A Decision-Support Tool for Renal Mass Classification.

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 mu...

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Detalles Bibliográficos
Publicado en:	Journal of Digital Imaging Vol. 31; no. 6; pp. 929 - 940
Autores principales:	Kunapuli, Gautam, Varghese, Bino A., Ganapathy, Priya, Desai, Bhushan, Cen, Steven, Aron, Manju, Gill, Inderbir, Duddalwar, Vinay
Formato:	algorithm equations & formulas research tables/charts Journal Article
Publicado:	Springer Nature Dec2018
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	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.