Angiomyolipoma is a hamartomatous condition which can occur as a component of the tuberous sclerosis complex. Lymphangiomyomatosis, another hamartomatous lesion occurring predominantly in the lungs, has long been suspected to be related to angiomyolipoma and tuberous sclerosis because of occasional clinical associations. We undertook this study to provide further support for the close relationship between these two entities. Five cases of lymphangiomyomatosis and 20 case of angiomyolipoma were retrieved for histological review and immunohistochemical studies. The antibodies used were anti-muscle specific actin (HHF-35), anti-desmin (D33) and anti-melanoma (HMB-45). Lesions featuring smooth muscle proliferation were used as controls. The proliferated smooth muscle cells in both lymphangiomyomatosis and angiomyolipoma were much plumper and paler or even clear, when compared with the deeply eosinophilic cytoplasm of the normal spindly smooth muscle cells and those of leiomyomas. Their nuclei were round to oval and pale rather than elongated and dark. Cells with bizarre nuclei were commoner in angiomyolipoma (18/20 cases) than lymphangiomyomatosis (1/5). In 12 cases of angiomyolipoma there were foci indistinguishable from lymphangiomyomatosis, i.e. plump spindle cells arranged in short fascicles around ramifying endothelium-lined spaces. All five cases of lymphangiomyomatosis stained for muscle-specific actin, desmin and HMB-45. For angiomyolipomas, the positivity rates for these markers were: 20/20, 17/20 and 18/20, respectively, including one case that was negative for both desmin and HMB-45. The various smooth muscle proliferations and tumours selected as controls were uniformly HMB-45 negative. The distinctive cytological features, morphological overlap and immunophenotypic profile all support a close relationship between lymphangiomyomatosis and angiomyolipoma, which probably represent different morphological manifestations of hamartomatous proliferation of a peculiar form of HMB-45-positive smooth muscle.

IntroductionComputer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However—due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice.Material and methodsIn this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance.ResultsOverall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups.DiscussionComplete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

No abstract

No abstract

No abstract