Multi-modality classification between myxofibrosarcoma and myxoma using radiomics and machine learning models

Myxofibrosarcoma is a rare, malignant myxoid soft tissue tumor. It can be challenging to distinguish it from a benign myxoma in clinical practice as there exists imaging and histologic feature overlap between these two entities. Some previous works used radiomics features of T1-weighted images to differentiate myxoid tumors, but few have used multimodality data. In this project, we collect a dataset containing 20 myxomas and 20 myxofibrosarcomas, each with a T1-weighted image, a T2-weighted image, and clinical features. Radiomics features from multi-modality images and clinical features are used to train multiple machine learning models. Our experiment results show that the prediction accuracy using the multi-modality features surpasses the results from a single modality. The radiomics features Gray Level Variance, Gray Level Non-uniformity Normalized extracted from the Gray Level Run Length Matrix (GLRLM) of the T2 images, and age are the top three features selected by the least absolute shrinkage and selection operator (LASSO) feature reduction model.