Texture Feature Analysis To Predict Metastatic And Necrotic Soft Tissue Sarcomas

Soft Tissue Sarcomas (STS) are malignant tumors which emanate from soft tissues of the body. They are challenging for physicians because of the infrequency of their occurrence and non-predictable outcomes. In this paper, we propose a novel framework to classify STS which focuses on radiologically defined sub-regions, so-called 'habitats'. The distinctive habitats are regions where tumor evolution may be observed. We assess T1 post-and pre-contrast gadolinium and T2 non-contrast Magnetic Resonance Images (MRIs) of 36 patients prior to treatment. This paper considers spatially distinct habitats, which may be helpful in clinical treatment, especially chemotherapy and radiation. Our approach contains three main steps: (1) intra-tumor segmentation into habitats based on pixel intensity; (2) texture analysis within each distinctive habitat to capture heterogeneity; and (3) prediction of metastatic and necrotic tumor. The experimental results show the individual cases were correctly classified as metastatic or non-metastatic disease with 86.11% accuracy based on 5 features and for necrosis >= 90% or necrosis < 90% with 81.81% accuracy based on 4 features by using several meta-classifiers.