Prä- und posttherapeutische Larynxbildgebung

Zusammenfassung: Sowohl CT als auch MRT und neuerdings die PET-CT sind unentbehrliche Zusatzuntersuchungen zur Diagnostik und Stadieneinteilung von Tumoren des Larynx. Sie sind der klinischen Untersuchung (einschließlich endoskopischer Biopsie) beigeordnet und ergänzen diese komplementär. Eine sehr genaue Kenntnis der submukösen Tumorausbreitungswege, der diagnostischen Zeichen der Tumorinfiltration und deren Konsequenzen für Stadieneinteilung und Therapie sind unentbehrlich für die Interpretation von CT-, MRT- und PET-CT-Bildern. Sowohl CT als auch MRT sind hochsensitive Untersuchungen zum Nachweis der neoplastischen Infiltration des präepi- und paraglottischen Raums, der Subglottis und des Knorpels. Die Spezifität ist jedoch mit beiden Methoden weniger hoch als zunächst erwartet, wodurch eine Tendenz zum Überschätzen der Tumorausbreitung resultiert. Neuere Untersuchungen haben jedoch gezeigt, dass die Spezifität der MRT mittels Anwendung neuer diagnostischer Kriterien signifikant verbessert werden kann, da eine Unterscheidung zwischen Tumor und peritumoraler Entzündung in vielen Fällen möglich ist. Der sehr hohe negative Vorhersagewert der beiden Schnittbildverfahren ist aus klinischer Sicht wichtig, da er es ermöglicht, die neoplastische Knorpelinfiltration auszuschließen. Beide Methoden verbessern signifikant die prätherapeutische Stagingtreffsicherheit, wenn sie zusätzlich zur Endoskopie eingesetzt werden. Bei submukösen Tumoren liefern sowohl CT als auch MRT wertvolle Hinweise auf eine mögliche Ätiologie, auf das Ausmaß des submukösen Wachstums und die geeignete Biopsiestelle. Sie spielen auch eine wichtige Rolle bei der Diagnose von Laryngozelen, der Abklärung von N.-laryngeus-recurrens-Paresen und Larynxfrakture

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy

Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation

In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented

Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented

Node-negative T1-T2 anal cancer: radiotherapy alone or concomitant chemoradiotherapy?

PURPOSE: To evaluate the influence of concomitant chemotherapy on loco-regional control (LRC) and cancer-specific survival (CSS) in patients with T1-T2 N0 M0 anal cancer treated conservatively by primary radiotherapy (RT). MATERIALS AND METHODS: Between 1976 and 2008, 146 patients with T1 (n=29) or T2 (n=117) N0 M0 anal cancer were treated curatively by RT alone (n=71) or by combined chemoradiotherapy (CRT) (n=75) consisting of mitomycin C±5-fluorouracil. Univariate and multivariate analyses were performed to assess patient-, tumor- and treatment-related factors influencing LRC and CSS. RESULTS: With a median follow-up of 62.5 months (interquartilerange, 26-113 months), 122 (84%) patients were locally controlled. The five-year actuarial LRC, CSS and overall survival for the population were 81.4%±3.6%, 91.9%±2.6%, and 75.4%±3.9%, respectively. The five-year LRC and CSS for patients treated with RT alone and with CRT were 75.5%±6.0% vs. 86.8%±4.1% (p=0.155) and 88.5%±4.5% vs. 94.9%±2.9% (p=0.161), respectively. In the multivariate analysis, no clinical or therapeutic factors were found to significantly influence the LRC and CSS, while the addition of chemotherapy was of borderline significance (p=0.065 and p=0.107, respectively). CONCLUSIONS: In the management of node negative T1-T2 anal cancer, LRC and CSS tend to be superior in patients treated by combined CRT, even though the difference was not significant. Randomized studies are warranted to assess definitively the role of combined treatment in early-stage anal carcinoma

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the headand neck lymph node regions using a new active contourbased atlas registration model. We propose to segment thelymph node regions without directly including them in theatlas registration process; instead, they are segmentedusing the dense deformation field computed from theregistration of the atlas structures with distinctboundaries. This approach results in robust and accuratesegmentation of the lymph node regions even in thepresence of significant anatomical variations between theatlas-image and the patient's image to be segmented. Wealso present a quantitative evaluation of lymph noderegions segmentation using various statistical as well asgeometrical metrics: sensitivity, specificity, dicesimilarity coefficient and Hausdorff distance. Acomparison of the proposed method with two other state ofthe art methods is presented. The robustness of theproposed method to the atlas selection, in segmenting thelymph node regions, is also evaluated

Combined radioimmunotherapy and radiotherapy of liver metastases from colorectal cancer: a feasibility study.

BACKGROUND: A combination of radioimmunotherapy (RIT) and radiotherapy (RT) should allow one to increase the dose of radiation targeting a particular tumour without the concomitant increase of toxic side effects. This might be obtained if the dose limiting side effect of each individual radiation therapy concerned different organs. METHODS: Six patients with limited liver metastatic disease from colorectal cancer were treated with 6.9 GBq (range 4.7 to 8.4 GBq) 131I-labelled anti-CEA MAb F(ab')2 fragments combined with 20 Gy RT to the liver. Both treatments were given in close association, according to timing schedules evaluated in animals that gave the best results. RESULTS: Reversible bone marrow and liver toxicity was observed in 6 and 5 patients, respectively. Three patients who first received 20 Gy RT to the liver, showed a significant platelet drop upon completion of RT. Repeat computerized tomography (CT) after 2 months showed a minor response in 1 patient and stable disease in 3 patients. CONCLUSION: The study shows potential ways of combining RIT and RT, suggesting that this combination is feasible for the treatment of liver metastases