A Decision-Support Tool for Renal Mass Classification
Published in Journal of Digital Imaging, 2018
Recommended citation: G. Kunapuli, B. A. Varghese, P. Ganapathy, B. Desai, S. Cen, M. Aron, I. Gill and V. Duddalwar. A Decision-Support Tool for Renal Mass Classification . Journal of Digital Imaging , Volume 1, Number 11 (2018). http://gkunapuli.github.io/files/18radiomicsJDI.pdf
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists.