüglich ihres Wachstumsverhalten und ihrer Malignität aufweisen. Die optimale Behandlung eines Glioms ist nur sichergestellt, wenn der zugrundeliegende Tumorgrad bekannt ist. Ein verbreitetes Klassifikationsschema auf diesem Gebiet ist die Klassifikation von Tumoren des zentralen Nervensystems der Weltgesundheitsorganisation (WHO), welches vier Tumorgrade differenziert. Der Standard bezüglich der Bestimmung des Tumorgrades ist die histopathologische Aufarbeitung von bioptischen Proben, die in invasiven Verfahren gewonnen werden. Diese Eingriffe stellen jedoch ein Risiko für den Patienten dar. Darüber hinaus tragen sie zu einem Zeitverzug zwischen dem initialen Hinweis auf einen Tumor, häufig gewonnen durch die medizinische Bildgebung, und der Einleitung einer Behandlung bei. Im Gegensatz zu invasiven Methoden existieren verschiedene Bildgebungsverfahren wie die Röntgen-Computertomographie (CT), die Magnetresonanztomographie (MRT) und, aus dem Bereich der Nuklearmedizin, die Positronenemissionstomographie (PET). Diese Bildgebungsmodalitäten sind in der Lage, umfassende Aspekte der Tumorphysiologie und -morphologie darzustellen. Folglich können mithilfe jener Verfahren Indizien für den zugrundeliegenden Grad des Tumors gewonnen werden. Die multimodale Bildgebung generiert jedoch eine hohe Komplexität, die eine auf reiner Bildbetrachtung basierende Malignitätsdiagnostik erschwert. In der vorliegenden Arbeit stellen wir Ansätze zur Beurteilung des Tumorgrads unter Anwendung von Mustererkennungsverfahren auf Bilddaten der MRT, der PET und der CT vor. Im Detail verwenden wir gadoliniumkontrastiertes, T1-gewichtetes MRT, T2-FLAIR MRT, diffusionsgewichtetes MRT, natives Niedrigdosis-CT und dynamisches PET der mit F-18-Ethyltyrosin (FET) dargestellten zerebralen Aminosäureaufnahme. Die vorgestellten Methoden umfassen die Vorverarbeitung der medizinischen Bilder, die Merkmalsextraktion und -berechnung, die Normierung der Merkmale und die vollautomatische Klassifizierung. Wir ermitteln die besten Modalitäten und Klassifikatoren auf Basis unserer Patientenpopulation und zeigen, dass eine Normalisierung der Datensätze im Zuge der Datenvorverarbeitung die Klassifikationsrate erhöhen kann. Für die PET, die aufgrund ihrer ausgedehnten Aufnahmedauer potentiell in besonderem Maße von Bewegungsartefakten betroffen ist, stellen wir eine Bewegungskorrekturmethode vor, welche auf starrer und retrospektiver Bildregistrierung basiert. Diese Korrektur verbessert die Bildqualität der PET signifikant. Weiterhin zeigen wir, dass mit unserem automatisierten Ansatz mit hoher Genauigkeit diagnostische Aussagen aus hochkomplexen, multimodalen Bilddaten gewonnen werden können. Nicht nur die Unterscheidung von niedrig- und hochgradigen Tumoren, sondern darüber hinaus die Abgrenzung der vier WHO-Grade kann in gewissen Grenzen realisiert werden

Journal of Orofacial Orthopedics / Geometric morphometrics of different malocclusions in lateral skull radiographs

Background To evaluate the role of craniofacial shape in malocclusion by application of geometric morphometrics to a set of two-dimensional landmarks and semilandmarks obtained from lateral skull radiographs. Methods Cephalometric radiograph tracings of 88 untreated Caucasians (age range 739 years) were assigned to four groups according to their occlusion: neutrocclusion, distocclusion, mesiocclusion, and anterior open bite. The geometric morphometric shape analysis incorporated 66 landmarks and semilandmarks, which underwent generalized Procrustes analysis, between-groups principal component analysis, thin-plate spline deformation grid visualization, permutation tests, and receiver operating characteristic curves. Results The position and shape of the mandible contributed to differences between the distocclusion and mesiocclusion groups, whereas the maxillary shape showed less variation. The growth-related shape alteration during adolescence was most pronounced in the mesiocclusion group and least pronounced in the neutrocclusion group. The open bite group was associated with an altered orientation of the mandibular body and the maxilla, showed the most hyperdivergent maxillomandibular pattern but was not an own skeletal entity. Despite clear differences in mean shape across the four groups, the individual distribution of craniofacial shape overlapped between the groups without discrete clusters. Conclusions Craniofacial shape was clearly associated with dental malocclusion and showed considerable variation. Geometric morphometrics was a powerful research tool but for diagnosing individual malocclusion standard cephalometric measurements including overjet and overbite were equally or more efficient than geometric morphometric descriptors.(VLID)349857

