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ABSTRACT Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment. [ABSTRACT FROM AUTHOR]

ABSTRACT Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index = 95.59%, Jaccard Index = 91.86%, Sensitivity = 97.39%, Specificity = 94.30%, Mean Absolute Distance = 0.246[pixels], Maximum Distance = 1.050[pixels], and Hausdorff Distance = 1.365[pixels]. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 213-225, 2015 [ABSTRACT FROM AUTHOR]

The pathologist examines biopsy material obtained at surgery or preoperatively by core biopsy to make a definitive diagnosis of malignancy and to determine the type and grade of the tumour. Additional prognostic information is provided to the clinician, and this includes the true tumour size and the relative contribution of thein situand invasive components. Other important factors include multifocality, vascular invasion and the presence and extent of axillary nodal metastases. For optimal use of this important information, close cooperation between the members of the multidisciplinary team is essential. [ABSTRACT FROM AUTHOR]

Breast cancer usually develops after a series of epithelial changes in the terminal ductolobular unit. There are multiple benign causes of breast lumps, the majority of which are not associated with an increased risk of breast cancer. Histological changes of pre-malignancy such as atypical hyperplasia andin situcarcinoma can be identified, and these are indications for either close surveillance or further surgery. At the time of diagnosis, breast cancers can be staged both clinically and pathologically, and this facilitates international comparisons of results of treatment. [ABSTRACT FROM AUTHOR]