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Computer-Aided Diagnosis of Breast Angiosarcoma: Results in 14 cases

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Abstract

Secondary breast angiosarcoma is a rare and highly aggressive malignant cancer, occurring most commonly on the skin surface and could be a result of radiation exposure during primary breast conserving therapy (BCT). Detecting the extent of the lesion is imperative to ensure complete tumor resection and reducing the probability of loco- regional tumor recurrence. Dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive imaging modality to detect the lesion. However, current quantitative methods of detecting the lesion are sensitive to the imaging parameters, timing of bolus injection, contrast-to- noise ratio, temporal sampling rate etc. We present a novel quantitative method to diagnose and stage angiosarcomas on DCE-MRI. The result of the algorithm has been compared to the radiologist's segmentation and the output of a commercial software, CADstream for 14 cases. Compared to the radiologist's segmentation, the algorithm detected the sarcoma with 96.1% sensitivity and 89.2% accuracy with a mean overlap, measured in terms of the Dice Similarity Coefficient (DSC), of 0.63. In 15.4% (2/13) of the cases, CADstream failed to detect the lesion. For the remaining cases, the algorithm detected the tumor with 91.7% sensitivity and accuracy and a DSC of 0.59, considering the CADstream output as the gold standard. The LDS based algorithm shows potential for automatically segmenting the lesion on DCE-MRI and to further help in planning surgical resection.

© 2013 Optical Society of America

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