Ictal SPECT in patients with rapid eye movement sleep behaviour disorder

Rapid eye movement sleep behaviour disorder is a rapid eye movement parasomnia clinically characterized by acting out dreams due to disinhibition of muscle tone in rapid eye movement sleep. Up to 80–90% of the patients with rapid eye movement sleep behaviour disorder develop neurodegenerative disorders within 10–15 years after symptom onset. The disorder is reported in 45–60% of all narcoleptic patients. Whether rapid eye movement sleep behaviour disorder is also a predictor for neurodegeneration in narcolepsy is not known. Although the pathophysiology causing the disinhibition of muscle tone in rapid eye movement sleep behaviour disorder has been studied extensively in animals, little is known about the mechanisms in humans. Most of the human data are from imaging or post-mortem studies. Recent studies show altered functional connectivity between substantia nigra and striatum in patients with rapid eye movement sleep behaviour disorder. We were interested to study which regions are activated in rapid eye movement sleep behaviour disorder during actual episodes by performing ictal single photon emission tomography. We studied one patient with idiopathic rapid eye movement sleep behaviour disorder, one with Parkinson’s disease and rapid eye movement sleep behaviour disorder, and two patients with narcolepsy and rapid eye movement sleep behaviour disorder. All patients underwent extended video polysomnography. The tracer was injected after at least 10 s of consecutive rapid eye movement sleep and 10 s of disinhibited muscle tone accompanied by movements registered by an experienced sleep technician. Ictal single photon emission tomography displayed the same activation in the bilateral premotor areas, the interhemispheric cleft, the periaqueductal area, the dorsal and ventral pons and the anterior lobe of the cerebellum in all patients. Our study shows that in patients with Parkinson’s disease and rapid eye movement sleep behaviour disorder—in contrast to wakefulness—the neural activity generating movement during episodes of rapid eye movement sleep behaviour disorder bypasses the basal ganglia, a mechanism that is shared by patients with idiopathic rapid eye movement sleep behaviour disorder and narcolepsy patients with rapid eye movement sleep behaviour disorder

Longitudinal analysis of bone metabolism using SPECT/CT and 99mTc-diphosphono-propanedicarboxylic acid: comparison of visual and quantitative analysis

Background The therapy response of osseous metastases (OM) is commonly monitored by bone scintigraphies (BS). The aim of this study was to compare visual evaluation of changes in tracer uptake with quantitation in absolute units in OMs; 52 OMs from 19 patients who underwent BS with SPECT/CT at time points one and two (TP1/2) were analyzed retrospectively, with an average of 10.3 months between TP1 and 2. Tracer uptake in lesions was visually compared by two independent readers in both planar scintigraphies and SPECT/CT across both TPs and classified as regressive, stable, or progressive. Quantitative analysis was performed by measuring peak standardized uptake values (SUV). Based on quantitation, lesions were similarly classified as regressive (>30 % decrease), progressive (>30 % increase), or stable (rest). If available, uptake in reference regions in the lower thoracic or lumbar spine was used for normalization. Results In OMs at TP1 and TP2, mean SUVpeak (±SD) was found to be 20.4 (±20.8) and 16.4 (±11.5), respectively. For the reference region, mean SUVmean was 5.6 (±1.9) and 4.9 (±2.2). Agreement between quantitative and visual assessment was only moderate, with an average Cohen’s kappa of 0.42 for planar scintigraphy and 0.62 for SPECT/CT. Discrepancies occurred in between 11 and 22 of the 52 lesions, depending on the reader and whether planar or SPECT imaging was considered. Conclusions Compared to measuring uptake in absolute units, visual evaluation of skeletal scintigraphies for change in tumor metabolism yields inconsistent results in roughly one third of the cases

Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy

Background: Currently, there is a high interest in Lu-177 targeted radionuclide therapies, which could be attributed to favorable results obtained from Lu-177 compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves. Methods: We applied a PF for de-noising the TACs of 26 patients, who underwent Lu-177-DOTATOC or -PSMA therapy. The TACs were obtained from fiilly-quantitative, serial SPECT (/CT) data, acquired at 4 h, 24 h, 48 h, 72 h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF. Results: The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys -22.3%, spleen -49.6%, tumor -60.0%), as well as for PFF (kidneys -37.1%, spleen -57.9%, tumor -70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced. Conclusion: The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise

Estimation of [177Lu]PSMA-617 tumor uptake based on voxel-wise 3D Monte Carlo tumor dosimetry in patients with metastasized castration resistant prostate cancer

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