Building A Reduced Dictionary Of Relevant Perfusion Patterns From Ceus Data For The Classification Of Testis Lesions

Radical orchifunicolectomy has traditionally been the main clinical treatment for small testicular masses (STMs); however STMs represent a constantly increasing and often incidental finding. Since many of them result benign, a more conservative testis-sparing surgery was proposed, but it requires a preliminary differentiation between benign and malignant masses: this however remains challenging. Although common understanding in radiology and oncology is that perfusion patterns might provide a useful information about the type of masses, no guidelines or consensus is available for the differentiation of STMs. We propose to build a dictionary of relevant perfusion patterns, extracted using non-negative matrix factorization on pixel-wise time-intensity curves from contrast-enhanced ultrasound data. When data from a lesion are reconstructed using this dictionary, a vector containing the frequency of utilization of each pattern can be used as a tissue signature. Using this signature, a support vector machine classifier has been trained, and the cross validated accuracy reached 100% in our pilot cohort.